4
DDBA 8307 Week 7 Assignment Template
John Doe
DDBA 8307-6
Dr. Jane Doe
1
Two-Way Contingency Table Analysis
Type text here. You will describe and defend using the two-way contingency table analysis. Use at least two outside resources—that is, resources not provided in the course resources, readings, etc. These citations will be presented in the References section. This exercise will give you practice for addressing Rubric Item 2.13b, which states, “Describes and defends, in detail, the statistical analyses that the student will conduct….” This section should be no more than two paragraphs.
Research Question
Type appropriate research question here?
Hypotheses
H0: Type appropriate null hypothesis here.
H1: Type appropriate alternative hypothesis here.
Results
Type introduction here.
Descriptive Statistics
Present the descriptive statistics here—use appropriate table and figures.
Inferential Results
Type the inferential results here.
2
References
Type references here in proper APA format.
Appendix – Two-Way Contingency Table Analysis
SPSS Output
BUS 308 Week 2 Lecture 2
Statistical Testing for Differences – Part 1
After reading this lecture, the student should know:
1. How statistical distributions are used in hypothesis testing.
2. How to interpret the F test (both options) produced by Excel
3. How to interpret the T-test produced by Excel
Overview
Lecture 1 introduced the logic of statistical testing using the hypothesis testing procedure.
It also mentioned that we will be looking at two different tests this week. The t-test is used to
determine if means differ, from either a standard or claim or from another group. The F-test is
used to examine variance differences between groups.
This lecture starts by looking at statistical distributions – they underline the entire
statistical testing approach. They are kind of like the detective’s base belief that crimes are
committed for only a couple of reasons – money, vengeance, or love. The statistical distribution
that underlies each test assumes that statistical measures (such as the F value when comparing
variances and the t value when looking at means) follow a particular pattern, and this can be used
to make decisions.
While the underlying distributions differ for the different tests we will be looking at
throughout the course, they all have some basic similarities that allow us to examine the t
distribution and extrapolate from it to interpreting results based on other distributions.
Distributions. The basic logic for all statistical tests: If the null hypothesis claim is
correct, then the distribution of the statistical outcome will be distributed around a central value,
and larger and smaller values will be increasingly rare. At some point (and we define this as our
alpha value), we can say that the likelihood of getting a difference this large is extremely
unlikely and we will say that our results do.
BUS 308 Week 2 Lecture 2 Statistical Testing for Differenc.docxjasoninnes20
BUS 308 Week 2 Lecture 2
Statistical Testing for Differences – Part 1
After reading this lecture, the student should know:
1. How statistical distributions are used in hypothesis testing.
2. How to interpret the F test (both options) produced by Excel
3. How to interpret the T-test produced by Excel
Overview
Lecture 1 introduced the logic of statistical testing using the hypothesis testing procedure.
It also mentioned that we will be looking at two different tests this week. The t-test is used to
determine if means differ, from either a standard or claim or from another group. The F-test is
used to examine variance differences between groups.
This lecture starts by looking at statistical distributions – they underline the entire
statistical testing approach. They are kind of like the detective’s base belief that crimes are
committed for only a couple of reasons – money, vengeance, or love. The statistical distribution
that underlies each test assumes that statistical measures (such as the F value when comparing
variances and the t value when looking at means) follow a particular pattern, and this can be used
to make decisions.
While the underlying distributions differ for the different tests we will be looking at
throughout the course, they all have some basic similarities that allow us to examine the t
distribution and extrapolate from it to interpreting results based on other distributions.
Distributions. The basic logic for all statistical tests: If the null hypothesis claim is
correct, then the distribution of the statistical outcome will be distributed around a central value,
and larger and smaller values will be increasingly rare. At some point (and we define this as our
alpha value), we can say that the likelihood of getting a difference this large is extremely
unlikely and we will say that our results do not seem to come from a population that matches the
claims of the null hypothesis.
Note that this logic has several key elements:
1. The test is based on an assumption that the null hypothesis is correct. This gives us a
starting point, even if later proven wrong.
2. All sample results are turned into a statistic that matches the test selected (for
example, the F statistic when using the F-test, or the t-statistic when using the T-test.)
3. The calculated statistic is compared to a related statistical distribution to see how
likely an outcome we have.
4. The larger the test statistic, the more unlikely it is that the result matches or comes
from the population described by the null hypothesis claim.
We will demonstrate these ideas by looking at the questions being asked in this week’s
homework. We will show results of the related Excel tests, and discuss how to interpret the
output.
We need to remember that seeing different value (mean, variance, etc.) from different
samples does not tell us that the population parameters we are estimating are, in fact, different.
The ...
BUS 308 Week 3 Lecture 1 Examining Differences - Continued.docxcurwenmichaela
BUS 308 Week 3 Lecture 1
Examining Differences - Continued
Expected Outcomes
After reading this lecture, the student should be familiar with:
1. Issues around multiple testing
2. The basics of the Analysis of Variance test
3. Determining significant differences between group means
4. The basics of the Chi Square Distribution.
Overview
Last week, we found out ways to examine differences between a measure taken on two
groups (two-sample test situation) as well as comparing that measure to a standard (a one-sample
test situation). We looked at the F test which let us test for variance equality. We also looked at
the t-test which focused on testing for mean equality. We noted that the t-test had three distinct
versions, one for groups that had equal variances, one for groups that had unequal variances, and
one for data that was paired (two measures on the same subject, such as salary and midpoint for
each employee). We also looked at how the 2-sample unequal t-test could be used to use Excel
to perform a one-sample mean test against a standard or constant value. This week we expand
our tool kit to let us compare multiple groups for similar mean values.
A second tool will let us look at how data values are distributed – if graphed, would they
look the same? Different shapes or patterns often means the data sets differ in significant ways
that can help explain results.
Multiple Groups
As interesting as comparing two groups is, often it is a bit limiting as to what it tells us.
One obvious issue that we are missing in the comparisons made last week was equal work. This
idea is still somewhat hard to get a clear handle on. Typically, as we look at this issue, questions
arise about things such as performance appraisal ratings, education distribution, seniority impact,
etc.
Some of these can be tested with the tools introduced last week. We can see, for
example, if the performance rating average is the same for each gender. What we couldn’t do, at
this point however, is see if performance ratings differ by grade, do the more senior workers
perform relatively better? Is there a difference between ratings for each gender by grade level?
The same questions can be asked about seniority impact. This week will give us tools to expand
how we look at the clues hidden within the data set about equal pay for equal work.
ANOVA
So, let’s start taking a look at these questions. The first tool for this week is the Analysis
of Variance – ANOVA for short. ANOVA is often confusing for students; it says it analyzes
variance (which it does) but the purpose of an ANOVA test is to determine if the means of
different groups are the same! Now, so far, we have considered means and variance to be two
distinct characteristics of data sets; characteristics that are not related, yet here we are saying that
looking at one will give us insight into the other.
The reason is due to the way the variance is an.
Article Write-upsTo help you connect what you’re learning in cl.docxdavezstarr61655
Article Write-ups
To help you connect what you’re learning in class with current events, you are required to find an economics-related news article, read it, and write a short paper connecting your findings to topics we’ve discussed in class. You should select your article from a reputable source like The Economist, New York Times, or Wall Street Journal (read: not a blog post or obscure website). Students must read the selected piece, summarize the content, relate it to subjects covered in class, and turn in a short (1-2 pages) typed paper with these elements. Please note that this means you need to pick an article substantial enough to write more than one page about it.
Some suggested topics:
Tradeoffs or opportunity costs
Supply & Demand (including input costs, other nonprice determinants)
Substitute & complementary goods
Elasticity
Market efficiency
Behavioral economics (framing, heuristics)
Consumer choice (utility maximization, diminishing marginal utility)
Write-up Requirements:
-Minimum 1 page of text not including header or references (double spaced, 12 point font, standard margins).
-A discussion of how your selected article directly relates to or relies upon a particular economic principle we have covered in class. This should include defining and explaining the concept, in addition to discussing how it is used in your article. (I.e. “This article uses the concept of opportunity costs, which means…” or “This article discusses using sales prices to frame consumer decisions. From behavioral economics we learned that framing is….”)
-A summary of the contents that demonstrates you read the article.
-A review and general evaluation based upon what you’ve learned in class.
-A properly formatted reference for your article (citation style of your choice – APA, MLA, etc.). For online sources please provide a link.
-You must submit your assignment on Moodle as a Word document (.doc or .docx)
Week 3 Lecture 9
Effect Size
When we reject the null hypothesis with an ANOVA test, we have two questions that arise. The first, which pair of means differs significantly, we have dealt with already. The second question, similar to what we asked with the t-test null hypothesis rejection is; what caused the rejection, the sample size or the variable interactions? This question is again answered using an effect size measure.
Recall that the effect size measure shows how likely the variable interaction caused the null hypothesis rejection. Large values lead us to say the variables caused the outcome, while small values lead us to say the outcome has little to no practical significance as the sample size was the most likely cause of the rejection of the null.
With the single factor ANOVA, the effect size measure is eta squared, and equals the SS(between)/SS(total) (Tanner & Youssef-Morgan, 2013). For our salary example in Lecture 8, eta squared equals 17686.02 (SS(between) / 18066 (SS(total) = 0.979 (rounded). Eta squared effect s.
BUS308 – Week 1 Lecture 2 Describing Data Expected Out.docxcurwenmichaela
BUS308 – Week 1 Lecture 2
Describing Data
Expected Outcomes
After reading this lecture, the student should be familiar with:
1. Basic descriptive statistics for data location
2. Basic descriptive statistics for data consistency
3. Basic descriptive statistics for data position
4. Basic approaches for describing likelihood
5. Difference between descriptive and inferential statistics
What this lecture covers
This lecture focuses on describing data and how these descriptions can be used in an
analysis. It also introduces and defines some specific descriptive statistical tools and results.
Even if we never become a data detective or do statistical tests, we will be exposed and
bombarded with statistics and statistical outcomes. We need to understand what they are telling
us and how they help uncover what the data means on the “crime,” AKA research question/issue.
How we obtain these results will be covered in lecture 1-3.
Detecting
In our favorite detective shows, starting out always seems difficult. They have a crime,
but no real clues or suspects, no idea of what happened, no “theory of the crime,” etc. Much as
we are at this point with our question on equal pay for equal work.
The process followed is remarkably similar across the different shows. First, a case or
situation presents itself. The heroes start by understanding the background of the situation and
those involved. They move on to collecting clues and following hints, some of which do not pan
out to be helpful. They then start to build relationships between and among clues and facts,
tossing out ideas that seemed good but lead to dead-ends or non-helpful insights (false leads,
etc.). Finally, a conclusion is reached and the initial question of “who done it” is solved.
Data analysis, and specifically statistical analysis, is done quite the same way as we will
see.
Descriptive Statistics
Week 1 Clues
We are interested in whether or not males and females are paid the same for doing equal
work. So, how do we go about answering this question? The “victim” in this question could be
considered the difference in pay between males and females, specifically when they are doing
equal work. An initial examination (Doc, was it murder or an accident?) involves obtaining
basic information to see if we even have cause to worry.
The first action in any analysis involves collecting the data. This generally involves
conducting a random sample from the population of employees so that we have a manageable
data set to operate from. In this case, our sample, presented in Lecture 1, gave us 25 males and
25 females spread throughout the company. A quick look at the sample by HR provided us with
assurance that the group looked representative of the company workforce we are concerned with
as a whole. Now we can confidently collect clues to see if we should be concerned or not.
As with any detective, the first issue is to understand the.
3Type your name hereType your three-letter and -number cours.docxlorainedeserre
3
Type your name here
Type your three-letter and -number course code here
The date goes here
Type instructor’s name here
Your Title Goes Here
This is an electronic template for papers written in GCU style. The purpose of the template is to help you follow the basic writing expectations for beginning your coursework at GCU. Margins are set at 1 inch for top, bottom, left, and right. The first line of each paragraph is indented a half inch (0.5"). The line spacing is double throughout the paper, even on the reference page. One space after punctuation is used at the end of a sentence. The font style used in this template is Times New Roman. The font size is 12 point. When you are ready to write, and after having read these instructions completely, you can delete these directions and start typing. The formatting should stay the same. If you have any questions, please consult with your instructor.
Citations are used to reference material from another source. When paraphrasing material from another source (such as a book, journal, website), include the author’s last name and the publication year in parentheses.When directly quoting material word-for-word from another source, use quotation marks and include the page number after the author’s last name and year.
Using citations to give credit to others whose ideas or words you have used is an essential requirement to avoid issues of plagiarism. Just as you would never steal someone else’s car, you should not steal his or her words either. To avoid potential problems, always be sure to cite your sources. Cite by referring to the author’s last name, the year of publication in parentheses at the end of the sentence, such as (George & Mallery, 2016), and page numbers if you are using word-for-word materials. For example, “The developments of the World War II years firmly established the probability sample survey as a tool for describing population characteristics, beliefs, and attitudes” (Heeringa, West, & Berglund, 2017, p. 3).
The reference list should appear at the end of a paper (see the next page). It provides the information necessary for a reader to locate and retrieve any source you cite in the body of the paper. Each source you cite in the paper must appear in your reference list; likewise, each entry in the reference list must be cited in your text. A sample reference page is included below; this page includes examples (George & Mallery, 2016; Heeringa et al., 2017; Smith et al., 2018; “USA swimming,” 2018; Yu, Johnson, Deutsch, & Varga, 2018) of how to format different reference types (e.g., books, journal articles, and a website). For additional examples, see the GCU Style Guide.
References
George, D., & Mallery, P. (2016). IBM SPSS statistics 23 step by step: A simple guide and reference. New York, NY: Routledge.
Heeringa, S. G., West, B. T., & Berglund, P. A. (2017). Applied survey data analysis (2nd ed.). New York, NY: Chapman & Hall/CRC Press.
Smith, P. D., Martin, B., Chewning, B., ...
BUS308 – Week 5 Lecture 1 A Different View Expected Ou.docxcurwenmichaela
BUS308 – Week 5 Lecture 1
A Different View
Expected Outcomes
After reading this lecture, the student should be familiar with:
1. What a confidence interval for a statistic is.
2. What a confidence interval for differences is.
3. The difference between statistical and practical significance.
4. The meaning of an Effect Size measure.
Overview
Years ago, a comedy show used to introduce new skits with the phrase “and now for
something completely different.” That seems appropriate for this week’s material.
This week we will look at evaluating our data results in somewhat different ways. One of
the criticisms of the hypothesis testing procedure is that it only shows one value, when it is
reasonably clear that a number of different values would also cause us to reject or not reject a
null hypothesis of no difference. Many managers and researchers would like to see what these
values could be; and, in particular, what are the extreme values as help in making decisions.
Confidence intervals will help us here.
The other criticism of the hypothesis testing procedure is that we can “manage” the
results, or ensure that we will reject the null, by manipulating the sample size. For example, if
we have a difference in a customer preference between two products of only 1%, is this a big
deal? Given the uncertainty contained in sample results, we might tend to think that we can
safely ignore this result. However, if we were to use a sample of, say, 10,000, we would find
that this difference is statistically significant. This, for many, seems to fly in the face of
reasonableness. We will look at a measure of “practical significance,” meaning the likelihood of
the difference being worth paying any attention to, called the effect size to help us here.
Confidence Intervals
A confidence interval is a range of values that, based upon the sample results, most likely
contains the actual population parameter. The “most likely” element is the level of confidence
attached to the interval, 95% confidence interval, 90% confidence interval, 99% confidence
interval, etc. They can be created at any time, with or without performing a statistical test, such
as the t-test.
A confidence interval may be expressed as a range (45 to 51% of the town’s population
support the proposal) or as a mean or proportion with a margin of error (48% of the town
supports the proposal, with a margin of error of 3%). This last format is frequently seen with
opinion poll results, and simply means that you should add and subtract this margin of error from
the reported proportion to obtain the range. With either format, the confidence percent should
also be provided.
Confidence intervals for a single mean (or proportion) are fairly straightforward to
understand, and relate to t-test outcomes simply. Details on how to construct the interval will be
given in this week’s second lecture. We want to understand how to interpret and understa.
Business Email Rubric Subject Line Subject line clea.docxjasoninnes20
Business Email Rubric
Subject Line
Subject line
clearly states the
main point of the
email
5points
Subject line is a
bit long (5+
words) or a bit
short (1 word) but
states the point of
the email
3points
Subject line does
not correspond to
the main point of
the email
2points
Subject line is
missing
0points
Greeting
Email includes a
professional
greeting that is
appropriate for the
audience; uses
the person's first
name
5points
Email includes a
professional
greeting that is
adequate for the
audience but uses
the person's first
and last name or
just the last name
3points
Email includes a
greeting but it is
not personalized
2points
Email lacks a
greeting
0points
Introductory
Comment
Email includes an
introductory
positive, relevant
comment
5points
it Email includes
an introductory,
positive comment
but may be very
general
3points
Email includes an
introduction only
2points
Email lacks an
introductory
comment
0points
Content
Purpose of the
email is clear, as
is the outcome;
content is succinct
and well organized
and does not
include
unnecessary
information
5points
Purpose of the
email is clear, as
is the outcome;
content could be
better organized
3points
Purpose of the
email is not clear
and/or content is
poorly organized;
it took more than
one reading to
understand what
the email is about
2points
Email seems to be
a collection of
unrelated
statements; it is
difficult to figure
out what the
purpose is
0points
Closing and
Signature
Email includes a
complementary
closing and
signature with all
required items
(name,
title/position,
company name,
phone number)
5points
Email includes a
complementary
closing and
signature but the
signature is
incomplete
3points
Email may be
missing either a
complementary
closing or
signature
2points
Email lacks a
closing and
signature
0points
Writing
Conventions
Email looks
professional and
does not have any
formatting or
writing errors
5points
Email is well
presented with
minimal (<3)
formatting or
writing errors
3points
Email includes
several (3+)
formatting or
writing errors
2points
Email is poorly
presented and has
an accumulation
of writing errors
that interfere with
readability
0points
BUS308 Week 3 Lecture 2
Examining Differences – ANOVA and Chi Square
Expected Outcomes
After reading this lecture, the student should be familiar with:
1. Conducting hypothesis tests with the ANVOA and Chi Square tests
2. How to interpret the Analysis of Variance test output
3. How to interpret Determining significant differences between group means
4. The basics of the Chi Square Distribution.
Overview
This week we introduced the ANOVA test for multiple mean equality and the Chi Square
tests for distrib ...
This document provides instructions for a problem set analyzing employee salary data. It instructs the user to copy salary and employee data into their assignment file. It then provides guidance on using descriptive statistics and t-tests to analyze differences in compensation ratios between male and female employees. The goal is to determine if there is statistical evidence that male and female employees are paid equally for equal work.
BUS 308 Week 2 Lecture 2 Statistical Testing for Differenc.docxjasoninnes20
BUS 308 Week 2 Lecture 2
Statistical Testing for Differences – Part 1
After reading this lecture, the student should know:
1. How statistical distributions are used in hypothesis testing.
2. How to interpret the F test (both options) produced by Excel
3. How to interpret the T-test produced by Excel
Overview
Lecture 1 introduced the logic of statistical testing using the hypothesis testing procedure.
It also mentioned that we will be looking at two different tests this week. The t-test is used to
determine if means differ, from either a standard or claim or from another group. The F-test is
used to examine variance differences between groups.
This lecture starts by looking at statistical distributions – they underline the entire
statistical testing approach. They are kind of like the detective’s base belief that crimes are
committed for only a couple of reasons – money, vengeance, or love. The statistical distribution
that underlies each test assumes that statistical measures (such as the F value when comparing
variances and the t value when looking at means) follow a particular pattern, and this can be used
to make decisions.
While the underlying distributions differ for the different tests we will be looking at
throughout the course, they all have some basic similarities that allow us to examine the t
distribution and extrapolate from it to interpreting results based on other distributions.
Distributions. The basic logic for all statistical tests: If the null hypothesis claim is
correct, then the distribution of the statistical outcome will be distributed around a central value,
and larger and smaller values will be increasingly rare. At some point (and we define this as our
alpha value), we can say that the likelihood of getting a difference this large is extremely
unlikely and we will say that our results do not seem to come from a population that matches the
claims of the null hypothesis.
Note that this logic has several key elements:
1. The test is based on an assumption that the null hypothesis is correct. This gives us a
starting point, even if later proven wrong.
2. All sample results are turned into a statistic that matches the test selected (for
example, the F statistic when using the F-test, or the t-statistic when using the T-test.)
3. The calculated statistic is compared to a related statistical distribution to see how
likely an outcome we have.
4. The larger the test statistic, the more unlikely it is that the result matches or comes
from the population described by the null hypothesis claim.
We will demonstrate these ideas by looking at the questions being asked in this week’s
homework. We will show results of the related Excel tests, and discuss how to interpret the
output.
We need to remember that seeing different value (mean, variance, etc.) from different
samples does not tell us that the population parameters we are estimating are, in fact, different.
The ...
BUS 308 Week 3 Lecture 1 Examining Differences - Continued.docxcurwenmichaela
BUS 308 Week 3 Lecture 1
Examining Differences - Continued
Expected Outcomes
After reading this lecture, the student should be familiar with:
1. Issues around multiple testing
2. The basics of the Analysis of Variance test
3. Determining significant differences between group means
4. The basics of the Chi Square Distribution.
Overview
Last week, we found out ways to examine differences between a measure taken on two
groups (two-sample test situation) as well as comparing that measure to a standard (a one-sample
test situation). We looked at the F test which let us test for variance equality. We also looked at
the t-test which focused on testing for mean equality. We noted that the t-test had three distinct
versions, one for groups that had equal variances, one for groups that had unequal variances, and
one for data that was paired (two measures on the same subject, such as salary and midpoint for
each employee). We also looked at how the 2-sample unequal t-test could be used to use Excel
to perform a one-sample mean test against a standard or constant value. This week we expand
our tool kit to let us compare multiple groups for similar mean values.
A second tool will let us look at how data values are distributed – if graphed, would they
look the same? Different shapes or patterns often means the data sets differ in significant ways
that can help explain results.
Multiple Groups
As interesting as comparing two groups is, often it is a bit limiting as to what it tells us.
One obvious issue that we are missing in the comparisons made last week was equal work. This
idea is still somewhat hard to get a clear handle on. Typically, as we look at this issue, questions
arise about things such as performance appraisal ratings, education distribution, seniority impact,
etc.
Some of these can be tested with the tools introduced last week. We can see, for
example, if the performance rating average is the same for each gender. What we couldn’t do, at
this point however, is see if performance ratings differ by grade, do the more senior workers
perform relatively better? Is there a difference between ratings for each gender by grade level?
The same questions can be asked about seniority impact. This week will give us tools to expand
how we look at the clues hidden within the data set about equal pay for equal work.
ANOVA
So, let’s start taking a look at these questions. The first tool for this week is the Analysis
of Variance – ANOVA for short. ANOVA is often confusing for students; it says it analyzes
variance (which it does) but the purpose of an ANOVA test is to determine if the means of
different groups are the same! Now, so far, we have considered means and variance to be two
distinct characteristics of data sets; characteristics that are not related, yet here we are saying that
looking at one will give us insight into the other.
The reason is due to the way the variance is an.
Article Write-upsTo help you connect what you’re learning in cl.docxdavezstarr61655
Article Write-ups
To help you connect what you’re learning in class with current events, you are required to find an economics-related news article, read it, and write a short paper connecting your findings to topics we’ve discussed in class. You should select your article from a reputable source like The Economist, New York Times, or Wall Street Journal (read: not a blog post or obscure website). Students must read the selected piece, summarize the content, relate it to subjects covered in class, and turn in a short (1-2 pages) typed paper with these elements. Please note that this means you need to pick an article substantial enough to write more than one page about it.
Some suggested topics:
Tradeoffs or opportunity costs
Supply & Demand (including input costs, other nonprice determinants)
Substitute & complementary goods
Elasticity
Market efficiency
Behavioral economics (framing, heuristics)
Consumer choice (utility maximization, diminishing marginal utility)
Write-up Requirements:
-Minimum 1 page of text not including header or references (double spaced, 12 point font, standard margins).
-A discussion of how your selected article directly relates to or relies upon a particular economic principle we have covered in class. This should include defining and explaining the concept, in addition to discussing how it is used in your article. (I.e. “This article uses the concept of opportunity costs, which means…” or “This article discusses using sales prices to frame consumer decisions. From behavioral economics we learned that framing is….”)
-A summary of the contents that demonstrates you read the article.
-A review and general evaluation based upon what you’ve learned in class.
-A properly formatted reference for your article (citation style of your choice – APA, MLA, etc.). For online sources please provide a link.
-You must submit your assignment on Moodle as a Word document (.doc or .docx)
Week 3 Lecture 9
Effect Size
When we reject the null hypothesis with an ANOVA test, we have two questions that arise. The first, which pair of means differs significantly, we have dealt with already. The second question, similar to what we asked with the t-test null hypothesis rejection is; what caused the rejection, the sample size or the variable interactions? This question is again answered using an effect size measure.
Recall that the effect size measure shows how likely the variable interaction caused the null hypothesis rejection. Large values lead us to say the variables caused the outcome, while small values lead us to say the outcome has little to no practical significance as the sample size was the most likely cause of the rejection of the null.
With the single factor ANOVA, the effect size measure is eta squared, and equals the SS(between)/SS(total) (Tanner & Youssef-Morgan, 2013). For our salary example in Lecture 8, eta squared equals 17686.02 (SS(between) / 18066 (SS(total) = 0.979 (rounded). Eta squared effect s.
BUS308 – Week 1 Lecture 2 Describing Data Expected Out.docxcurwenmichaela
BUS308 – Week 1 Lecture 2
Describing Data
Expected Outcomes
After reading this lecture, the student should be familiar with:
1. Basic descriptive statistics for data location
2. Basic descriptive statistics for data consistency
3. Basic descriptive statistics for data position
4. Basic approaches for describing likelihood
5. Difference between descriptive and inferential statistics
What this lecture covers
This lecture focuses on describing data and how these descriptions can be used in an
analysis. It also introduces and defines some specific descriptive statistical tools and results.
Even if we never become a data detective or do statistical tests, we will be exposed and
bombarded with statistics and statistical outcomes. We need to understand what they are telling
us and how they help uncover what the data means on the “crime,” AKA research question/issue.
How we obtain these results will be covered in lecture 1-3.
Detecting
In our favorite detective shows, starting out always seems difficult. They have a crime,
but no real clues or suspects, no idea of what happened, no “theory of the crime,” etc. Much as
we are at this point with our question on equal pay for equal work.
The process followed is remarkably similar across the different shows. First, a case or
situation presents itself. The heroes start by understanding the background of the situation and
those involved. They move on to collecting clues and following hints, some of which do not pan
out to be helpful. They then start to build relationships between and among clues and facts,
tossing out ideas that seemed good but lead to dead-ends or non-helpful insights (false leads,
etc.). Finally, a conclusion is reached and the initial question of “who done it” is solved.
Data analysis, and specifically statistical analysis, is done quite the same way as we will
see.
Descriptive Statistics
Week 1 Clues
We are interested in whether or not males and females are paid the same for doing equal
work. So, how do we go about answering this question? The “victim” in this question could be
considered the difference in pay between males and females, specifically when they are doing
equal work. An initial examination (Doc, was it murder or an accident?) involves obtaining
basic information to see if we even have cause to worry.
The first action in any analysis involves collecting the data. This generally involves
conducting a random sample from the population of employees so that we have a manageable
data set to operate from. In this case, our sample, presented in Lecture 1, gave us 25 males and
25 females spread throughout the company. A quick look at the sample by HR provided us with
assurance that the group looked representative of the company workforce we are concerned with
as a whole. Now we can confidently collect clues to see if we should be concerned or not.
As with any detective, the first issue is to understand the.
3Type your name hereType your three-letter and -number cours.docxlorainedeserre
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References
George, D., & Mallery, P. (2016). IBM SPSS statistics 23 step by step: A simple guide and reference. New York, NY: Routledge.
Heeringa, S. G., West, B. T., & Berglund, P. A. (2017). Applied survey data analysis (2nd ed.). New York, NY: Chapman & Hall/CRC Press.
Smith, P. D., Martin, B., Chewning, B., ...
BUS308 – Week 5 Lecture 1 A Different View Expected Ou.docxcurwenmichaela
BUS308 – Week 5 Lecture 1
A Different View
Expected Outcomes
After reading this lecture, the student should be familiar with:
1. What a confidence interval for a statistic is.
2. What a confidence interval for differences is.
3. The difference between statistical and practical significance.
4. The meaning of an Effect Size measure.
Overview
Years ago, a comedy show used to introduce new skits with the phrase “and now for
something completely different.” That seems appropriate for this week’s material.
This week we will look at evaluating our data results in somewhat different ways. One of
the criticisms of the hypothesis testing procedure is that it only shows one value, when it is
reasonably clear that a number of different values would also cause us to reject or not reject a
null hypothesis of no difference. Many managers and researchers would like to see what these
values could be; and, in particular, what are the extreme values as help in making decisions.
Confidence intervals will help us here.
The other criticism of the hypothesis testing procedure is that we can “manage” the
results, or ensure that we will reject the null, by manipulating the sample size. For example, if
we have a difference in a customer preference between two products of only 1%, is this a big
deal? Given the uncertainty contained in sample results, we might tend to think that we can
safely ignore this result. However, if we were to use a sample of, say, 10,000, we would find
that this difference is statistically significant. This, for many, seems to fly in the face of
reasonableness. We will look at a measure of “practical significance,” meaning the likelihood of
the difference being worth paying any attention to, called the effect size to help us here.
Confidence Intervals
A confidence interval is a range of values that, based upon the sample results, most likely
contains the actual population parameter. The “most likely” element is the level of confidence
attached to the interval, 95% confidence interval, 90% confidence interval, 99% confidence
interval, etc. They can be created at any time, with or without performing a statistical test, such
as the t-test.
A confidence interval may be expressed as a range (45 to 51% of the town’s population
support the proposal) or as a mean or proportion with a margin of error (48% of the town
supports the proposal, with a margin of error of 3%). This last format is frequently seen with
opinion poll results, and simply means that you should add and subtract this margin of error from
the reported proportion to obtain the range. With either format, the confidence percent should
also be provided.
Confidence intervals for a single mean (or proportion) are fairly straightforward to
understand, and relate to t-test outcomes simply. Details on how to construct the interval will be
given in this week’s second lecture. We want to understand how to interpret and understa.
Business Email Rubric Subject Line Subject line clea.docxjasoninnes20
Business Email Rubric
Subject Line
Subject line
clearly states the
main point of the
email
5points
Subject line is a
bit long (5+
words) or a bit
short (1 word) but
states the point of
the email
3points
Subject line does
not correspond to
the main point of
the email
2points
Subject line is
missing
0points
Greeting
Email includes a
professional
greeting that is
appropriate for the
audience; uses
the person's first
name
5points
Email includes a
professional
greeting that is
adequate for the
audience but uses
the person's first
and last name or
just the last name
3points
Email includes a
greeting but it is
not personalized
2points
Email lacks a
greeting
0points
Introductory
Comment
Email includes an
introductory
positive, relevant
comment
5points
it Email includes
an introductory,
positive comment
but may be very
general
3points
Email includes an
introduction only
2points
Email lacks an
introductory
comment
0points
Content
Purpose of the
email is clear, as
is the outcome;
content is succinct
and well organized
and does not
include
unnecessary
information
5points
Purpose of the
email is clear, as
is the outcome;
content could be
better organized
3points
Purpose of the
email is not clear
and/or content is
poorly organized;
it took more than
one reading to
understand what
the email is about
2points
Email seems to be
a collection of
unrelated
statements; it is
difficult to figure
out what the
purpose is
0points
Closing and
Signature
Email includes a
complementary
closing and
signature with all
required items
(name,
title/position,
company name,
phone number)
5points
Email includes a
complementary
closing and
signature but the
signature is
incomplete
3points
Email may be
missing either a
complementary
closing or
signature
2points
Email lacks a
closing and
signature
0points
Writing
Conventions
Email looks
professional and
does not have any
formatting or
writing errors
5points
Email is well
presented with
minimal (<3)
formatting or
writing errors
3points
Email includes
several (3+)
formatting or
writing errors
2points
Email is poorly
presented and has
an accumulation
of writing errors
that interfere with
readability
0points
BUS308 Week 3 Lecture 2
Examining Differences – ANOVA and Chi Square
Expected Outcomes
After reading this lecture, the student should be familiar with:
1. Conducting hypothesis tests with the ANVOA and Chi Square tests
2. How to interpret the Analysis of Variance test output
3. How to interpret Determining significant differences between group means
4. The basics of the Chi Square Distribution.
Overview
This week we introduced the ANOVA test for multiple mean equality and the Chi Square
tests for distrib ...
This document provides instructions for a problem set analyzing employee salary data. It instructs the user to copy salary and employee data into their assignment file. It then provides guidance on using descriptive statistics and t-tests to analyze differences in compensation ratios between male and female employees. The goal is to determine if there is statistical evidence that male and female employees are paid equally for equal work.
Week 5 Lecture 14 The Chi Square Test Quite often, pat.docxcockekeshia
Week 5 Lecture 14
The Chi Square Test
Quite often, patterns of responses or measures give us a lot of information. Patterns are
generally the result of counting how many things fit into a particular category. Whenever we
make a histogram, bar, or pie chart we are looking at the pattern of the data. Frequently, changes
in these visual patterns will be our first clues that things have changed, and the first clue that we
need to initiate a research study (Lind, Marchel, & Wathen, 2008).
One of the most useful test in examining patterns and relationships in data involving
counts (how many fit into this category, how many into that, etc.) is the chi-square. It is
extremely easy to calculate and has many more uses than we will cover. Examining patterns
involves two uses of the Chi-square - the goodness of fit and the contingency table. Both of
these uses have a common trait: they involve counts per group. In fact, the chi-square is the only
statistic we will look at that we use when we have counts per multiple groups (Tanner &
Youssef-Morgan, 2013).
Chi Square Goodness of Fit Test
The goodness of fit test checks to see if the data distribution (counts per group) matches
some pattern we are interested in. Example: Are the employees in our example company
distributed equal across the grades? Or, a more reasonable expectation for a company might be
are the employees distributed in a pyramid fashion – most on the bottom and few at the top?
The Chi Square test compares the actual versus a proposed distribution of counts by
generating a measure for each cell or count: (actual – expected)2/actual. Summing these for all
of the cells or groups provides us with the Chi Square Statistic. As with our other tests, we
determine the p-value of getting a result as large or larger to determine if we reject or not reject
our null hypothesis. An example will show the approach using Excel.
Regardless of the Chi Square test, the chi square related functions are found in the fx
Statistics window rather than the Data Analysis where we found the t and ANOVA test
functions. The most important for us are:
• CHISQ.TEST (actual range, expected range) – returns the p-value for the test
• CHISQ.INV.RT(p-value, df) – returns the actual Chi Square value for the p-value
or probability value used.
• CHISQ.DIST.RT(X, df) – returns the p-value for a given value.
When we have a table of actual and expected results, using the =CHISQ.TEST(actual
range, expected range) will provide us with the p-value of the calculated chi square value (but
does not give us the actual calculated chi square value for the test). We can compare this value
against our alpha criteria (generally 0.05) to make our decision about rejecting or not rejecting
the null hypothesis.
If, after finding the p-value for our chi square test, we want to determine the calculated
value of the chi square statistic, we can use the =CHISQ.INV.RT(probability, df).
Chapter 18 – Pricing Setting in the Business WorldThere are few .docxrobert345678
Chapter 18 – Pricing Setting in the Business World
There are few Methods for setting pricing – costs methods vs demand methods
Formulas considering costs and mark up will help you to do the Problem set assignment:
1. Markup for setting prices (Mark up $ = SP-CP); MARK UP % = (MU $/SP) X100)
Formula for setting price with the markup method
SP = Cost/(1- Markup %)
Example - retailer buys A hat for $15 and wants a 40% markup, his selling price would be….
SP = 15/(1-.40)=.60
= $25.00
2. Understand Role of different costs – fixed, variable, total costs and average costs
3. What is the breakeven point? Formula for calculating the Break Even point.
BE = Total Fixed Cost/Fixed cost contribution
Fixed Cost Contribution=Price – variable cost
4. Average Cost = when there are many flavors/types of the same product, producer determines average cost and then add the mark up to set a common selling price.
.ANOVA
Analysis of Variance is a method of testing the equality of three or more population
means by analyzing sample variance.
One-Way ANOVA
The one-way ANOVA is used to compare three or more population means when there is
one factor of interest.
Requirements
The populations have distributions that are approximately normal.
The populations have the same variance.
The samples are simple random samples of quantitative data.
The samples are independent of each other.
The different samples are from populations that are categorized in only one.
way
One-Way ANOVA is a hypothesis test. There are seven steps for a hypothesis test.
Example
A professor at a local University believes there is a relationship between head size and
the major of the students in her biostatistics classes. She takes a random sample of 20
students from each of three classes and records their major and head circumference.
The data are shown in the following table.
Step 1: State the null hypothesis.
Mean 1 equals mean 2 equals mean 3 equals mean 4.
Step 2: State the Alternative hypothesis.
At least one mean is different.
Step 3: State the Level of Significance.
The level of significance is 0.05.
Step 4: State the test statistic.
variance between samples
variance within samples
F
The test statistic follows the F distribution which has two degrees of freedom, one for
the numerator and one for the denominator.
The calculations for the test statistic are complicated, so a software program is
generally used for the calculations. We will be using Microsoft Excel for this example.
Step 5: Calculate
The calculations are done in Microsoft Excel using the data analysis toolpak. Enter the
data into the spread sheet as shown here. Click on data and the data analysis tookpak
button is on the right.
When you click on the button a dialogue box appears.
Choose ANOVA One Factor. Then another dialogue box appears.
Input range is where the data is in the table. Be sure to put a check in the box for labels
in.
Running head Organization behaviorOrganization behavior 2.docxtoltonkendal
Running head: Organization behavior
Organization behavior 2
Organization behavior
Name:
Institution:
Course:
Date:
Organizational behavior analyzes the environment in different perspectives in order to come up with policies which make the organization convenient in its business operations. The organization must analyze various factors which affect it in order to frame the different policies. This means finding out the challenges or problems which an individual face in an organization and also the problems that groups faces in the organization. In this context, organization behavior is simply the way which an organization uses to solve the problems in its environment (Kreitner 2012). This discussion will involve Apple Inc.
One of the challenges facing Apple Inc. is managing human resources. Human resources in Apple Inc. are an invaluable asset and are always associated with the organization. Apple had experienced problems in managing its human resources. Some of the issues it experienced include failing to retain employees’ talents, not observing diverse recruitment to its fullest, non-performance among employees and employees not getting their benefits appropriately (O'Grady 2015). This went hand in hand with violation of rules governing employees, code of conduct and features which keep the value of team and organization high. The individuals’ and organization’s wellbeing depend highly on each other. This means that what people do while in the organization should reflect what is in their mind. The organizational value highly depends on social responsibility which the organization is portraying. They should put up policies for protecting the organizational environment. The issue has affected the behavior of Apple and the human resource management sorted them out (O'Grady 2015).
Managing human resources and employees ethics is a very important issue and a backbone of any organization. If managed well, the organization is likely to succeed easily. If not managed well, the issues will spoil the organization’s reputation completely and the organization may not undergo dissolution (Kreitner 2012).
References
Kreitner, Angelo Kinicki & Robert. 2012. Organization behavior. New York: Wiley.
O'Grady, Jason D. 2015. Apple Inc. Westport, Conn: Greenwood Press.
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GrStudents: Copy the Student Data file data values into this sheet to assist in doing your weekly assignments.The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.The column labels in the table mean:ID – Employee sample number Salary – Salary in thousands Age – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)Service – Years of service (rounded)Gender – 0 = male, 1 = female Midpoi ...
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GrStudents: Copy the Student Data file data values into this sheet to assist in doing your weekly assignments.The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.The column labels in the table mean:ID – Employee sample number Salary – Salary in thousands Age – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)Service – Years of service (rounded)Gender – 0 = male, 1 = female Midpoint – salary grade midpoint Raise – percent of last raiseGrade – job/pay gradeDegree (0= BS\BA 1 = MS)Gender1 (Male or Female)Compa - salary divided by midpoint
Week 1Week 1.Measurement and Description - chapters 1 and 2The goal this week is to gain an understanding of our data set - what kind of data we are looking at, some descriptive measurse, and a look at how the data is distributed (shape).1Measurement issues. Data, even numerically coded variables, can be one of 4 levels - nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, asthis impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data.Please list under each label, the variables in our data set that belong in each group.NominalOrdinalIntervalRatiob.For each variable that you did not call ratio, why did you make that decision?2The first step in analyzing data sets is to find some summary descriptive statistics for key variables.For salary, compa, age, performance rating, and service; find the mean, standard deviation, and range for 3 groups: overall sample, Females, and Males.You can use either the Data Analysis Descriptive Statistics tool or the Fx =average and =stdev functions. (the range must be found using the difference between the =max and =min functions with Fx) functions.Note: Place data to the right, if you use Descriptive statistics, place that to the right as well.Some of the values are completed for you - please finish the table.SalaryCompaAgePerf. Rat.ServiceOverallMean35.785.99.0Standard Deviation8.251311.41475.7177Note - data is a sample from the larger company populationRange304521FemaleMean32.584.27.9Standard Deviation6.913.64.9Range26.045.018.0MaleMean38.987.610.0Standard Deviation8.48.76.4Range28.030.021.03What is the probability for a:Probabilitya. Randomly selected person being a male in grade E?b. Randomly selected male being in grade E? Note part b is the same as given a male, what is probabilty of being in grade E?c. Why are the results different?4A key issue in comparing data sets is to see if they are distributed/shaped the same. We can do this by looking at some measures of wheresome selected values are within each data set - that .
Week 5 Lecture 14 The Chi Square TestQuite often, patterns of .docxcockekeshia
Week 5 Lecture 14
The Chi Square Test
Quite often, patterns of responses or measures give us a lot of information. Patterns are generally the result of counting how many things fit into a particular category. Whenever we make a histogram, bar, or pie chart we are looking at the pattern of the data. Frequently, changes in these visual patterns will be our first clues that things have changed, and the first clue that we need to initiate a research study (Lind, Marchel, & Wathen, 2008).
One of the most useful test in examining patterns and relationships in data involving counts (how many fit into this category, how many into that, etc.) is the chi-square. It is extremely easy to calculate and has many more uses than we will cover. Examining patterns involves two uses of the Chi-square - the goodness of fit and the contingency table. Both of these uses have a common trait: they involve counts per group. In fact, the chi-square is the only statistic we will look at that we use when we have counts per multiple groups (Tanner & Youssef-Morgan, 2013). Chi Square Goodness of Fit Test
The goodness of fit test checks to see if the data distribution (counts per group) matches some pattern we are interested in. Example: Are the employees in our example company distributed equal across the grades? Or, a more reasonable expectation for a company might be are the employees distributed in a pyramid fashion – most on the bottom and few at the top?
The Chi Square test compares the actual versus a proposed distribution of counts by generating a measure for each cell or count: (actual – expected)2/actual. Summing these for all of the cells or groups provides us with the Chi Square Statistic. As with our other tests, we determine the p-value of getting a result as large or larger to determine if we reject or not reject our null hypothesis. An example will show the approach using Excel.
Regardless of the Chi Square test, the chi square related functions are found in the fx Statistics window rather than the Data Analysis where we found the t and ANOVA test functions. The most important for us are:
· CHISQ.TEST (actual range, expected range) – returns the p-value for the test
· CHISQ.INV.RT(p-value, df) – returns the actual Chi Square value for the p-value or probability value used.
· CHISQ.DIST.RT(X, df) – returns the p-value for a given value.
When we have a table of actual and expected results, using the =CHISQ.TEST(actual range, expected range) will provide us with the p-value of the calculated chi square value (but does not give us the actual calculated chi square value for the test). We can compare this value against our alpha criteria (generally 0.05) to make our decision about rejecting or not rejecting the null hypothesis.
If, after finding the p-value for our chi square test, we want to determine the calculated value of the chi square statistic, we can use the =CHISQ.INV.RT(probability, df) function, the value for probability is .
Week 2 – Lecture 3 Making judgements about differences bet.docxcockekeshia
Week 2 – Lecture 3
Making judgements about differences between group statistics is one of the most
powerful things that statistics can do for us. It is also one of the most counter-intuitive things
that we need to master in the class.
Lecture 1 introduced the hypothesis testing procedure used in statistical testing. Lecture
2 examined how to set up, perform, and interpret the F test for variance equality. This lecture
will focus on t-tests for testing mean equality. Again, these examples will use the compa-ratio
variable, while the homework should use the Salary variable.
The T-Test
While we test for variance equality with an F test, we use the T-Test to test for mean
equality testing. The t-test also uses the degree of freedom (df) value in providing us with our
probability result; but again, Excel does the work for us.
There are three versions of the T-Test done for us by Excel. The first two are similar
except one version is done if the variances are equal and the other if the variances are not equal.
(Now we see the second reason for performing the F-test first.)
The third version of the T-test is for paired data, and is called T-test Paired Two Sample
for Means. Paired data are two measures taken on the same subject. Examples include a math
and English test score for students, preference for different drinks, and, in our data set the salary
and midpoint values. Note that paired data must be measured in the same units, and be from the
same subjects. Students in the past have incorrectly used the paired t-test on male and female
salaries. These are not paired, as the measures are taken on different people and cannot be paired
together for analysis.
In many ways, setting up Excel’s T-tests, and virtually all the functions we will study,
follow the same steps as we just went through:
1. Set up the data into distinct groups.
2. Select the test function from either the Fx or Analysis list
3. Input the data ranges and output ranges into the appropriate entry boxes, checking
Labels if appropriate.
4. Clicking on OK to produce the output.
As with the F-test, the T-test has a couple of options depending upon what you want your
output to look like. The Fx (or Formulas) option returns simply the p-value for the selected
version of the test. The Data | Analysis selection provides descriptive statistics that are useful for
additional analysis (some of which we will discuss later in the course).
The t-test requires that we select between three versions, one assuming equal variances
between the populations, one assuming unequal variances in the populations, and one requiring
paired data (two measures on each element in the sample, such as salary and midpoint for each
person in our data set.) All have the same data set-up approach, so only one will be shown.
Question 2
The second question for this week asks about salary mean equality between males and
females. The data and test se.
Chi-square tests are great to show if distributions differ or i.docxMARRY7
Chi-square tests are great to show if distributions differ or if two variables interact in producing outcomes. What are some examples of variables that you might want to check using the chi-square tests? What would these results tell you?
DataSee comments at the right of the data set.IDSalaryCompaMidpointAgePerformance RatingServiceGenderRaiseDegreeGender1Grade8231.000233290915.80FAThe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 10220.956233080714.70FANote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.11231.00023411001914.80FA14241.04323329012160FAThe column labels in the table mean:15241.043233280814.90FAID – Employee sample number Salary – Salary in thousands 23231.000233665613.31FAAge – Age in yearsPerformance Rating – Appraisal rating (Employee evaluation score)26241.043232295216.21FAService – Years of service (rounded)Gender: 0 = male, 1 = female 31241.043232960413.90FAMidpoint – salary grade midpoint Raise – percent of last raise35241.043232390415.31FAGrade – job/pay gradeDegree (0= BS\BA 1 = MS)36231.000232775314.31FAGender1 (Male or Female)Compa - salary divided by midpoint37220.956232295216.21FA42241.0432332100815.70FA3341.096313075513.60FB18361.1613131801115.61FB20341.0963144701614.81FB39351.129312790615.51FB7411.0254032100815.70FC13421.0504030100214.71FC22571.187484865613.80FD24501.041483075913.81FD45551.145483695815.20FD17691.2105727553130FE48651.1405734901115.31FE28751.119674495914.41FF43771.1496742952015.51FF19241.043233285104.61MA25241.0432341704040MA40251.086232490206.30MA2270.870315280703.90MB32280.903312595405.60MB34280.903312680204.91MB16471.175404490405.70MC27401.000403580703.91MC41431.075402580504.30MC5470.9794836901605.71MD30491.0204845901804.30MD1581.017573485805.70ME4661.15757421001605.51ME12601.0525752952204.50ME33641.122573590905.51ME38560.9825745951104.50ME44601.0525745901605.21ME46651.1405739752003.91ME47621.087573795505.51ME49601.0525741952106.60ME50661.1575738801204.60ME6761.1346736701204.51MF9771.149674910010041MF21761.1346743951306.31MF29721.074675295505.40MF
Week 1Week 1.Measurement and Description - chapters 1 and 21Measurement issues. Data, even numerically coded variables, can be one of 4 levels - nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, asthis impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data.Please list under each label, the variables in our data set that belong in each group.NominalOrdinalIntervalRatiob.For each variable that you did not call ratio, why did you make that decision?2The first step in analyzing data sets is to find some summary descriptive statistics for key variables.For salary, compa, age, performance rating, and service; find the mean, standard deviation, and range for 3 groups: ...
1. Descriptive statistics provide a simple summary of data through measures of central tendency, frequency, and variability.
2. Common measures include the mean, median, mode, standard deviation, and outliers.
3. Inferential statistics allow researchers to make generalizations about populations based on analyses of samples. They include t-tests, ANOVA, correlation, and regression.
This document provides a tutorial on principal components analysis (PCA). It begins with an introduction to PCA and its applications. It then covers the necessary background mathematical concepts, including standard deviation, covariance, and eigenvalues/eigenvectors. The tutorial includes examples throughout and recommends a textbook for further mathematical information.
Statistical tests help justify if sample results can be applied to a population. ANOVA compares group means and is preferred over t-tests for 3+ groups. It calculates variation between and within groups to obtain an F-ratio. If the F-ratio exceeds its critical value, the null hypothesis that group means are equal is rejected, showing group means differ significantly. Two-way ANOVA extends this to consider two factors' influence, computing interaction effects between factors.
This presentation will address the issue of sample size determination for social sciences. A simple example is provided for every to understand and explain the sample size determination.
The document discusses hypotheses in research. A hypothesis is a testable statement about the relationship between two variables. Researchers propose a null hypothesis, which states there is no relationship between the variables, and an alternative or experimental hypothesis, which predicts a relationship. Statistical tests are used to analyze data and determine whether to reject the null hypothesis in favor of the alternative hypothesis. The document provides examples of different types of hypotheses and statistical tests used, including t-tests and z-tests.
This document provides guidelines for conducting parametric and non-parametric statistical tests in SPSS, including one-way ANOVA, repeated measures ANOVA, MANOVA, Kruskal-Wallis test, and Friedman's ANOVA. For one-way ANOVA, it discusses how to conduct planned contrasts to test specific hypotheses, and which post hoc tests are appropriate depending on equal group sizes and assumptions. The example analyzes exam marks from students in three teaching conditions to test the hypotheses that reward leads to better scores than punishment or indifference, and indifference leads to better scores than punishment.
STATISTICS : Changing the way we do: Hypothesis testing, effect size, power, ...Musfera Nara Vadia
Researchers should take several steps to make statistical results meaningful:
1. Perform a power analysis to determine adequate sample size and ensure power is above .50, ideally .80. Power is the probability of detecting real effects.
2. Never set the alpha level lower than .05 and try to set it higher to .10 if acceptable.
3. Report effect sizes and confidence intervals to provide context around statistical significance. Effect sizes indicate the magnitude of differences between groups.
Excel Files AssingmentsCopy of Student_Assignment_File.11.01..docxSANSKAR20
Excel Files Assingments/Copy of Student_Assignment_File.11.01.2016.xlsx
DataIDSalaryCompa-ratioMidpointAgePerformance RatingServiceGenderRaiseDegreeGender1GradeCopy Employee Data set to this page.The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.The column labels in the table mean:ID – Employee sample number Salary – Salary in thousands Age – Age in yearsPerformance Rating – Appraisal rating (Employee evaluation score)SERvice – Years of serviceGender: 0 = male, 1 = female Midpoint – salary grade midpoint Raise – percent of last raiseGrade – job/pay gradeDegree (0= BS\BA 1 = MS)Gender1 (Male or Female)Compa-ratio - salary divided by midpoint
Week 2This assignment covers the material presented in weeks 1 and 2.Six QuestionsBefore starting this assignment, make sure the the assignment data from the Employee Salary Data Set file is copied over to this Assignment file.You can do this either by a copy and paste of all the columns or by opening the data file, right clicking on the Data tab, selecting Move or Copy, and copying the entire sheet to this file(Weekly Assignment Sheet or whatever you are calling your master assignment file).It is highly recommended that you copy the data columns (with labels) and paste them to the right so that whatever you do will not disrupt the original data values and relationships.To Ensure full credit for each question, you need to show how you got your results. For example, Question 1 asks for several data values. If you obtain them using descriptive statistics,then the cells should have an "=XX" formula in them, where XX is the column and row number showing the value in the descriptive statistics table. If you choose to generate each value using fxfunctions, then each function should be located in the cell and the location of the data values should be shown.So, Cell D31 - as an example - shoud contain something like "=T6" or "=average(T2:T26)". Having only a numerical value will not earn full credit.The reason for this is to allow instructors to provide feedback on Excel tools if the answers are not correct - we need to see how the results were obtained.In starting the analysis on a research question, we focus on overall descriptive statistics and seeing if differences exist. Probing into reasons and mitigating factors is a follow-up activity.1The first step in analyzing data sets is to find some summary descriptive statistics for key variables. Since the assignment problems willfocus mostly on the compa-ratios, we need to find the mean, standard deviations, and range for our groups: Males, Females, and Overall.Sorting the compa-ratios into male and females will require you copy and paste the Compa-ratio and Gender1 columns, and then sort on Gender1.The values for age, performance rating, and service are prov ...
Christian Schussele Men of ProgressOil on canvas, 1862Coope.docxtroutmanboris
Christian Schussele Men of Progress
Oil on canvas, 1862
Cooper Union, New York, New York
Transfer from the National Gallery of Art; gift of Andrew W. Mellon, 1942
NPG.65.60
Edward Sorel, “People of Progress” 1999, Cooper Union, New York, New York
Syllabus
The clerks of the Department of State of the United States may be called upon to give evidence of transactions in the Department which are not of a confidential character.
The Secretary of State cannot be called upon as a witness to state transactions of a confidential nature which may have occurred in his Department. But he may be called upon to give testimony of circumstances which were not of that character.
Clerks in the Department of State were directed to be sworn, subject to objections to questions upon confidential matters.
Some point of time must be taken when the power of the Executive over an officer, not removable at his will, must cease. That point of time must be when the constitutional power of appointment has been exercised. And the power has been exercised when the last act required from the person possessing the power has been performed. This last act is the signature of the commission.
If the act of livery be necessary to give validity to the commission of an officer, it has been delivered when executed, and given to the Secretary of State for the purpose of being sealed, recorded, and transmitted to the party.
In cases of commissions to public officers, the law orders the Secretary of State to record them. When, therefore, they are signed and sealed, the order for their being recorded is given, and, whether inserted inserted into the book or not, they are recorded.
When the heads of the departments of the Government are the political or confidential officers of the Executive, merely to execute the will of the President, or rather to act in cases in which the Executive possesses a constitutional or legal discretion, nothing can be more perfectly clear than that their acts are only politically examinable. But where a specific duty is assigned by law, and individual rights depend upon the performance of that duty, it seems equally clear that the individual who considers himself injured has a right to resort to the laws of his country for a remedy.
The President of the United States, by signing the commission, appointed Mr. Marbury a justice of the peace for the County of Washington, in the District of Columbia, and the seal of the United States, affixed thereto by the Secretary of State, is conclusive testimony of the verity of the signature, and of the completion of the appointment; and the appointment conferred on him a legal right to the office for the space of five years. Having this legal right to the office, he has a consequent right to the commission, a refusal to deliver which is a plain violation of that right for which the laws of the country afford him a remedy.
To render a mandamus a proper remedy, the officer to whom it is directed must be one to who.
Christian EthicsChristian ethics deeply align with absolutism. E.docxtroutmanboris
Christian Ethics
Christian ethics deeply align with absolutism. Ethical absolutism claims that moral principles do exist. According to Christians, God created moral absolutes. These absolutes can be seen in God’s revelation. God’s special and general revelation reveal his moral truths. This does not mean that only Christians can understand moral truths. Because humans are made in God’s image, they can recognize moral truths even if they do not believe in God
[1]
. These absolutes were instated by God. Therefore, they apply to all of humanity. This worldview is in direct opposition to the idea of relativism. Christian ethics cannot be viewed through a relativistic point of view. According to relativism, there is no moral truths. There is no absolute distinction between right and wrong within this way of thinking. Right and wrong can be decided by individuals or groups of people. Cultures decide what is right for themselves and their way of life. Even individuals have the ability to decide their own personal moral code. This can seem somewhat reasonable at times. Some things that were considered moral or immoral in the past are viewed differently today. Even with this understanding, Christians deny the idea of relativism. Christians hold to the belief that moral truths come from God. Therefore, these truths do not change. God himself never changes; therefore, his moral truths remain the same. According to Christian ethics, mankind is expected to hold to the moral absolutes mandated by God himself. This understanding is not compatible with relativism. Relativism makes no place of a God. From a relativistic point of view, mankind decides their own morality. Right and wrong are not fixed. In Christian ethics, right and wrong are permanently decided by the God of the universe.
The subjective aspects of Christian ethics can look similar to relativism. The areas that are somewhat subjective in Christian aspects are referred to as the liberties of a Christian. There are some matters that are not said to be morally wrong in the Bible. Some see these issues to be wrong; therefore, they are. Others do not find certain issues to be morally wrong. These individuals are claiming their Christian liberty. One of these issues is drinking alcohol. Some Christians believe that ingesting any amount of alcohol is morally wrong. According to the idea of Christian liberty, it would be wrong for the individuals who hold to this belief to drink alcohol. Others do not have this conviction and are not doing wrong by consuming alcohol. On the surface, the idea of Christian liberty can seem to be related to relativism, but upon closer inspection these ideas are not closely related. Christian liberty is a Biblical concept that harmonize well with the overall message of the Bible. Relativism is nowhere found in the Bible. The Bible is clear that there are universal moral laws. These laws are placed upon humanity by God himself. There are some areas where the Bible remain.
More Related Content
Similar to 4DDBA 8307 Week 7 Assignment TemplateJohn DoeD.docx
Week 5 Lecture 14 The Chi Square Test Quite often, pat.docxcockekeshia
Week 5 Lecture 14
The Chi Square Test
Quite often, patterns of responses or measures give us a lot of information. Patterns are
generally the result of counting how many things fit into a particular category. Whenever we
make a histogram, bar, or pie chart we are looking at the pattern of the data. Frequently, changes
in these visual patterns will be our first clues that things have changed, and the first clue that we
need to initiate a research study (Lind, Marchel, & Wathen, 2008).
One of the most useful test in examining patterns and relationships in data involving
counts (how many fit into this category, how many into that, etc.) is the chi-square. It is
extremely easy to calculate and has many more uses than we will cover. Examining patterns
involves two uses of the Chi-square - the goodness of fit and the contingency table. Both of
these uses have a common trait: they involve counts per group. In fact, the chi-square is the only
statistic we will look at that we use when we have counts per multiple groups (Tanner &
Youssef-Morgan, 2013).
Chi Square Goodness of Fit Test
The goodness of fit test checks to see if the data distribution (counts per group) matches
some pattern we are interested in. Example: Are the employees in our example company
distributed equal across the grades? Or, a more reasonable expectation for a company might be
are the employees distributed in a pyramid fashion – most on the bottom and few at the top?
The Chi Square test compares the actual versus a proposed distribution of counts by
generating a measure for each cell or count: (actual – expected)2/actual. Summing these for all
of the cells or groups provides us with the Chi Square Statistic. As with our other tests, we
determine the p-value of getting a result as large or larger to determine if we reject or not reject
our null hypothesis. An example will show the approach using Excel.
Regardless of the Chi Square test, the chi square related functions are found in the fx
Statistics window rather than the Data Analysis where we found the t and ANOVA test
functions. The most important for us are:
• CHISQ.TEST (actual range, expected range) – returns the p-value for the test
• CHISQ.INV.RT(p-value, df) – returns the actual Chi Square value for the p-value
or probability value used.
• CHISQ.DIST.RT(X, df) – returns the p-value for a given value.
When we have a table of actual and expected results, using the =CHISQ.TEST(actual
range, expected range) will provide us with the p-value of the calculated chi square value (but
does not give us the actual calculated chi square value for the test). We can compare this value
against our alpha criteria (generally 0.05) to make our decision about rejecting or not rejecting
the null hypothesis.
If, after finding the p-value for our chi square test, we want to determine the calculated
value of the chi square statistic, we can use the =CHISQ.INV.RT(probability, df).
Chapter 18 – Pricing Setting in the Business WorldThere are few .docxrobert345678
Chapter 18 – Pricing Setting in the Business World
There are few Methods for setting pricing – costs methods vs demand methods
Formulas considering costs and mark up will help you to do the Problem set assignment:
1. Markup for setting prices (Mark up $ = SP-CP); MARK UP % = (MU $/SP) X100)
Formula for setting price with the markup method
SP = Cost/(1- Markup %)
Example - retailer buys A hat for $15 and wants a 40% markup, his selling price would be….
SP = 15/(1-.40)=.60
= $25.00
2. Understand Role of different costs – fixed, variable, total costs and average costs
3. What is the breakeven point? Formula for calculating the Break Even point.
BE = Total Fixed Cost/Fixed cost contribution
Fixed Cost Contribution=Price – variable cost
4. Average Cost = when there are many flavors/types of the same product, producer determines average cost and then add the mark up to set a common selling price.
.ANOVA
Analysis of Variance is a method of testing the equality of three or more population
means by analyzing sample variance.
One-Way ANOVA
The one-way ANOVA is used to compare three or more population means when there is
one factor of interest.
Requirements
The populations have distributions that are approximately normal.
The populations have the same variance.
The samples are simple random samples of quantitative data.
The samples are independent of each other.
The different samples are from populations that are categorized in only one.
way
One-Way ANOVA is a hypothesis test. There are seven steps for a hypothesis test.
Example
A professor at a local University believes there is a relationship between head size and
the major of the students in her biostatistics classes. She takes a random sample of 20
students from each of three classes and records their major and head circumference.
The data are shown in the following table.
Step 1: State the null hypothesis.
Mean 1 equals mean 2 equals mean 3 equals mean 4.
Step 2: State the Alternative hypothesis.
At least one mean is different.
Step 3: State the Level of Significance.
The level of significance is 0.05.
Step 4: State the test statistic.
variance between samples
variance within samples
F
The test statistic follows the F distribution which has two degrees of freedom, one for
the numerator and one for the denominator.
The calculations for the test statistic are complicated, so a software program is
generally used for the calculations. We will be using Microsoft Excel for this example.
Step 5: Calculate
The calculations are done in Microsoft Excel using the data analysis toolpak. Enter the
data into the spread sheet as shown here. Click on data and the data analysis tookpak
button is on the right.
When you click on the button a dialogue box appears.
Choose ANOVA One Factor. Then another dialogue box appears.
Input range is where the data is in the table. Be sure to put a check in the box for labels
in.
Running head Organization behaviorOrganization behavior 2.docxtoltonkendal
Running head: Organization behavior
Organization behavior 2
Organization behavior
Name:
Institution:
Course:
Date:
Organizational behavior analyzes the environment in different perspectives in order to come up with policies which make the organization convenient in its business operations. The organization must analyze various factors which affect it in order to frame the different policies. This means finding out the challenges or problems which an individual face in an organization and also the problems that groups faces in the organization. In this context, organization behavior is simply the way which an organization uses to solve the problems in its environment (Kreitner 2012). This discussion will involve Apple Inc.
One of the challenges facing Apple Inc. is managing human resources. Human resources in Apple Inc. are an invaluable asset and are always associated with the organization. Apple had experienced problems in managing its human resources. Some of the issues it experienced include failing to retain employees’ talents, not observing diverse recruitment to its fullest, non-performance among employees and employees not getting their benefits appropriately (O'Grady 2015). This went hand in hand with violation of rules governing employees, code of conduct and features which keep the value of team and organization high. The individuals’ and organization’s wellbeing depend highly on each other. This means that what people do while in the organization should reflect what is in their mind. The organizational value highly depends on social responsibility which the organization is portraying. They should put up policies for protecting the organizational environment. The issue has affected the behavior of Apple and the human resource management sorted them out (O'Grady 2015).
Managing human resources and employees ethics is a very important issue and a backbone of any organization. If managed well, the organization is likely to succeed easily. If not managed well, the issues will spoil the organization’s reputation completely and the organization may not undergo dissolution (Kreitner 2012).
References
Kreitner, Angelo Kinicki & Robert. 2012. Organization behavior. New York: Wiley.
O'Grady, Jason D. 2015. Apple Inc. Westport, Conn: Greenwood Press.
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GrStudents: Copy the Student Data file data values into this sheet to assist in doing your weekly assignments.The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.The column labels in the table mean:ID – Employee sample number Salary – Salary in thousands Age – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)Service – Years of service (rounded)Gender – 0 = male, 1 = female Midpoi ...
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GrStudents: Copy the Student Data file data values into this sheet to assist in doing your weekly assignments.The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.The column labels in the table mean:ID – Employee sample number Salary – Salary in thousands Age – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)Service – Years of service (rounded)Gender – 0 = male, 1 = female Midpoint – salary grade midpoint Raise – percent of last raiseGrade – job/pay gradeDegree (0= BS\BA 1 = MS)Gender1 (Male or Female)Compa - salary divided by midpoint
Week 1Week 1.Measurement and Description - chapters 1 and 2The goal this week is to gain an understanding of our data set - what kind of data we are looking at, some descriptive measurse, and a look at how the data is distributed (shape).1Measurement issues. Data, even numerically coded variables, can be one of 4 levels - nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, asthis impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data.Please list under each label, the variables in our data set that belong in each group.NominalOrdinalIntervalRatiob.For each variable that you did not call ratio, why did you make that decision?2The first step in analyzing data sets is to find some summary descriptive statistics for key variables.For salary, compa, age, performance rating, and service; find the mean, standard deviation, and range for 3 groups: overall sample, Females, and Males.You can use either the Data Analysis Descriptive Statistics tool or the Fx =average and =stdev functions. (the range must be found using the difference between the =max and =min functions with Fx) functions.Note: Place data to the right, if you use Descriptive statistics, place that to the right as well.Some of the values are completed for you - please finish the table.SalaryCompaAgePerf. Rat.ServiceOverallMean35.785.99.0Standard Deviation8.251311.41475.7177Note - data is a sample from the larger company populationRange304521FemaleMean32.584.27.9Standard Deviation6.913.64.9Range26.045.018.0MaleMean38.987.610.0Standard Deviation8.48.76.4Range28.030.021.03What is the probability for a:Probabilitya. Randomly selected person being a male in grade E?b. Randomly selected male being in grade E? Note part b is the same as given a male, what is probabilty of being in grade E?c. Why are the results different?4A key issue in comparing data sets is to see if they are distributed/shaped the same. We can do this by looking at some measures of wheresome selected values are within each data set - that .
Week 5 Lecture 14 The Chi Square TestQuite often, patterns of .docxcockekeshia
Week 5 Lecture 14
The Chi Square Test
Quite often, patterns of responses or measures give us a lot of information. Patterns are generally the result of counting how many things fit into a particular category. Whenever we make a histogram, bar, or pie chart we are looking at the pattern of the data. Frequently, changes in these visual patterns will be our first clues that things have changed, and the first clue that we need to initiate a research study (Lind, Marchel, & Wathen, 2008).
One of the most useful test in examining patterns and relationships in data involving counts (how many fit into this category, how many into that, etc.) is the chi-square. It is extremely easy to calculate and has many more uses than we will cover. Examining patterns involves two uses of the Chi-square - the goodness of fit and the contingency table. Both of these uses have a common trait: they involve counts per group. In fact, the chi-square is the only statistic we will look at that we use when we have counts per multiple groups (Tanner & Youssef-Morgan, 2013). Chi Square Goodness of Fit Test
The goodness of fit test checks to see if the data distribution (counts per group) matches some pattern we are interested in. Example: Are the employees in our example company distributed equal across the grades? Or, a more reasonable expectation for a company might be are the employees distributed in a pyramid fashion – most on the bottom and few at the top?
The Chi Square test compares the actual versus a proposed distribution of counts by generating a measure for each cell or count: (actual – expected)2/actual. Summing these for all of the cells or groups provides us with the Chi Square Statistic. As with our other tests, we determine the p-value of getting a result as large or larger to determine if we reject or not reject our null hypothesis. An example will show the approach using Excel.
Regardless of the Chi Square test, the chi square related functions are found in the fx Statistics window rather than the Data Analysis where we found the t and ANOVA test functions. The most important for us are:
· CHISQ.TEST (actual range, expected range) – returns the p-value for the test
· CHISQ.INV.RT(p-value, df) – returns the actual Chi Square value for the p-value or probability value used.
· CHISQ.DIST.RT(X, df) – returns the p-value for a given value.
When we have a table of actual and expected results, using the =CHISQ.TEST(actual range, expected range) will provide us with the p-value of the calculated chi square value (but does not give us the actual calculated chi square value for the test). We can compare this value against our alpha criteria (generally 0.05) to make our decision about rejecting or not rejecting the null hypothesis.
If, after finding the p-value for our chi square test, we want to determine the calculated value of the chi square statistic, we can use the =CHISQ.INV.RT(probability, df) function, the value for probability is .
Week 2 – Lecture 3 Making judgements about differences bet.docxcockekeshia
Week 2 – Lecture 3
Making judgements about differences between group statistics is one of the most
powerful things that statistics can do for us. It is also one of the most counter-intuitive things
that we need to master in the class.
Lecture 1 introduced the hypothesis testing procedure used in statistical testing. Lecture
2 examined how to set up, perform, and interpret the F test for variance equality. This lecture
will focus on t-tests for testing mean equality. Again, these examples will use the compa-ratio
variable, while the homework should use the Salary variable.
The T-Test
While we test for variance equality with an F test, we use the T-Test to test for mean
equality testing. The t-test also uses the degree of freedom (df) value in providing us with our
probability result; but again, Excel does the work for us.
There are three versions of the T-Test done for us by Excel. The first two are similar
except one version is done if the variances are equal and the other if the variances are not equal.
(Now we see the second reason for performing the F-test first.)
The third version of the T-test is for paired data, and is called T-test Paired Two Sample
for Means. Paired data are two measures taken on the same subject. Examples include a math
and English test score for students, preference for different drinks, and, in our data set the salary
and midpoint values. Note that paired data must be measured in the same units, and be from the
same subjects. Students in the past have incorrectly used the paired t-test on male and female
salaries. These are not paired, as the measures are taken on different people and cannot be paired
together for analysis.
In many ways, setting up Excel’s T-tests, and virtually all the functions we will study,
follow the same steps as we just went through:
1. Set up the data into distinct groups.
2. Select the test function from either the Fx or Analysis list
3. Input the data ranges and output ranges into the appropriate entry boxes, checking
Labels if appropriate.
4. Clicking on OK to produce the output.
As with the F-test, the T-test has a couple of options depending upon what you want your
output to look like. The Fx (or Formulas) option returns simply the p-value for the selected
version of the test. The Data | Analysis selection provides descriptive statistics that are useful for
additional analysis (some of which we will discuss later in the course).
The t-test requires that we select between three versions, one assuming equal variances
between the populations, one assuming unequal variances in the populations, and one requiring
paired data (two measures on each element in the sample, such as salary and midpoint for each
person in our data set.) All have the same data set-up approach, so only one will be shown.
Question 2
The second question for this week asks about salary mean equality between males and
females. The data and test se.
Chi-square tests are great to show if distributions differ or i.docxMARRY7
Chi-square tests are great to show if distributions differ or if two variables interact in producing outcomes. What are some examples of variables that you might want to check using the chi-square tests? What would these results tell you?
DataSee comments at the right of the data set.IDSalaryCompaMidpointAgePerformance RatingServiceGenderRaiseDegreeGender1Grade8231.000233290915.80FAThe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 10220.956233080714.70FANote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.11231.00023411001914.80FA14241.04323329012160FAThe column labels in the table mean:15241.043233280814.90FAID – Employee sample number Salary – Salary in thousands 23231.000233665613.31FAAge – Age in yearsPerformance Rating – Appraisal rating (Employee evaluation score)26241.043232295216.21FAService – Years of service (rounded)Gender: 0 = male, 1 = female 31241.043232960413.90FAMidpoint – salary grade midpoint Raise – percent of last raise35241.043232390415.31FAGrade – job/pay gradeDegree (0= BS\BA 1 = MS)36231.000232775314.31FAGender1 (Male or Female)Compa - salary divided by midpoint37220.956232295216.21FA42241.0432332100815.70FA3341.096313075513.60FB18361.1613131801115.61FB20341.0963144701614.81FB39351.129312790615.51FB7411.0254032100815.70FC13421.0504030100214.71FC22571.187484865613.80FD24501.041483075913.81FD45551.145483695815.20FD17691.2105727553130FE48651.1405734901115.31FE28751.119674495914.41FF43771.1496742952015.51FF19241.043233285104.61MA25241.0432341704040MA40251.086232490206.30MA2270.870315280703.90MB32280.903312595405.60MB34280.903312680204.91MB16471.175404490405.70MC27401.000403580703.91MC41431.075402580504.30MC5470.9794836901605.71MD30491.0204845901804.30MD1581.017573485805.70ME4661.15757421001605.51ME12601.0525752952204.50ME33641.122573590905.51ME38560.9825745951104.50ME44601.0525745901605.21ME46651.1405739752003.91ME47621.087573795505.51ME49601.0525741952106.60ME50661.1575738801204.60ME6761.1346736701204.51MF9771.149674910010041MF21761.1346743951306.31MF29721.074675295505.40MF
Week 1Week 1.Measurement and Description - chapters 1 and 21Measurement issues. Data, even numerically coded variables, can be one of 4 levels - nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, asthis impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data.Please list under each label, the variables in our data set that belong in each group.NominalOrdinalIntervalRatiob.For each variable that you did not call ratio, why did you make that decision?2The first step in analyzing data sets is to find some summary descriptive statistics for key variables.For salary, compa, age, performance rating, and service; find the mean, standard deviation, and range for 3 groups: ...
1. Descriptive statistics provide a simple summary of data through measures of central tendency, frequency, and variability.
2. Common measures include the mean, median, mode, standard deviation, and outliers.
3. Inferential statistics allow researchers to make generalizations about populations based on analyses of samples. They include t-tests, ANOVA, correlation, and regression.
This document provides a tutorial on principal components analysis (PCA). It begins with an introduction to PCA and its applications. It then covers the necessary background mathematical concepts, including standard deviation, covariance, and eigenvalues/eigenvectors. The tutorial includes examples throughout and recommends a textbook for further mathematical information.
Statistical tests help justify if sample results can be applied to a population. ANOVA compares group means and is preferred over t-tests for 3+ groups. It calculates variation between and within groups to obtain an F-ratio. If the F-ratio exceeds its critical value, the null hypothesis that group means are equal is rejected, showing group means differ significantly. Two-way ANOVA extends this to consider two factors' influence, computing interaction effects between factors.
This presentation will address the issue of sample size determination for social sciences. A simple example is provided for every to understand and explain the sample size determination.
The document discusses hypotheses in research. A hypothesis is a testable statement about the relationship between two variables. Researchers propose a null hypothesis, which states there is no relationship between the variables, and an alternative or experimental hypothesis, which predicts a relationship. Statistical tests are used to analyze data and determine whether to reject the null hypothesis in favor of the alternative hypothesis. The document provides examples of different types of hypotheses and statistical tests used, including t-tests and z-tests.
This document provides guidelines for conducting parametric and non-parametric statistical tests in SPSS, including one-way ANOVA, repeated measures ANOVA, MANOVA, Kruskal-Wallis test, and Friedman's ANOVA. For one-way ANOVA, it discusses how to conduct planned contrasts to test specific hypotheses, and which post hoc tests are appropriate depending on equal group sizes and assumptions. The example analyzes exam marks from students in three teaching conditions to test the hypotheses that reward leads to better scores than punishment or indifference, and indifference leads to better scores than punishment.
STATISTICS : Changing the way we do: Hypothesis testing, effect size, power, ...Musfera Nara Vadia
Researchers should take several steps to make statistical results meaningful:
1. Perform a power analysis to determine adequate sample size and ensure power is above .50, ideally .80. Power is the probability of detecting real effects.
2. Never set the alpha level lower than .05 and try to set it higher to .10 if acceptable.
3. Report effect sizes and confidence intervals to provide context around statistical significance. Effect sizes indicate the magnitude of differences between groups.
Excel Files AssingmentsCopy of Student_Assignment_File.11.01..docxSANSKAR20
Excel Files Assingments/Copy of Student_Assignment_File.11.01.2016.xlsx
DataIDSalaryCompa-ratioMidpointAgePerformance RatingServiceGenderRaiseDegreeGender1GradeCopy Employee Data set to this page.The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.The column labels in the table mean:ID – Employee sample number Salary – Salary in thousands Age – Age in yearsPerformance Rating – Appraisal rating (Employee evaluation score)SERvice – Years of serviceGender: 0 = male, 1 = female Midpoint – salary grade midpoint Raise – percent of last raiseGrade – job/pay gradeDegree (0= BS\BA 1 = MS)Gender1 (Male or Female)Compa-ratio - salary divided by midpoint
Week 2This assignment covers the material presented in weeks 1 and 2.Six QuestionsBefore starting this assignment, make sure the the assignment data from the Employee Salary Data Set file is copied over to this Assignment file.You can do this either by a copy and paste of all the columns or by opening the data file, right clicking on the Data tab, selecting Move or Copy, and copying the entire sheet to this file(Weekly Assignment Sheet or whatever you are calling your master assignment file).It is highly recommended that you copy the data columns (with labels) and paste them to the right so that whatever you do will not disrupt the original data values and relationships.To Ensure full credit for each question, you need to show how you got your results. For example, Question 1 asks for several data values. If you obtain them using descriptive statistics,then the cells should have an "=XX" formula in them, where XX is the column and row number showing the value in the descriptive statistics table. If you choose to generate each value using fxfunctions, then each function should be located in the cell and the location of the data values should be shown.So, Cell D31 - as an example - shoud contain something like "=T6" or "=average(T2:T26)". Having only a numerical value will not earn full credit.The reason for this is to allow instructors to provide feedback on Excel tools if the answers are not correct - we need to see how the results were obtained.In starting the analysis on a research question, we focus on overall descriptive statistics and seeing if differences exist. Probing into reasons and mitigating factors is a follow-up activity.1The first step in analyzing data sets is to find some summary descriptive statistics for key variables. Since the assignment problems willfocus mostly on the compa-ratios, we need to find the mean, standard deviations, and range for our groups: Males, Females, and Overall.Sorting the compa-ratios into male and females will require you copy and paste the Compa-ratio and Gender1 columns, and then sort on Gender1.The values for age, performance rating, and service are prov ...
Similar to 4DDBA 8307 Week 7 Assignment TemplateJohn DoeD.docx (20)
Christian Schussele Men of ProgressOil on canvas, 1862Coope.docxtroutmanboris
Christian Schussele Men of Progress
Oil on canvas, 1862
Cooper Union, New York, New York
Transfer from the National Gallery of Art; gift of Andrew W. Mellon, 1942
NPG.65.60
Edward Sorel, “People of Progress” 1999, Cooper Union, New York, New York
Syllabus
The clerks of the Department of State of the United States may be called upon to give evidence of transactions in the Department which are not of a confidential character.
The Secretary of State cannot be called upon as a witness to state transactions of a confidential nature which may have occurred in his Department. But he may be called upon to give testimony of circumstances which were not of that character.
Clerks in the Department of State were directed to be sworn, subject to objections to questions upon confidential matters.
Some point of time must be taken when the power of the Executive over an officer, not removable at his will, must cease. That point of time must be when the constitutional power of appointment has been exercised. And the power has been exercised when the last act required from the person possessing the power has been performed. This last act is the signature of the commission.
If the act of livery be necessary to give validity to the commission of an officer, it has been delivered when executed, and given to the Secretary of State for the purpose of being sealed, recorded, and transmitted to the party.
In cases of commissions to public officers, the law orders the Secretary of State to record them. When, therefore, they are signed and sealed, the order for their being recorded is given, and, whether inserted inserted into the book or not, they are recorded.
When the heads of the departments of the Government are the political or confidential officers of the Executive, merely to execute the will of the President, or rather to act in cases in which the Executive possesses a constitutional or legal discretion, nothing can be more perfectly clear than that their acts are only politically examinable. But where a specific duty is assigned by law, and individual rights depend upon the performance of that duty, it seems equally clear that the individual who considers himself injured has a right to resort to the laws of his country for a remedy.
The President of the United States, by signing the commission, appointed Mr. Marbury a justice of the peace for the County of Washington, in the District of Columbia, and the seal of the United States, affixed thereto by the Secretary of State, is conclusive testimony of the verity of the signature, and of the completion of the appointment; and the appointment conferred on him a legal right to the office for the space of five years. Having this legal right to the office, he has a consequent right to the commission, a refusal to deliver which is a plain violation of that right for which the laws of the country afford him a remedy.
To render a mandamus a proper remedy, the officer to whom it is directed must be one to who.
Christian EthicsChristian ethics deeply align with absolutism. E.docxtroutmanboris
Christian Ethics
Christian ethics deeply align with absolutism. Ethical absolutism claims that moral principles do exist. According to Christians, God created moral absolutes. These absolutes can be seen in God’s revelation. God’s special and general revelation reveal his moral truths. This does not mean that only Christians can understand moral truths. Because humans are made in God’s image, they can recognize moral truths even if they do not believe in God
[1]
. These absolutes were instated by God. Therefore, they apply to all of humanity. This worldview is in direct opposition to the idea of relativism. Christian ethics cannot be viewed through a relativistic point of view. According to relativism, there is no moral truths. There is no absolute distinction between right and wrong within this way of thinking. Right and wrong can be decided by individuals or groups of people. Cultures decide what is right for themselves and their way of life. Even individuals have the ability to decide their own personal moral code. This can seem somewhat reasonable at times. Some things that were considered moral or immoral in the past are viewed differently today. Even with this understanding, Christians deny the idea of relativism. Christians hold to the belief that moral truths come from God. Therefore, these truths do not change. God himself never changes; therefore, his moral truths remain the same. According to Christian ethics, mankind is expected to hold to the moral absolutes mandated by God himself. This understanding is not compatible with relativism. Relativism makes no place of a God. From a relativistic point of view, mankind decides their own morality. Right and wrong are not fixed. In Christian ethics, right and wrong are permanently decided by the God of the universe.
The subjective aspects of Christian ethics can look similar to relativism. The areas that are somewhat subjective in Christian aspects are referred to as the liberties of a Christian. There are some matters that are not said to be morally wrong in the Bible. Some see these issues to be wrong; therefore, they are. Others do not find certain issues to be morally wrong. These individuals are claiming their Christian liberty. One of these issues is drinking alcohol. Some Christians believe that ingesting any amount of alcohol is morally wrong. According to the idea of Christian liberty, it would be wrong for the individuals who hold to this belief to drink alcohol. Others do not have this conviction and are not doing wrong by consuming alcohol. On the surface, the idea of Christian liberty can seem to be related to relativism, but upon closer inspection these ideas are not closely related. Christian liberty is a Biblical concept that harmonize well with the overall message of the Bible. Relativism is nowhere found in the Bible. The Bible is clear that there are universal moral laws. These laws are placed upon humanity by God himself. There are some areas where the Bible remain.
Christian Ethics BA 616 Business Ethics Definiti.docxtroutmanboris
Christian Ethics
BA 616 Business Ethics
Definition of Christian Ethics
A system of values based upon the Judeo/Christian Scriptures
Principles of behavior in concordance with the behaviors of Christian teachings
Standards of thought and behavior as taught by Jesus.
Discussion
What are some of the “ethical” attributes presented in the teachings of Jesus?
What are some ethical attributes presented in the teachings of other religious persons?
Quotes about Christian Ethics
Quotes on Christian Ethics
Recognize the value of work
“And when you reap the harvest of your land, you shall not reap your field right up to its edge, nor shall you gather the gleanings after your harvest. You shall leave them for the poor and for the sojourner: I am the Lord your God.” (Leviticus 23:22).
Do not give the poor the food, rather allow the poor to work for themselves
Discussion
What are examples of the value of work?
Today, some U.S. state governors are trying to get those “able bodied” individuals to work for welfare. They are meeting great resistance politically, why do you think this is?
The value of work
Confirmed by Elton Mayo
Fulfills social, psychological and economic needs of the individual
“If a man will not work, he shall not eat” (2 Thessalonians 3:10)
Christian Ethics
The fruit of a people that have inwardly committed their lives to Christ and are outwardly aligning their actions with His teachings.
“May the favor of the Lord our God rest on us; establish the work of our hands for us— yes, establish the work of our hands” (Psalms. 90:17).
Employees with a Christian Code of Ethics
Welcome accountability
Happy to show their efforts
A system of checks and balances
Sees possible training moment
Fosters collaboration with management
“Those who work their land will have abundant food, but those who chase fantasies have no sense” (Proverbs 12:11)
Employees with a Christian Code of Ethics
Not motivated by greed
Work is its own reward
Measure success in a non-monetary way
Seek payment for the work they do
Money is second to obedience
“Whatever you do, work at it with all your heart, as working for the Lord, not for human masters” (Colossians 3:23).
Employees with a Christian Code of Ethics
Are highly productive
Are work focused
Work hard throughout the day
Find value in completing assigned tasks
Understand that they are there to work
“Diligent hands will rule, but laziness ends in forced labor” (Proverbs 12:24).
Employees with a Christian Code of Ethics
Have a strong work ethic
Believe in a Biblical perspective of work
Reliable
Recognize the value of work
Relate their job to their faith
“All hard work brings a profit, but mere talk leads only to poverty” (Proverbs 14:23)
Employees with a Christian Code of Ethics
Bring a cooperative spirit to the workplace
Supportive of management
Strong contribu.
CHPSI think you made a really good point that Howard lacks poli.docxtroutmanboris
CH/PS
I think you made a really good point that Howard lacks political aspects-especially for presidency. I have no heard his speeches quite yet (since I tend to stray away from politics altogether because people are so aggressive), do you think he is a great leader-type and is he charismatic at all? Great leaders, especially for presidency, should be honest, charismatic, and not only cater to the audience's needs but to the entire country's needs without sugar coating things.
Also, I am not sure what you mean by "In order to improve his leadership style, Jeff should change his model of carrying out business activities. This is because it can be copied and imitated by other companies (Mauri, 2016)".- how can it be imitted by other companies? In what way?
Do you think Jeff Bezos is a bad leader? and why?
CH/AR
I found your comparison of Howard Schultz and Jeff Bezos interesting and compelling. When I was looking at the list of leaders to select from, it was staggering to me how many of the corporate leaders have run or are planning to run for political office. I'm not sure, given our current political environment, that running a large corporation is the right background and experience for the leader of the United States. We'll see what happens in the next year and a half!
Amazon is an amazing, transformative company to watch. I work in the financial services industry and one of our leaders recently described our competition not as other financial services firms but as Amazon. Financial services firms pretty much all offer the same products and services and at a very reasonable price point. Amazon, however, has excelled in service delivery. I would imagine that at sometime in the future, Amazon will partner with a financial service firm to deliver products and services. I'll admit that I was and still am skeptical about Amazon's purchase of Whole Foods, but Bezos seems to be up for trying just about anything.
In your analysis of the two leaders, you didn't mention directly the challenges faced by either the leaders or the organization. Last year, Starbucks was all over the news regarding the incident involving two African American gentlemen and how they were treated by a manger at Starbucks. I'm curious how you or others in the class through about how Schultz led the organization through that crisis. Bezos, as well, has not been immune to controversy with his recent affair and divorce becoming public. How do the personal lives and behaviors of leader impact the organizations they lead? Should it matter?
SO
The first leader I chose to research is Sundar Pichai, the CEO of Google. Sundar began to show in interest in technology at an early age, and eventually earned a degree in Metallurgy, and an M.B.A from the Wharton School of the University of Pennsylvania. He then began working at Google in 2004 as the head of product management and development (Shepherd). From there, he assisted in the development of many different departme.
Chosen brand CHANELStudents are required to research a fash.docxtroutmanboris
Chosen brand:
CHANEL
Students are required to research a fashion brand of their choice and analyze its positioning strategy in the market.
● The report will assess students’ ability to collect data, in an efficient manner and use this data to scrutinise the marketing aspects of a fashion brand.
● The report will be covering the following subjects:
1. Analysis Of The Macro And Micro-environment of the brand.
2. Positioning Strategy Of The Brand: Target Customer(Pen Portrait)
3. Competitor Analysis.
4. Critical evaluation of the marketing communications strategy of the brand
supporting the development of the individual report, using relevant PRIMARY and SECONDARY RESEARCH.
NB: Please kindly devise a survey (Google forms) and make up some responses to it so as to then incorporate PRIMARY results into the report. Thanks
see attached file
word count: 2000 words
.
Chose one person to reply to ALBORES 1. Were Manning’s acti.docxtroutmanboris
Chose one person to reply to:
ALBORES
1. Were Manning’s actions legal under the Foreign Corrupt Practices Act, and what are the possible penalties for violating the act?
The Foreign Corrupt Practices Act states (1977) “It shall be unlawful for any issuer...to offer, payment, promise to pay, or authorization of the payment of any money, or offer, gift, promise to give... “. Manning assumed the duty of an issuer because he attended dinner with the prime minister to discuss the contract. Then, Manning offered to fly the prime minister to New York, which he then promised to pay for all of the prime minister's expenses. However, according to the Foreign Corrupt Practices Act (1977) a promise or offer is acceptable if the expense was ”reasonable and bona fide expenditure, such as travel and lodging expenses, incurred by or on behalf of a foreign official… was directly related to the promotion, demonstration, or explanation of products or services”. Manning promised to fly out the prime minister because he wanted to “discuss business further” (UMUC, 2019). Further, Manning used company funds to take the prime minister to luxurious activities and restaurants because he wanted to retain the contract from the prime minister.
Even though Manning did not directly give money to the prime minister, he authorized payment for the prime minster’s two-week stay, which did not involve discussing the contract. Out of the two weeks, business was only conducted for a day. In addition, Manning can be held responsible for bribing the customs officials at Neristan. According to the Foreign Corrupt Practices Act (1977), it is unlawful to influence “any act or decision of such foreign official in his official capacity... omit to do any act in violation of the lawful duty of such official”. Manning influenced the customs officials because Manning gave each custom official $100 to clear the shipment. Custom officials act on behalf of the Neristan government and sometimes require large shipments to be inspected. Manny will likely be held responsible for offering payment to the customs officials in exchange for expediting the company’s shipment.
If Manning violated the Foreign Corrupt Practices Act, he could face imprisonment. Also, the company may have to pay the penalty. The penalty for violating the act is “a fine of up to $2 million per violation. Likewise, an individual may face up to five years in prison and/or a fine of $250,000 per violation of the anti-bribery provision” (Woody, 2018, p. 275).
2. Were Manning’s actions legal under the UK Bribery Act and what are the possible penalties for violating the act?
Based on the UK Bribery Act (2010), an individual is guilty of bribing an official if “intention is to influence F (government official) in F's capacity as a foreign public official...intend to obtain or retain business, or an advantage in the conduct of business.”. Manning bribed the prime minister because he stated: “If, after we are done conducting busi.
Choosing your literary essay topic on Disgrace by J. M. Coetzee .docxtroutmanboris
Choosing your literary essay topic on
Disgrace
by J. M. Coetzee is the first step to writing your literary analysis paper.
After reading the novel, you should be able to decide in which direction you'd like to take your paper.
Topics/ approaches
(Focus on only one of the following, though some may overlap):
Analyze one of the minor characters, such as Petrus.
Example
: Analyze not only the chosen characters' personality but also what role they played in advancing the overall theme of the novel.
The protagonist's conflict, the hurdles to be overcome, and how he resolves it.
Examples:
It could be hope for change, both in South Africa and in David Lurie. OR: the disgrace David Lurie has suffered over the affair with a student and how that matches the disgrace South Africa has suffered through apartheid.
The function of setting to reinforce theme and characterization.
Example
: post-apartheid South Africa is a setting arguably more important than anything else in the novel. Your outside sources would be a bit of history concerning apartheid.The use of literary devices to communicate theme: imagery, metaphor, symbolism, foreshadowing, irony
Symbolism in the novel--
Examples:
Determine if David Lurie represents the old, white authorities of South Africa, while Lucy represents the new white people of South Africa. OR: Analyze what dogs symbolize in this story. Another example: What is symbolized by the opera David Lurie is writing on Byron?
Careful examination of one or more central scenes and its/their crucial role in plot development, resolution of conflict, and exposition of the theme.
Example:
Analyze one or more scenes in which hope that change for the better is possible through a character's remorse and subsequent action, for example, the scene in which David Lurie apologizes to the parents OR the scene in which Lucy gets raped.
The possible issue to be addressed in introduction or conclusion:
Characteristics that make the work typical (or atypical) of the period, the setting, or the author that produced it. For this information, you must go to a library database (you must read "How to Access Miami Dade Databases" if you don't know how) or a valid search site, such as Google Scholar (there is often a fee for this one).
Do
not
open or close with biographical material on the author. Biographical material is important as it influences the author’s writing only and should not be a focus of your paper.
Guidelines for Literary Essay
Be aware that you will be writing about a novel, which in its broadest sense is any extended fictional narrative almost always in prose, in which the representation of character is often the focus. Good authors use the elements of fiction, such as plot, theme, setting etc. purposefully, with a very clear goal in mind. One of the paths to literary analysis is to discover what the author's purpose is with each of his choices. Avoid the problem th.
Choosing your Philosophical Question The Final Project is an opp.docxtroutmanboris
Choosing your Philosophical Question
The Final Project is an opportunity for you to investigate one of the discussion questions to a much greater degree than in the forums. For your Final Project you will choose a philosophical question (stage 1), conduct an analysis of the claims and arguments relevant to the question by reading the primary texts of the philosopher (stage 2), and then take a position on the chosen question and offer an argument in support of your position (stage 3).
For this first stage of your Final Project assignment, (a) choose a question that appears as a discussion question (listed below, with some exceptions). You may choose one that you have previously begun to answer in the discussion forums, or one that you have yet to consider, then (b) explain briefly why you are interested in exploring this philosopher, the primary text and the question further. Submit this assignment on a Word .docx.
Week Four: Philosopher: Thomas Aquinas, Primary Text: Summa Theologica, Part 1, Question 2, Article 1-3
Q1. Does God really exist?
Question to write on, and answer the question fully in all its parts. Be mindful of the question. You are making a claim about something and offering support for it. Try to use examples from the Primary Texts you have read and/or your own experiences in that support.
DISCUSSION QUESTION CHOICE #1: Philosophy of Religion. Study Aquinas' five "ways" of demonstrating God's existence in the learning resources then engage in the study of ontology by examining your belief in God:
Answer the question: Does God really exist?
Use Aquinas and your own reasoning in your argument.
Kreeft, Peter. A Shorter Summa: The Essential Philosophical Passages of St. Thomas Aquinas'
Summa Theologica, Ignatius Press (San Francisco, 1993), chapter II.
Summa Theologica, Part 1, Question 2, Articles 1-3
The Existence of God
Because the chief aim of sacred doctrine is to teach the knowledge of God, not only as He is in
Himself, but also as He is the beginning of things and their last end, and especially of rational
creatures, as is clear from what has been already said, therefore, in our endeavor to expound this
science, we shall treat: (1) Of God; (2) Of the rational creature’s advance towards God; (3) Of
Christ, Who as man, is our way to God.
In treating of God there will be a threefold division: For we shall consider (1) Whatever concerns
the Divine Essence; (2) Whatever concerns the distinctions of Persons; (3) Whatever concerns the
procession of creatures from Him
Concerning the Divine Essence, we must consider: (1) Whether God exists? (2) The manner of His
existence, or, rather, what is not the manner of His existence; (3) Whatever concerns His
operations — namely, His knowledge, will, power.
Concerning the first, there are three points of inquiry: (1) Whether the proposition “God exists” is
self-evident? (2) Whether it is demonstrable? (3) Whether God exists?-
FIRST ARTICLE
Whether the Existence .
Choosing Your Research Method in a NutshellBy James Rice and.docxtroutmanboris
Choosing Your Research Method in a Nutshell
By James Rice and Marilyn K. Simon
Research Method Brief Type
Action research Participatory ‐ problem identification, solution,
solution review
III
Appreciative inquiry Helps groups identify solutions III, IV
Case Study research Group observation to determine how and why a
situation exists
III
Causal‐comparative research Identify causal relationship among variable that
can't be controlled
IV
Content analysis Analyze text and make inferences IV
Correlational research Collect data and determine level of correlation
between variables
I
Critical Incident technique Identification of determining incident of a critical
event
III
Delphi research Analysis of expert knowledge to forecast future
events
I, IV
Descriptive research Study of "as is" phenomena I
Design based research/ decision analysis Identify meaningful change in practices II
Ethnographic Cultural observation of a group
Evaluation research Study the effectiveness of an intervention or
program
IV
Experimental research Study the effect of manipulating a variable or
variables
II
Factor analysis Statistically assess the relationship between large
numbers of variables
I
Grounded Theory Produce a theory that explains a process based on
observation
III, IV
Hermeneutic research Study the meaning of subjects/texts (exegetics is
text only) by concentrating on the historical
meaning of the experience and its developmental
and cumulative effects on the individual and society
III
Historical research historical data collection and analysis of person or
organization
IV
Meta‐analysis research Seek patterns in data collected by other studies and
formulate principals
Narrative research Study of a single person's experiences
Needs assessment Systematic process of determine the needs of a
defined demographic population
Phenomenography Answer questions about thinking and learning
Phenomenology Make sense of lived experiences of participants
regarding a specified phenomenon.
III, IV
Quasi‐experimental Manipulation of variables in populations without
benefit of random assignment or control group.
II
Q‐method A mixed‐method approach to study subjectivity ‐
patterns of thought
I
Regression‐discontinuity design (RD) Cut‐off score assignment of participants to group
(non‐random) used to study effectiveness of an
intervention
II
Repertory grid analysis Interview process to determine how a person
interprets the meaning of an experience
I
Retrospective record review Study of historic data collected about a prior
intervention (both effected and control group)
II
Semiology Studies the meaning of symbols II, III
Situational analysis Post‐modernist approach to grounded theory
(holistic view rather than isolated variables) by
studying lived experiences around a phenomenon
Trend Analysis research Formulate a f.
Choose two of the systems (education, work, the military, and im.docxtroutmanboris
Choose
two
of the systems (education, work, the military, and immigration). Explain how they fit into the domain of social work and the social justice issues social workers should be aware of in these systems.
How does the education, military, workplace, or immigration system rely on social workers?
What is one social justice issue found in education, the military, the workplace, or immigration that influences the practice of social work?
.
Choose two disorders from the categories presented this week.C.docxtroutmanboris
Choose
two disorders from the categories presented this week.
Create
a 15- to 20-slide Microsoft® PowerPoint® presentation that includes the following:
Describes the disorders and explains their differences
Discusses how these disorders are influenced by the legal system
Discusses how the legal system is influenced by these disorders
Include
a minimum of two peer-reviewed sources.
Format
your presentation consistent with APA guidelines.
Submit
your assignment.
*3 slides on How is the legal system influenced by schizophrenia with speaker notes*
.
Choose ONE of the following topics Length 750-900 words, .docxtroutmanboris
Choose
ONE
of the following topics
Length:
750-900 words, double spaced, 12 pt. font
Identify the different forms of religious groups that are comprised in the typology outlined by the classic sociologists of religion. Explain the basic characteristics of each and provide examples.
Establish a distinction between the popular misuses of the term "myth" and its meaning in the scholarly context of Religious Studies. Explain the functions of myth according to the scholar Joseph Campbell.
.
Choose one of the following topicsAmerica A Narrative.docxtroutmanboris
Choose
one
of the following topics
America: A Narrative History
notes Thomas Jefferson's election to the presidency set the tone of "republican simplicity". In what ways was this still true in 1850 following the "Market Revolution" and in what ways was it not?
Connect the technological improvements in water transportation of the early 19th century to the territory acquired in the LA Purchase.
.
Choose one of the following topics below. Comparecont.docxtroutmanboris
Choose
one
of the following topics below.
Compare/contrast the role women played in Puritan Society in colonial Massachusetts with their role in the Great Awakening of the 18th century.
Why is the Declaration of Independence considered historically as a product of the Age of Enlightenment?
500 words
.
Choose one of the following topics below. Comparecon.docxtroutmanboris
Choose
one
of the following topics below.
Compare/contrast the role women played in Puritan Society in colonial Massachusetts with their role in the Great Awakening of the 18th century.
Why is the Declaration of Independence considered historically as a product of the Age of Enlightenment?
requirement of this assignment
Write a 500 word essay
.
Choose one of the states of RacialCultural Identity Development.docxtroutmanboris
Choose one of the states of Racial/Cultural Identity Developmental Model and reflect on how you will intervine with a client in that stage.
Stages:
Conformity
Dissonance and Appreciating
Resistance and immersion
Introspection
Integrative Awareness
.
Choose one of the following topicsNative AmericansWomenEnvi.docxtroutmanboris
Choose
one of the following topics:
Native Americans
Women
Environment
Latin Americans
Sexual liberation
Read
at least three different newspaper articles between 1968 and 1980 that cover important changes affecting your topic. In the University Library, use the ProQuest
®
historical newspaper archive (available under
General Resources > ProQuest >
Advanced Search
>
Search Options
>
Source Type
), which includes the following major newspapers, among others:
New York Times
Washington Post
Wall Street Journal
Los Angeles Times
Christian Science Monitor
Write
a 700- to 1,050-word paper in which you describe the status of the chosen group or idea and how that group or idea was affected by the changes brought about during the 1960s. Include information gleaned from the newspaper articles as well as other material.
.
Choose one of the following films for review (with faculty’s appro.docxtroutmanboris
Choose
one of the following films for review (with faculty’s approval). Put yourself in the movie by choosing one character to follow. What cultural issues would you face? What are cultural challenges? Write a short paper describing the film and your observations. Present your findings in class.
•
Secret Lives of Bees
•
Chocolate
•
Under the Same Moon
•
Maid in Manhattan
•
Walk in the Clouds
•
Get Rich or Die Trying (Gang Culture
) "I like this one"
•
Mu
lan
•
Mississippi Burning
•
A Time to Kill - "
I Also like this one
"
•
Only Fools Rush In
.
Choose and complete one of the two assignment options.docxtroutmanboris
Choose
and
complete
one of the two assignment options:
Option 1: Forecasting Comparison Presentation
Identify
a state, local, or federal policy that impacts your organization or community.
Create
an 8- to 10-slide Microsoft® PowerPoint® presentation in which you complete the following:
Describe how forecasting can be used to implement this policy and highlight any limitations of the usage of forecasting.
Compare and contrast the different forms of forecasting used to aid decision-makers when evaluating policy outcomes.
Discuss the types of information needed to ensure forecasts are accurate.
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1. 4
DDBA 8307 Week 7 Assignment Template
John Doe
DDBA 8307-6
Dr. Jane Doe
1
Two-Way Contingency Table Analysis
Type text here. You will describe and defend using the two-way
contingency table analysis. Use at least two outside resources—
that is, resources not provided in the course resources, readings,
etc. These citations will be presented in the References section.
This exercise will give you practice for addressing Rubric Item
2.13b, which states, “Describes and defends, in detail, the
statistical analyses that the student will conduct….” This
section should be no more than two paragraphs.
Research Question
Type appropriate research question here?
Hypotheses
H0: Type appropriate null hypothesis here.
2. H1: Type appropriate alternative hypothesis here.
Results
Type introduction here.
Descriptive Statistics
Present the descriptive statistics here—use appropriate table and
figures.
Inferential Results
Type the inferential results here.
2
References
Type references here in proper APA format.
Appendix – Two-Way Contingency Table Analysis
SPSS Output
3. BUS 308 Week 2 Lecture 2
Statistical Testing for Differences – Part 1
After reading this lecture, the student should know:
1. How statistical distributions are used in hypothesis testing.
2. How to interpret the F test (both options) produced by Excel
3. How to interpret the T-test produced by Excel
Overview
Lecture 1 introduced the logic of statistical testing using the
hypothesis testing procedure.
It also mentioned that we will be looking at two different tests
this week. The t-test is used to
determine if means differ, from either a standard or claim or
from another group. The F-test is
used to examine variance differences between groups.
This lecture starts by looking at statistical distributions – they
underline the entire
statistical testing approach. They are kind of like the
detective’s base belief that crimes are
4. committed for only a couple of reasons – money, vengeance, or
love. The statistical distribution
that underlies each test assumes that statistical measures (such
as the F value when comparing
variances and the t value when looking at means) follow a
particular pattern, and this can be used
to make decisions.
While the underlying distributions differ for the different tests
we will be looking at
throughout the course, they all have some basic similarities that
allow us to examine the t
distribution and extrapolate from it to interpreting results based
on other distributions.
Distributions. The basic logic for all statistical tests: If the null
hypothesis claim is
correct, then the distribution of the statistical outcome will be
distributed around a central value,
and larger and smaller values will be increasingly rare. At some
point (and we define this as our
alpha value), we can say that the likelihood of getting a
difference this large is extremely
unlikely and we will say that our results do not seem to come
from a population that matches the
claims of the null hypothesis.
Note that this logic has several key elements:
1. The test is based on an assumption that the null hypothesis is
correct. This gives us a
starting point, even if later proven wrong.
2. All sample results are turned into a statistic that matches the
test selected (for
example, the F statistic when using the F-test, or the t-statistic
5. when using the T-test.)
3. The calculated statistic is compared to a related statistical
distribution to see how
likely an outcome we have.
4. The larger the test statistic, the more unlikely it is that the
result matches or comes
from the population described by the null hypothesis claim.
We will demonstrate these ideas by looking at the questions
being asked in this week’s
homework. We will show results of the related Excel tests, and
discuss how to interpret the
output.
We need to remember that seeing different value (mean,
variance, etc.) from different
samples does not tell us that the population parameters we are
estimating are, in fact, different.
The one thing we know about sampling is that each sample will
be a bit different. They
generally provide a “close enough” estimate to the population
values of concern for decision and
action purposes. But, they are not an exact match. This
difference is examined by the use of the
statistical tests, which tell us how much importance we should
attach to observed differences.
Lecture Examples
The lectures for each week will also look at our class question
of whether or not males
and females are paid equally for equal work. These additional
6. analyses provide some different
clues on what the data is trying to tell us about company pay
practices.
While your analysis will focus directly on the salary that males
and females are being
paid, the lecture examples will use an alternate method of
examining pay practices. Many
compensation professionals often use a relative pay measure
called the “comparison-ratio,” or
compa-ratio, to examine pay patterns within the company.
Some background on this measure. Many companies use grades
to group jobs of equal
value to the company into groups that have a similar pay range
– the values that a company is
willing to pay employees for the job. (As strong as a performer
a mail room clerk is, they will
rarely be paid the same as the CEO.) Many companies will set
the middle of this range, the
midpoint, as the average salary that that market pays to hire
someone into the job. This is how
companies remain competitive in their hiring.
Now, compensation professionals will generally want to analyze
how the company is
paying employees relative to these market rates (as summarized
by the midpoint). One approach
is to divide each employee’s salary by its related midpoint. The
outcome is the compa-ratio
which is considered an alternate measure of pay that eliminates
the impact of different grades.
The compa-ratio reports pay as a ratio of the actual salary
divided by the salary grade’s midpoint.
The compa-ratio shows if an employee is being paid more than
7. the midpoint (measure’s
value > 1.0) or less than the midpoint (< 1.0). This measure
allows us to look at salary
dispersion within a company without focusing on the exact
dollar values. It allows a comparison
between what the company is paying and what the outside
market is paying (which most
company’s target as the midpoint of a salary range) for the jobs.
The compa-ratio shows if employees are paid above or below
the grade midpoint and it
can be used to see what the dispersion pattern of pay. Equal
pay would expect to see similar
distributions, variances and means, between males and females
in this measure.
The lecture examples will cover the same statistical tests as the
homework assignments
but will focus on the compa-ratio pay measure rather than
salary. As such, the results presented
each week should be included and/or factored into your weekly
conclusions on what the data has
told us about the answer to our question.
The first step in looking at whether males and females are paid
equally would be to look
at the average pay of each. Given our sample is a random
sample of the population of employees
(and, therefore considered to be representative of the
population), the average salaries or average
compa-ratios (they measure related but not identical measures
of pay) will give us an indication
of whether things are the same for each gender or not.
8. One issue in looking at averages is the variation within the
groups. If both groups have
the same or very similar variation across the salaries then we
test the averages for a difference
using one approach. If the group variances are significantly
different, we use a slightly
approach. So, the first step is to examine group variances. This
is done with the F-test.
F-test
As noted, the F-test is used to compare variances to determine if
the differences noted
could be from simple sampling error (also known as pure chance
alone) or if the differences are
large enough to be considered statistically different. The F
statistic is simply one group’s
variance divided by the other group’s variance. (When done by
hand, it is traditional to have the
largest variance in the numerator, but this is not critical when
Excel performs the test for us.) So,
if the variances are equal, then the result of one variance
divided by the other would equal 1.0 –
this is the center of the F distribution. How about a situation
where one variance equaled 4 and
the other equaled 5 (randomly picked numbers for this
example)? If we divided the larger by the
smaller, we would get 5/4 = 1.25 while if we divided the
smaller by the larger, we would get 0.8.
Note that these values are on each side of the center value of
1.00. This is what is meant by “two
tails” with the F-test – one tail of the distribution has values
less than 1.0 while the other has
values greater than 1.0.. Our value of F depends first on the
variances (of course) and then on
9. how we do the division. The likelihood of these two variances
coming from populations that
have the same variance does not depend upon which tail the
result is in, but rather how likely it is
to see a difference from 1.00. This is given to us by the F-test
p-value (probability value of
seeing a difference as large or larger than what we have if the
null hypothesis is true).
One new concept introduced with the F-test is the idea of
degrees of freedom (df). While
the technical explanation is somewhat tedious, we can
understand the concept with a simple
example. If we have 5 numbers, for example: 1, 2, 3, 4, and 5;
we also have a sum of them; in
this case 15. Now, assume we can change any of the numbers in
the data set with the only
requirement being that the total must remain the same. How
many of the numbers are we free to
change; or what is our degree of freedom in making changes?
In this case, we can change any 4
of the values, as soon as we do so we automatically get the fifth
value (whatever is needed to
make the sum equal to 15). Thus, to generalize, our df is the
count we have minus 1 (equaling n
-1). N-1 is the formula for the degrees of freedom for each
variable in the F-test. We will se this
idea in other statistical tests, each of which has its own formula
to calculate it. The nice thing is
that Excel will give us this outcome without our needing to
worry about it, and we rarely have to
actually use it in any of our work – but, it is technically part of
most statistical tests.
10. There are two versions of the F-test available for use. One is
located in the Data ribbon
under the Analysis block in the Data Analysis link and is called
F-Test Two-Sample for
Variances. The other is located in the Fx Statistical list (which
is duplicated in the Formulas
ribbon under the More Functions option and the Statistical list)
and is called simply F.test.
While both test variances, there is an important difference. The
F-Test Two Sample for
Variances option provides some additional summary statistics
(mean, variance, count) for each
sample, but only provides a one-tail test outcome. One-tail
results, whether with the F-Test or
the T-test are used to test a directional difference in variances,
when we want to know if one
variance is larger (or smaller) than the other. Since, in general
we are interested in the simpler
question of whether the variances are equal or not (without
regard to which is larger), when
using this form to test for equality or not, we need to double the
p-value to find the two t-tail p-
value we need for our decision on rejecting the null hypothesis.
On the other hand, the F.test found in Fx or Formulas returns
only the two-tail p-value;
enough for a decision on rejecting or failing to reject the null
hypothesis of no difference but
nothing else. Technically, this is the version we should use
when conducting our two-tail
questions in the homework, but (as noted) either can be used if
we remember to double the p-
value for the one -tail outcome.
Example: Testing for Variance Equality
11. As mentioned above, it is often beneficial to start with looking
at variance equality when
comparing groups. We need to start our analysis of equal pay
for equal work by seeing if there is
even an issue to be concerned with. So, we have selected our
random sample of 25 males and 25
females from our corporate population. (A couple of
assumptions; the company exists in only
one location, and all our employees in the sample are exempt
professions or managers with at
least a bachelor’s college degree.)
Our initial question is: Are the male and female compa-ratio
variances equal? (Note, if
they are, this would mean that the standard deviations of both
groups are the same.) As with all
statistical tests, we will be using our samples to make
judgements or inferences about the
population values. While the sample result values will differ,
this difference may not be large
enough to show that the population values are not the same.
Question 1. One of the first things of interest to detectives is if
the behavior of the
suspects differs from what they normally do. That is, who’s
behavior varies from the norm?
Relating this to our compa-ratio measures has us asking if the
compa-ratio variance for males
and females are equal within the population. (In the homework,
the question asks about salary
variances. The logic and approach for answering the salary-
based question is the same as shown
below.)
Variance equality is tested using the F-Test. There are two
12. versions of this test available
to us that we could use, and both will be shown below. Note
that equal standard deviations do
not automatically mean that the means are close, it just tells us
if the dispersion patterns are
similar. If similar, the means of each group can be considered
equally reflective of the data. The
following focuses on just setting up the data for and performing
the statistical test.
The following show only the output for the six hypothesis
testing steps. How the Excel
F-tests are set up is covered in Lecture 3 for this week.
Step 1: The question asked is whether the variances for males
and females are equal. The
hypothesis statements for an equality test are shown below.
Ho: Male compa-ratio variance = Female compa-ratio variance
Ha: Male compa-ratio variance =/= Female compa-ratio
variance
(Since the question asks about equality and not a directional
difference, this is a two-tail
test. The Null must contain the names of the two variables
involved (Male and Female),
the statistic being tested (variance), and the relationship sign
(=). The alternate provides
the opposite view so that between them all possible outcomes
are covered. We are only
concerned if the variances are equal, not whether one or the
other is larger (or smaller).)
13. Step 2: We state our decision-making criteria here. It is: Alpha
= 0.05 (This will be the
same for all statistical tests we perform in the class, and
therefore the same in all
hypothesis set-ups.)
Step 3: The test, test statistic, and the reason for selecting the
test are stated here. For this
example, we are using: F statistic and F-test for Variance. We
use these as they are
designed to test variance equality.
Step 4: Our decision-making rule is presented here: Reject the
null hypothesis if the p-
value is < alpha = 0.05.
(This step is also the same for every statistical test we will
perform; it says we will reject
the null hypothesis if the probability of getting a result as large
as what we see is less than
5% or a probability of 0.05.)
Note that these steps are set-up before we even look at the data.
While, we may have set
up the data columns, we should not have done any analysis yet.
These steps tell us how
we will make a decision from the results we get.
Step 5: Perform the analysis. This is the step where Excel
performs the analysis and
produces output tables. The setting up of each Excel test is
covered in Lecture 3, we are
primarily interested in how to interpret the results in this
lecture.
14. Here is a screenshot of the results for both versions of the F-
test. (Only one is needed for
the question.)
Step 6: Conclusions and Interpretation. This is where we
interpret what the data is trying
to tell us.
Before moving on to interpreting these results, let’s look at
what we have. The F-Test
Two-Sample for Variances output clearly has more information
than the F.TEST. We
have the labels identifying each group as well as the mean,
variance, and count
(Observations) for each group. The df, equaling the (sample
size -1), is shown as well as
the calculated F statistic (which equals the left group’s (or Male
in this case) variance
divided by the right group’s (Female) variance. Note, Excel
divides the variances in the
order that they were entered into the data entry box, for this
example the Males were
entered first.
The next two rows are critical for our decision making; but they
are incomplete. They
show the one-tail critical values used in decision making. The
P(F<=f) one-tail is the P-
value, or the probability of getting an F-value as large or larger
than we have if the null
hypothesis is true. However, it is only a one-tail outcome,
while we want a two-tail
outcome, since we only care about the variances being equal or
15. not, not which one is
larger. So, the result as presented cannot be used directly.
What we need to do will be
covered after we look at the F.TEST result.
The F.TEST gives less information, but it provides us with
exactly what we need; the
two-tail p-value. For our data set, we have a 40% (rounded)
chance of getting an F value
this large or larger purely by chance alone when we are looking
at a two-tail outcome.
Note that this value, 0.39766 is twice the one-tail value of
0.19883 from the F-Test table.
This will always be true, the two-tail probabilities will be twice
the one-tail values.
So, if we want to use the F-TEST Two-Sample for Variance
tool, we need to double the
p-value before making our step 6 decisions.
We are now ready to move on to what step 6 asks for. This step
has several parts.
• What is the p-value: 0.3977 (our compa-ratio example result).
This value equals
EITHER tht F.TEST outcome or 2 times the F-test result. (If,
the F-test p-value is
in cell K-15, you could enter =2*K15 to get the value desired).
• What is your decision: REJ or Not reject the null? (If our p-
value is < (less than)
0.05, we say REJ, if the p-value is > (larger than) 0.05, we say
NOT reject. This is
16. what our decision rule says to do.) Our answer is for compa-
ratio variances:
NOT Rej. This means we do not reject the claim made by the
null hypothesis
and accept it as the most likely description of the variances
within the population.
• Why? This line asks us to explain why we made the decision
we did. Our compa-
ratio response is: The p-value is > (greater than) 0.05, and the
decision rule is
reject if the p-value was < 0.05. (The answer here is simply
why, based on the
reasoning shown above, you chose your REJ or NOT Rej
choice.)
• What is your conclusion about the variances in the population
for the male and
female salaries? This part asks us to translate the statistical
decision into a clear
answer to the initial question (Are male and female compa-
values equal?) Our
response: We do not have enough evidence to say that the
variances differ in
the population. The variances are equal in the population. Had
we rejected
the null hypothesis, we would have said the population
variances differed.
Note that this question did not tell us anything about actual pay
differences between the
genders. It did tell us that both groups are dispersed in a
similar manner, and thus supported
some of the conclusions we drew from looking at the data last
week.
17. Examples: Testing for Mean Equality
While we test for variance equality with an F test, we use the T-
Test to test for mean
equality testing. The t-test also uses the degree of freedom (df)
value in providing us with our
probability result; but again, Excel does the work for us. The t
distribution is a bell-shaped curve
that is flatter and a bit more spread out than the normal curve
we discussed last week. The center
is located at 0 (zero) and the tails (the negative and positive
values) are symmetrical.
The t statistic for the testing of two means is basically: (Mean1
– Mean2)/standard error
estimate. (The standard error formula varies according to which
type of t-test we are
performing.) Note that the t will be either positive or negative
depending upon which mean is
larger. So, if we are interested in simply equal or not equal, it
does not matter if we have a
positive or negative t value, only the size of the difference
matters. As with the F-test, half of
our alpha goes in the positive tail and half goes in the negative
tail when making our equality
decision.
We have two questions about means this week.
Question 2. The second question for this week asks about
salary mean equality between males
and females. Again, the set up for this question is covered in
Lecture 3, we are concerned here
with the interpretation of the results. (Note, the comments
18. about each step made above apply to
this example as well, but they will not be repeated except for
specific information related to the t-
test outcome.) Specific differences from the variance example
of question 1 will be highlighted
with italics. Again, the results discussed with each step are
shown in Lecture 2.
Since the question asks if male and female compa-ratios
(salaries in the homework), are equal we
have a equal versus non-equal hypothesis pair.
Step 1: Ho: Male compa-ratio mean = Female compa-ratio mean
Ha: Male compa-ratio mean =/= Female compa-ratio mean
Step 2: The decision criteria is constant: Alpha = 0.05
Step 3: t statistic and t-test, assuming equal variances. We use
these as they are
designed to test mean equality, and we are assuming (and
according to the F-test
have) equal variances.
Step 4: Again, our decision rule is the same: Reject the null
hypothesis if the p-value is
< alpha = 0.05.
Step 5: Perform the analysis. Here is the screen shot for the
results, using the same data
as with Question 1.
As with the F-test output, the t-test starts with the test name, the
19. group names, and some
descriptive statistics. Line 4 start with a new result, the Pooled
Variance; this is a
weighted average of the sample variances since we are assuming
that the related
population variances are equal. The next line, hypothesized
Mean Difference, shows up
only if a value was entered in the data input box setting up the
test (discussed in Lecture
3).
Next comes our friend degrees of freedom, which equal the sum
of both sample sizes
minus 2 (or N1-1 + N2 -1). The calculated T value (similar to
the calculated F value in
the F test output) comes next. Note that since we have a
negative t Stat, it falls in the left
tail of the t distribution. This is important in one tail tests but
not in two tail tests.
Following the calculated T value come the one and two tail
decision points. The one tail
p-value is found in the P(T<==t) one-tail row followed by the T
critical one-tail value.
The two tail results follow.
Step 6: Conclusions and Interpretation. This step has several
parts.
What is the p-value? 0.571 (our rounded compa-ratio example
result).
(Since we again have a two-tail test, we use the P(T<=t) two-
tail result.)
What is your decision: REJ or Not reject the null? NOT Rej
(our compa-ratio result)
20. Why? The p-value (0.571) is > (greater than) 0.05. (The compa-
ratio result)
What is your conclusion about the means in the population for
the male and female
salaries? We do not have enough evidence to say that the means
differ in the
population. So, our conclusion is that the means are equal in
the population. (Our
compa-ratio result.)
Question 3
The third question for this week asks about salary differences
based on educational level
rather than gender. Since education is a legitimate reason to
pay someone more, it will be
helpful to see if a graduate degree results in a higher average
pay. Note that this question has a
directional focus (do employees with an advanced degree
(degree code = 1) have higher average
salaries?). This means we must develop a direction set of
hypothesis statements. We will use
the terms UnderG (for undergraduate degree code 0) and Grad
(for graduate degree code 1) in
these statements. Again, the results discussed with each step
are shown in Lecture 2.
Step 1: Ho: UnderG mean compa-ratio => Grad mean compa-
ratio
Ha: UnderG mean ratio < Grad mean compa-ratio
21. (Note the way the inequalities are set up; since the question is if
degree 1 salary
means >, the question becomes the alternate hypothesis as it
does not contain an =
claim. These can be written with the Grad mean listed first but
the arrow heads
must point to Grad showing an expectation that grad means are
larger.)
Step 2: Alpha = 0.05 (Our constant decision criterion)
Step 3: t statistic and t-test assuming equal variances. We use
these as they are
designed to test mean equality. (The variance equality
assumption is part of the
question set-up.)
Step 4: Reject the null hypothesis if the p-value is < alpha =
0.05. (Our constant
decision rule.)
Step 5: Perform the analysis. Here is a screen print for a T-test
on the question of
whether the graduate and undergraduate degree compa-ratios
means in the population are
equal or not. We are assuming equal variances, we are using
the T-Test Two-Sample
Assuming Equal Variances form.
t-Test: Two-Sample Assuming Equal Variances
UnderG Grad
Mean 1.05172 1.07324
Variance 0.00581 0.005999
Observations 25 25
Pooled Variance 0.005904
22. Hypothesized Mean
Difference 0
df 48
t Stat -0.99016
P(T<=t) one-tail 0.163529
t Critical one-tail 1.677224
P(T<=t) two-tail 0.327059
t Critical two-tail 2.010635
The table output is read exactly the same way as with the
question 2 table with the
exception that we are interested in the one-tail outcome, so we
use the (highlighted) one-
tail p-value row in our decision making.
Step 6: Conclusions and Interpretation. This step has several
parts.
• What is the p-value? 0.164(rounded) (our rounded compa-ratio
example result).
(Since we again have a one-tail test, we use the P(T<=t) one-tail
result.)
• Is the t value in the t-distribution tail indicated by the arrow in
the Ha claim? Yes.
The t-value is negative, and the Ha arrow points to the left (or
negative) tail
of the t distribution. (Since we only care about a difference in
one direction, the
23. result must be consistent with the desired direction. Only large
negative values
are of interest in this case/set-up, since our difference is
calculated by (UnderG –
Grad); large negative values show a larger Grad salary. If we
had said Grad <=
Underg in the Null, the alternate arrow would have pointed to
the right or positive
tail, and a positive t would have been needed.)
• What is your decision: REJ or Not reject the null? NOT Rej
(our compa-ratio
result)
• Why? The p-value is > (greater than) 0.05. So, the sign does
not matter in this
case, but it is in the correct or negative tail. (The compa-ratio
result)
• What is your conclusion about the impact of education on
average salaries? We
do not have enough information to suggest that graduate degree
holders have a
higher average salary than undergraduate degree holders. (Our
compa-ratio
result.)
Question 4
While the week 1 salary results suggest that males and females
are not paid the same, this
week’s compa-ratio tests still do not suggest any inequality.
Gender Compa-ratio variances and
means are not significantly different. A somewhat surprising
result was that graduate degree
holders did not have higher compa-ratios.
24. We still cannot answer our equal pay for equal work question;
however, as we have yet
developed a measure of pay for equal work. Compa-ratios do
remove the impact of grades, but
too many other work-related variables still need to be examined.
Summary
The F and t tests are used to determine if, based upon random
sample results, the
population parameters can reasonably be said to differ. The F-
test looks for differences in
population variances, while the t-test examines population mean
differences. Both tests are
performed as part of the hypothesis testing procedure and
always is done in step 5.
Differences in sample results can be transformed into statistical
distributions that allow us
to determine the probability or likelihood of getting a difference
as large or larger than we found.
It is this transformation that allows us to make our decisions
about the differences we see in the
results.
When either test is set-up using the Data | Analysis toolpak
function, these tests will
provide summary sample descriptive statistics for the mean,
variance, and count as well as the
calculated statistic, the critical value of the statistic, and the p-
value. When set-up using Excel’s
Fx or Formula functions, only the p-value is returned.
25. For both tests, if the appropriate p-value is less than the
specified alpha (always 0.05 in
this class), we reject the null hypothesis and say the alternate is
the more likely description of the
population.
We can test for a simple difference (called a two-tail test) where
it does not matter which
group has the larger value or we can use a directional test
(called a one-tail test) where we are
concerned about which variable is larger (or smaller). The null
and alternate hypothesis define
which difference we are looking for.
The t-test has three versions: equal variances, unequal
variances, and paired. The paired
test is used when we have two measures on each subject (such
as the salary and midpoint for
each employee). The F-test is used to help us decide if we need
to use the equal or unequal
variance form of the t-test.
The Analysis toolpak F test defaults to a one-tail test so we
need to double its p-value
when testing for simple variance differences. The Fx (or
Formula) F-test lets us select a one- or
two-tail outcome.
Please ask your instructor if you have any questions about this
material.
When you have finished with this lecture, please respond to
Discussion Thread 2 for this
week with your initial response and responses to others over a
couple of days.