can i get a quote. i got one from barzzy but she never replied.docx
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can i get a quote. i got one from barzzy but she never replied At this point we know the following about male and female salaries. a. Male and female overall average salaries are not equal in the population. b. Male and female overall average compas are equal in the population, but males are a bit more spread out. c. The male and female salary range are almost the same, as is their age and service. d. Average performance ratings per gender are equal. Let's look at some other factors that might influence pay - education(degree) and performance ratings. 1 Last week, we found that average performance ratings do not differ between males and females in the population. Now we need to see if they differ among the grades. Is the average performace rating the same for all grades? (Assume variances are equal across the grades for this ANOVA.) You can use these columns to place grade Perf Ratings if desired. A B C D E F Null Hypothesis: Alt. Hypothesis: Place B17 in Outcome range box. Interpretation: What is the p-value: Is P-value < 0.05? Do we REJ or Not reject the null? If the null hypothesis was rejected, what is the effect size value (eta squared): Meaning of effect size measure: What does that decision mean in terms of our equal pay question: 2 While it appears that average salaries per each grade differ, we need to test this assumption. Is the average salary the same for each of the grade levels? (Assume equal variance, and use the analysis toolpak function ANOVA.) Use the input table to the right to list salaries under each grade level. Null Hypothesis: If desired, place salaries per grade in these columns Alt. Hypothesis: A B C D E F Place B55 in Outcome range box. What is the p-value: Is P-value < 0.05? Do you reject or not reject the null hypothesis: If the null hypothesis was rejected, what is the effect size value (eta squared): Meaning of effect size measure: Interpretation: 3 The table and analysis below demonstrate a 2-way ANOVA with replication. Please interpret the results. BA MA Ho: Average compas by gender are equal Male 1.017 1.157 Ha: Ave.
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ScoreWeek 3ANOVA and Paired T-test.docx
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Week 3
ANOVA and Paired T-test
At this point we know the following about male and female salaries.
a.
Male and female overall average salaries are not equal in the population.
b.
Male and female overall average compas are equal in the population, but males are a bit more spread out.
c.
The male and female salary range are almost the same, as is their age and service.
d.
Average performance ratings per gender are equal.
Let's look at some other factors that might influence pay - education(degree) and performance ratings.
<1 point>
1
Last week, we found that average performance ratings do not differ between males and females in the population.
Now we need to see if they differ among the grades. Is the average performace rating the same for all grades?
(Assume variances are equal across the grades for this ANOVA.)
You can use these columns to place grade Perf Ratings if desired.
A
B
C
D
E
F
Null Hypothesis:
Alt. Hypothesis:
Place
B17 in Outcome range box.
Interpretation:
What is the p-value:
Is P-value < 0.05?
Do we REJ or Not reject the null?
If
the null hypothesis was rejected, what is the effect size value (eta squared):
Meaning of effect size measure:
What does that decision mean in terms of our equal pay question:
<1 point>
2
While it appears that average salaries per each grade differ, we need to test this assumption.
Is the average salary the same for each of the grade levels? (Assume equal variance, and use the analysis toolpak function ANOVA.)
Use the input table to the right to list salaries under each grade level.
Null Hypothesis:
If desired, place salaries per grade in these columns
Alt. Hypothesis:
A
B
C
D
E
F
.
Chi-square tests are great to show if distributions differ or i.docx
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: ...
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseD.docx
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 .
ScoreWeek 4Confidence Intervals and Chi Square (Chs .docx
Score:
Week 4
Confidence Intervals and Chi Square (Chs 11 - 12)
For questions 3 and 4 below, be sure to list the null and alternate hypothesis statements. Use .05 for your significance level in making your decisions.
For full credit, you need to also show the statistical outcomes - either the Excel test result or the calculations you performed.
<1 point>
1
Using our sample data, construct a 95% confidence interval for the population's mean salary for each gender.
Interpret the results. How do they compare with the findings in the week 2 one sample t-test outcomes (Question 1)?
Mean
St error
t value
Low
to
High
Males
Females
Interpretation:
<1 point>
2
Using our sample data, construct a 95% confidence interval for the mean salary difference between the genders in the population.
How does this compare to the findings in week 2, question 2?
Difference
St Err.
T value
Low
to
High
Yes/No
Can the means be equal?
Why?
How does this compare to the week 2, question 2 result (2 sampe t-test)?
a.
Why is using a two sample tool (t-test, confidence interval) a better choice than using 2 one-sample techniques when comparing two samples?
<1 point>
3
We found last week that the degree values within the population do not impact compa rates.
This does not mean that degrees are distributed evenly across the grades and genders.
Do males and females have athe same distribution of degrees by grade?
(Note: while technically the sample size might not be large enough to perform this test, ignore this limitation for this exercise.)
What are the hypothesis statements:
Ho:
Ha:
Note: You can either use the Excel Chi-related functions or do the calculations manually.
Data input tables - graduate degrees by gender and grade level
OBSERVED
A
B
C
D
E
F
Total
If desired, you can do manual calculations per cell here.
M Grad
A
B
C
D
E
F
Fem Grad
M Grad
Male Und
Fem Grad
Female Und
Male Und
Female Und
Sum =
EXPECTED
M Grad
For this exercise - ignore the requirement for a correction factor
Fem Grad
for cells with expected values less than 5.
Male Und
Female Und
Interpretation:
What is the value of the chi square statistic:
What is the p-value associated with this value:
Is the p-value <0.05?
Do you reject or not reject the null hypothesi.
Ashford 4 - Week 3 - Discussion 1Your initial discussio.docx
Ashford 4: - Week 3 - Discussion 1
Your initial discussion thread is due on Day 3 (Thursday) and you have until Day 7 (Monday) to respond to your classmates. Your grade will reflect both the quality of your initial post and the depth of your responses. Reference the Discussion Forum Grading Rubric for guidance on how your discussion will be evaluated.
ANOVA
In many ways, comparing multiple sample means is simply an extension of what we covered last week. Just as we had 3 versions of the t-test (1 sample, 2 sample (with and without equal variance), and paired; we have several versions of ANOVA – single factor, factorial (called 2-factor with replication in Excel), and within-subjects (2-factor without replication in Excel). What examples (professional, personal, social) can you provide on when we might use each type? What would be the appropriate hypotheses statements for each example?
Guided Response: Review several of your classmates’ posts. Respond to at least two classmates by commenting on why you agree or disagree with the statistical test that your peers have described as appropriate in this scenario.
Ashford 4: - Week 3 - Discussion 2
Your initial discussion thread is due on Day 3 (Thursday) and you have until Day 7 (Monday) to respond to your classmates. Your grade will reflect both the quality of your initial post and the depth of your responses. Reference the Discussion Forum Grading Rubric for guidance on how your discussion will be evaluated.
Effect Size
Several statistical tests have a way to measure effect size. What is this, and when might you want to use it in looking at results from these tests on job related data?
Ashford 4: - Week 3 - Assignment
Problem Set Week Three
Complete the problems included in the resources below and submit your work in an Excel document. Be sure to show all of your work and clearly label all calculations.
All statistical calculations will use the Employee Salary Data Set and the Week 3 assignment sheet.
Carefully review the Grading Rubric for the criteria that will be used to evaluate your assignment.
See comments at the right of the data set.
ID
Salary
Compa
Midpoint
Age
Performance Rating
Service
Gender
Raise
Degree
Gender1
Grade
8
23
1.000
23
32
90
9
1
5.8
0
F
A
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)?
10
22
0.956
23
30
80
7
1
4.7
0
F
A
Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.
11
23
1.000
23
41
100
19
1
4.8
0
F
A
14
24
1.043
23
32
90
12
1
6
0
F
A
The column labels in the table mean:
15
24
1.043
23
32
80
8
1
4.9
0
F
A
ID – Employee sample number
Salary – Salary in thousands
23
23
1.000
23
36
65
6
1
3.3
1
F
A
Age – Age in years
Performance Rating – Appraisal rating (Employee evaluation score)
26
24
1.043
23
22
95
2
1
6.2
1
F
A
Service – Years of service (rounded)
Gender: ...
Week 4Confidence Intervals and Chi Square (Chs 11 - 12).docx
Week 4
Confidence Intervals and Chi Square (Chs 11 - 12)
For questions 3 and 4 below, be sure to list the null and alternate hypothesis statements. Use .05 for your significance level in making your decisions.
For full credit, you need to also show the statistical outcomes - either the Excel test result or the calculations you performed.
1
Using our sample data, construct a 95% confidence interval for the population's mean salary for each gender.
Interpret the results. How do they compare with the findings in the week 2 one sample t-test outcomes (Question 1)?
Mean
St error
t value
Low
to
High
Males
Females
Interpretation:
2
Using our sample data, construct a 95% confidence interval for the mean salary difference between the genders in the population.
How does this compare to the findings in week 2, question 2?
Difference
St Err.
T value
Low
to
High
Yes/No
Can the means be equal?
Why?
How does this compare to the week 2, question 2 result (2 sampe t-test)?
a.
Why is using a two sample tool (t-test, confidence interval) a better choice than using 2 one-sample techniques when comparing two samples?
3
We found last week that the degree values within the population do not impact compa rates.
This does not mean that degrees are distributed evenly across the grades and genders.
Do males and females have athe same distribution of degrees by grade?
(Note: while technically the sample size might not be large enough to perform this test, ignore this limitation for this exercise.)
What are the hypothesis statements:
Ho:
Ha:
Note: You can either use the Excel Chi-related functions or do the calculations manually.
Data input tables - graduate degrees by gender and grade level
OBSERVED
A
B
C
D
E
F
Total
If desired, you can do manual calculations per cell here.
M Grad
A
B
C
D
E
Fem Grad
M Grad
Male Und
Fem Grad
Female Und
Male Und
Female Und
Sum =
EXPECTED
M Grad
For this exercise - ignore the requirement for a correction factor
Fem Grad
for cells with expected values less than 5.
Male Und
Female Und
Interpretation:
What is the value of the chi square statistic:
What is the p-value associated with this value:
Is the p-value <0.05?
Do you reject or not reject the null hypothesis:
If you rejected the null, what is the Cramer's V correlation:
What does this correlation mean?
What does this .
Week 2Testing means - T-testsIn qu.docx
Week 2
Testing means - T-tests
In questions 2 and 3, be sure to include the null and alternate hypotheses you will be testing.
In the first 3 questions use alpha = 0.05 in making your decisions on rejecting or not rejecting the null hypothesis.
1
Below are 2 one-sample t-tests comparing male and female average salaries to the overall sample mean.
(Note: a one-sample t-test in Excel can be performed by selecting the 2-sample unequal variance t-test and making the second variable = Ho value -- see column S)
Based on our sample, how do you interpret the results and what do these results suggest about the population means for male and female average salaries?
Males
Females
Ho: Mean salary = 45
Ho: Mean salary = 45
Ha: Mean salary =/= 45
Ha: Mean salary =/= 45
Note: While the results both below are actually from Excel's t-Test: Two-Sample Assuming Unequal Variances,
having no variance in the Ho variable makes the calculations default to the one-sample t-test outcome - we are tricking Excel into doing a one sample test for us.
Male
Ho
Female
Ho
Mean
52
45
Mean
38
45
Variance
316
0
Variance
334.667
0
Observations
25
25
Observations
25
25
Hypothesized Mean Difference
0
Hypothesized Mean Difference
0
df
24
df
24
t Stat
1.96890383
t Stat
-1.9132
P(T<=t) one-tail
0.03030785
P(T<=t) one-tail
0.03386
t Critical one-tail
1.71088208
t Critical one-tail
1.71088
P(T<=t) two-tail
0.0606157
P(T<=t) two-tail
0.06772
t Critical two-tail
2.06389856
t Critical two-tail
2.0639
Conclusion: Do not reject Ho; mean equals 45
Conclusion: Do not reject Ho; mean equals 45
Is this a 1 or 2 tail test?
Is this a 1 or 2 tail test?
- why?
- why?
P-value is:
P-value is:
Is P-value > 0.05?
Is P-value > 0.05?
Why do we not reject Ho?
Why do we not reject Ho?
Interpretation:
2
Based on our sample data set, perform a 2-sample t-test to see if the population male and female average salaries could be equal to each other.
(Since we have not yet covered testing for variance equality, assume the data sets have statistically equal variances.)
Ho:
Ha:
Test to use:
Place
B43 in Outcome range box.
P-value is:
Is P-value < 0.05?
Reject or do not reject Ho:
If
the null hypothesis was rejected, what is the effect size value:
Meaning of effect size measure:
Interpretation:
b.
Since the one and two sample t-test results provided different outcomes, which is the proper/correct apporach to comparing salary equality?
Why?
3
Based on our sample data set, can the male and female compas in the population be e.
Business Email Rubric Subject Line Subject line clea.docx
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 ...
Recommended
ScoreWeek 3ANOVA and Paired T-test.docx
Score:
Week 3
ANOVA and Paired T-test
At this point we know the following about male and female salaries.
a.
Male and female overall average salaries are not equal in the population.
b.
Male and female overall average compas are equal in the population, but males are a bit more spread out.
c.
The male and female salary range are almost the same, as is their age and service.
d.
Average performance ratings per gender are equal.
Let's look at some other factors that might influence pay - education(degree) and performance ratings.
<1 point>
1
Last week, we found that average performance ratings do not differ between males and females in the population.
Now we need to see if they differ among the grades. Is the average performace rating the same for all grades?
(Assume variances are equal across the grades for this ANOVA.)
You can use these columns to place grade Perf Ratings if desired.
A
B
C
D
E
F
Null Hypothesis:
Alt. Hypothesis:
Place
B17 in Outcome range box.
Interpretation:
What is the p-value:
Is P-value < 0.05?
Do we REJ or Not reject the null?
If
the null hypothesis was rejected, what is the effect size value (eta squared):
Meaning of effect size measure:
What does that decision mean in terms of our equal pay question:
<1 point>
2
While it appears that average salaries per each grade differ, we need to test this assumption.
Is the average salary the same for each of the grade levels? (Assume equal variance, and use the analysis toolpak function ANOVA.)
Use the input table to the right to list salaries under each grade level.
Null Hypothesis:
If desired, place salaries per grade in these columns
Alt. Hypothesis:
A
B
C
D
E
F
.
Chi-square tests are great to show if distributions differ or i.docx
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: ...
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseD.docx
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 .
ScoreWeek 4Confidence Intervals and Chi Square (Chs .docx
Score:
Week 4
Confidence Intervals and Chi Square (Chs 11 - 12)
For questions 3 and 4 below, be sure to list the null and alternate hypothesis statements. Use .05 for your significance level in making your decisions.
For full credit, you need to also show the statistical outcomes - either the Excel test result or the calculations you performed.
<1 point>
1
Using our sample data, construct a 95% confidence interval for the population's mean salary for each gender.
Interpret the results. How do they compare with the findings in the week 2 one sample t-test outcomes (Question 1)?
Mean
St error
t value
Low
to
High
Males
Females
Interpretation:
<1 point>
2
Using our sample data, construct a 95% confidence interval for the mean salary difference between the genders in the population.
How does this compare to the findings in week 2, question 2?
Difference
St Err.
T value
Low
to
High
Yes/No
Can the means be equal?
Why?
How does this compare to the week 2, question 2 result (2 sampe t-test)?
a.
Why is using a two sample tool (t-test, confidence interval) a better choice than using 2 one-sample techniques when comparing two samples?
<1 point>
3
We found last week that the degree values within the population do not impact compa rates.
This does not mean that degrees are distributed evenly across the grades and genders.
Do males and females have athe same distribution of degrees by grade?
(Note: while technically the sample size might not be large enough to perform this test, ignore this limitation for this exercise.)
What are the hypothesis statements:
Ho:
Ha:
Note: You can either use the Excel Chi-related functions or do the calculations manually.
Data input tables - graduate degrees by gender and grade level
OBSERVED
A
B
C
D
E
F
Total
If desired, you can do manual calculations per cell here.
M Grad
A
B
C
D
E
F
Fem Grad
M Grad
Male Und
Fem Grad
Female Und
Male Und
Female Und
Sum =
EXPECTED
M Grad
For this exercise - ignore the requirement for a correction factor
Fem Grad
for cells with expected values less than 5.
Male Und
Female Und
Interpretation:
What is the value of the chi square statistic:
What is the p-value associated with this value:
Is the p-value <0.05?
Do you reject or not reject the null hypothesi.
Ashford 4 - Week 3 - Discussion 1Your initial discussio.docx
Ashford 4: - Week 3 - Discussion 1
Your initial discussion thread is due on Day 3 (Thursday) and you have until Day 7 (Monday) to respond to your classmates. Your grade will reflect both the quality of your initial post and the depth of your responses. Reference the Discussion Forum Grading Rubric for guidance on how your discussion will be evaluated.
ANOVA
In many ways, comparing multiple sample means is simply an extension of what we covered last week. Just as we had 3 versions of the t-test (1 sample, 2 sample (with and without equal variance), and paired; we have several versions of ANOVA – single factor, factorial (called 2-factor with replication in Excel), and within-subjects (2-factor without replication in Excel). What examples (professional, personal, social) can you provide on when we might use each type? What would be the appropriate hypotheses statements for each example?
Guided Response: Review several of your classmates’ posts. Respond to at least two classmates by commenting on why you agree or disagree with the statistical test that your peers have described as appropriate in this scenario.
Ashford 4: - Week 3 - Discussion 2
Your initial discussion thread is due on Day 3 (Thursday) and you have until Day 7 (Monday) to respond to your classmates. Your grade will reflect both the quality of your initial post and the depth of your responses. Reference the Discussion Forum Grading Rubric for guidance on how your discussion will be evaluated.
Effect Size
Several statistical tests have a way to measure effect size. What is this, and when might you want to use it in looking at results from these tests on job related data?
Ashford 4: - Week 3 - Assignment
Problem Set Week Three
Complete the problems included in the resources below and submit your work in an Excel document. Be sure to show all of your work and clearly label all calculations.
All statistical calculations will use the Employee Salary Data Set and the Week 3 assignment sheet.
Carefully review the Grading Rubric for the criteria that will be used to evaluate your assignment.
See comments at the right of the data set.
ID
Salary
Compa
Midpoint
Age
Performance Rating
Service
Gender
Raise
Degree
Gender1
Grade
8
23
1.000
23
32
90
9
1
5.8
0
F
A
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)?
10
22
0.956
23
30
80
7
1
4.7
0
F
A
Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.
11
23
1.000
23
41
100
19
1
4.8
0
F
A
14
24
1.043
23
32
90
12
1
6
0
F
A
The column labels in the table mean:
15
24
1.043
23
32
80
8
1
4.9
0
F
A
ID – Employee sample number
Salary – Salary in thousands
23
23
1.000
23
36
65
6
1
3.3
1
F
A
Age – Age in years
Performance Rating – Appraisal rating (Employee evaluation score)
26
24
1.043
23
22
95
2
1
6.2
1
F
A
Service – Years of service (rounded)
Gender: ...
Week 4Confidence Intervals and Chi Square (Chs 11 - 12).docx
Week 4
Confidence Intervals and Chi Square (Chs 11 - 12)
For questions 3 and 4 below, be sure to list the null and alternate hypothesis statements. Use .05 for your significance level in making your decisions.
For full credit, you need to also show the statistical outcomes - either the Excel test result or the calculations you performed.
1
Using our sample data, construct a 95% confidence interval for the population's mean salary for each gender.
Interpret the results. How do they compare with the findings in the week 2 one sample t-test outcomes (Question 1)?
Mean
St error
t value
Low
to
High
Males
Females
Interpretation:
2
Using our sample data, construct a 95% confidence interval for the mean salary difference between the genders in the population.
How does this compare to the findings in week 2, question 2?
Difference
St Err.
T value
Low
to
High
Yes/No
Can the means be equal?
Why?
How does this compare to the week 2, question 2 result (2 sampe t-test)?
a.
Why is using a two sample tool (t-test, confidence interval) a better choice than using 2 one-sample techniques when comparing two samples?
3
We found last week that the degree values within the population do not impact compa rates.
This does not mean that degrees are distributed evenly across the grades and genders.
Do males and females have athe same distribution of degrees by grade?
(Note: while technically the sample size might not be large enough to perform this test, ignore this limitation for this exercise.)
What are the hypothesis statements:
Ho:
Ha:
Note: You can either use the Excel Chi-related functions or do the calculations manually.
Data input tables - graduate degrees by gender and grade level
OBSERVED
A
B
C
D
E
F
Total
If desired, you can do manual calculations per cell here.
M Grad
A
B
C
D
E
Fem Grad
M Grad
Male Und
Fem Grad
Female Und
Male Und
Female Und
Sum =
EXPECTED
M Grad
For this exercise - ignore the requirement for a correction factor
Fem Grad
for cells with expected values less than 5.
Male Und
Female Und
Interpretation:
What is the value of the chi square statistic:
What is the p-value associated with this value:
Is the p-value <0.05?
Do you reject or not reject the null hypothesis:
If you rejected the null, what is the Cramer's V correlation:
What does this correlation mean?
What does this .
Week 2Testing means - T-testsIn qu.docx
Week 2
Testing means - T-tests
In questions 2 and 3, be sure to include the null and alternate hypotheses you will be testing.
In the first 3 questions use alpha = 0.05 in making your decisions on rejecting or not rejecting the null hypothesis.
1
Below are 2 one-sample t-tests comparing male and female average salaries to the overall sample mean.
(Note: a one-sample t-test in Excel can be performed by selecting the 2-sample unequal variance t-test and making the second variable = Ho value -- see column S)
Based on our sample, how do you interpret the results and what do these results suggest about the population means for male and female average salaries?
Males
Females
Ho: Mean salary = 45
Ho: Mean salary = 45
Ha: Mean salary =/= 45
Ha: Mean salary =/= 45
Note: While the results both below are actually from Excel's t-Test: Two-Sample Assuming Unequal Variances,
having no variance in the Ho variable makes the calculations default to the one-sample t-test outcome - we are tricking Excel into doing a one sample test for us.
Male
Ho
Female
Ho
Mean
52
45
Mean
38
45
Variance
316
0
Variance
334.667
0
Observations
25
25
Observations
25
25
Hypothesized Mean Difference
0
Hypothesized Mean Difference
0
df
24
df
24
t Stat
1.96890383
t Stat
-1.9132
P(T<=t) one-tail
0.03030785
P(T<=t) one-tail
0.03386
t Critical one-tail
1.71088208
t Critical one-tail
1.71088
P(T<=t) two-tail
0.0606157
P(T<=t) two-tail
0.06772
t Critical two-tail
2.06389856
t Critical two-tail
2.0639
Conclusion: Do not reject Ho; mean equals 45
Conclusion: Do not reject Ho; mean equals 45
Is this a 1 or 2 tail test?
Is this a 1 or 2 tail test?
- why?
- why?
P-value is:
P-value is:
Is P-value > 0.05?
Is P-value > 0.05?
Why do we not reject Ho?
Why do we not reject Ho?
Interpretation:
2
Based on our sample data set, perform a 2-sample t-test to see if the population male and female average salaries could be equal to each other.
(Since we have not yet covered testing for variance equality, assume the data sets have statistically equal variances.)
Ho:
Ha:
Test to use:
Place
B43 in Outcome range box.
P-value is:
Is P-value < 0.05?
Reject or do not reject Ho:
If
the null hypothesis was rejected, what is the effect size value:
Meaning of effect size measure:
Interpretation:
b.
Since the one and two sample t-test results provided different outcomes, which is the proper/correct apporach to comparing salary equality?
Why?
3
Based on our sample data set, can the male and female compas in the population be e.
Business Email Rubric Subject Line Subject line clea.docx
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 ...
Final Exam Due Friday, Week EightInstructions Each response is.docx
Final Exam Due Friday, Week Eight
Instructions: Each response is worth a maximum of 50 points. Number and state the question. Space and then give your response. Each response will be a minimum 175 and maximum 225 words. Utilize the book, at least one resource beyond the course book and your personal examples in each response. This means that beyond the course text you should have a minimum of 4 references for this midterm exam. Create appropriate headings/subheading for each response and then give your detailed answer to the question.
Use Word doc. or docx only. Times New Roman, 12 font and double-space everything. Include a cover page and reference page. Return exam via email attachment NLT. Follow APA 6th ed. formatting requirements.
NO LATE EXAMS WILL BE ACCEPTED!
1. In determining rates of pay, how are the decisions made as to who gets paid $7.00 per hour and who will receive $1 million for a years work?
2. How can team effort result in improved organizational profitability?
3. What is reverse discrimination and how does it influence the design of an executive compensation plan?
4. With benefits consuming approximately 40 percent of the compensation dollar of most organizations, among many managers the “fringe benefit” concept still exists. Develop a detailed outline of the various components of a typically benefits program, indicating their importance and scope. Develop the outline so that it can be used for a presentation. (This response length may vary, so ensure you meet the requirements as stated.)
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 midpoint37220.956232295216.21FA42241.0432332100815.70FA3341.096313075513.60FB18361.1613131801115.61FB20341.0963144701614.81FB39351.129312790615.51FB7411.0254032100815.70FC13421.0504030100214.71FC22571.187484865613.80FD24501.041483075913.81FD45551.145483695815.20FD17691.2105727553130FE48651.1405734901115.31FE28751.119674495914.41FF43771.1496742952015.51FF19241.043233285104.61MA25241.0432341704040MA40251.086232490206.30MA2270.87 ...
ScoreWeek 5 Correlation and Regressio.docx
Score:
Week 5
Correlation and Regression
<1 point>
1.
Create a correlation table for the variables in our data set. (Use analysis ToolPak or StatPlus:mac LE function Correlation.)
a.
Reviewing the data levels from week 1, what variables can be used in a Pearson's Correlation table (which is what Excel produces)?
b. Place table here (C8):
c.
Using r = approximately .28 as the signicant r value (at p = 0.05) for a correlation between 50 values, what variables are
significantly related to Salary?
To compa?
d.
Looking at the above correlations - both significant or not - are there any surprises -by that I
mean any relationships you expected to be meaningful and are not and vice-versa?
e.
Does this help us answer our equal pay for equal work question?
<1 point>
2
Below is a regression analysis for salary being predicted/explained by the other variables in our sample (Midpoint,
age, performance rating, service, gender, and degree variables. (Note: since salary and compa are different ways of
expressing an employee’s salary, we do not want to have both used in the same regression.)
Plase interpret the findings.
Ho: The regression equation is not significant.
Ha: The regression equation is significant.
Ho: The regression coefficient for each variable is not significant
Note: technically we have one for each input variable.
Ha: The regression coefficient for each variable is significant
Listing it this way to save space.
Sal
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.9915591
R Square
0.9831894
Adjusted R Square
0.9808437
Standard Error
2.6575926
Observations
50
ANOVA
df
SS
MS
F
Significance F
Regression
6
17762.3
2960.38
419.1516
1.812E-36
Residual
43
303.7003
7.0628
Total
49
18066
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
-1.749621
3.618368
-0.4835
0.631166
-9.046755
5.5475126
-9.04675504
5.54751262
Midpoint
1.2167011
0.031902
38.1383
8.66E-35
1.1523638
1.2810383
1.152363828
1.28103827
Age
-0.004628
0.065197
-0.071
0.943739
-0.136111
0.1268547
-0.13611072
0.1268547
Performace Rating
-0.056596
0.034495
-1.6407
0.108153
-0.126162
0.0129695
-0.12616237
0.01296949
Service
-0.0425
0.084337
-0.5039
0.616879
-0.212582
0.1275814
-0.2125.
DataSalCompaMidAgeEESSERGRaiseDegGen1Gr1581.017573485805.70METhe o.docx
DataSalCompaMidAgeEESSERGRaiseDegGen1Gr1581.017573485805.70METhe 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)? 2270.870315280703.90MBNote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.3341.096313075513.61FB4661.15757421001605.51METhe column labels in the table mean:5470.9794836901605.71MDID – Employee sample number Sal – Salary in thousands 6761.1346736701204.51MFAge – Age in yearsEES – Appraisal rating (Employee evaluation score)7411.0254032100815.71FCSER – Years of serviceG – Gender (0 = male, 1 = female) 8231.000233290915.81FAMid – salary grade midpoint Raise – percent of last raise9771.149674910010041MFGrade – job/pay gradeDeg (0= BS\BA 1 = MS)10220.956233080714.71FAGen1 (Male or Female)Compa - salary divided by midpoint, a measure of salary that removes the impact of grade11231.00023411001914.81FA12601.0525752952204.50METhis data should be treated as a sample of employees taken from a company that has about 1,000 13421.0504030100214.70FCemployees using a random sampling approach.14241.04323329012161FA15241.043233280814.91FA16471.175404490405.70MCMac Users: The homework in this course assumes students have Windows Excel, and17691.2105727553131FEcan load the Analysis ToolPak into their version of Excel.18361.1613131801115.60FBThe analysis tool pak has been removed from Excel for Windows, but a free third-party 19241.043233285104.61MAtool that can be used (found on an answers Microsoft site) is:20341.0963144701614.80FBhttp://www.analystsoft.com/en/products/statplusmacle21761.1346743951306.31MFLike the Microsoft site, I make cannot guarantee the program, but do know that 22571.187484865613.81FDStatplus is a respected statistical package.You may use other approaches or tools23231.000233665613.30FAas desired to complete the assignments.24501.041483075913.80FD25241.0432341704040MA26241.043232295216.20FA27401.000403580703.91MC28751.119674495914.40FF29721.074675295505.40MF30491.0204845901804.30MD31241.043232960413.91FA32280.903312595405.60MB33641.122573590905.51ME34280.903312680204.91MB35241.043232390415.30FA36231.000232775314.30FA37220.956232295216.20FA38560.9825745951104.50ME39351.129312790615.50FB40251.086232490206.30MA41431.075402580504.30MC42241.0432332100815.71FA43771.1496742952015.50FF44601.0525745901605.21ME45551.145483695815.21FD46651.1405739752003.91ME47621.087573795505.51ME48651.1405734901115.31FE49601.0525741952106.60ME50661.1575738801204.60MEhttp://www.analystsoft.com/en/products/statplusmacle
Week 1Week 1.Describing the data.<Use right click on the row numbers at the left to insert rows below each question for your results and comments.>1Using the Excel Analysis ToolPak function descriptive statistics, generate and show the descriptive statistics for each appropriate variable in the sample data set.a. For which variables in the data set does this function not work correctly for? W.
ScoreWeek 2Testing means - T-tests.docx
Score:
Week 2
Testing means - T-tests
Q3
In questions 2 and 3, be sure to include the null and alternate hypotheses you will be testing.
Ho
Female
Male
Female
In the first 3 questions use alpha = 0.05 in making your decisions on rejecting or not rejecting the null hypothesis.
45
34
1.017
1.096
45
41
0.870
1.025
<1 point>
1
Below are 2 one-sample t-tests comparing male and female average salaries to the overall sample mean.
45
23
1.157
1.000
(Note: a one-sample t-test in Excel can be performed by selecting the 2-sample unequal variance t-test and making the second variable = Ho value -- see column S)
45
22
0.979
0.956
Based on our sample, how do you interpret the results and what do these results suggest about the population means for male and female average salaries?
45
23
1.134
1.000
Males
Females
45
42
1.149
1.050
Ho: Mean salary = 45
Ho: Mean salary = 45
45
24
1.052
1.043
Ha: Mean salary =/= 45
Ha: Mean salary =/= 45
45
24
1.175
1.043
45
69
1.043
1.210
Note: While the results both below are actually from Excel's t-Test: Two-Sample Assuming Unequal Variances,
45
36
1.134
1.161
having no variance in the Ho variable makes the calculations default to the one-sample t-test outcome - we are tricking Excel into doing a one sample test for us.
45
34
1.043
1.096
Male
Ho
Female
Ho
45
57
1.000
1.187
Mean
52
45
Mean
38
45
45
23
1.074
1.000
Variance
316
0
Variance
334.6666667
0
45
50
1.020
1.041
Observations
25
25
Observations
25
25
45
24
0.903
1.043
Hypothesized Mean Difference
0
Hypothesized Mean Difference
0
45
75
1.122
1.119
df
24
df
24
45
24
0.903
1.043
t Stat
1.968903827
t Stat
-1.913206357
45
24
0.982
1.043
P(T<=t) one-tail
0.03030785
P(T<=t) one-tail
0.033862118
45
23
1.086
1.000
t Critical one-tail
1.71088208
t Critical one-tail
1.71088208
45
22
1.075
0.956
P(T<=t) two-tail
0.060615701
P(T<=t) two-tail
0.067724237
45
35
1.052
1.129
t Critical two-tail
2.063898562
t Critical two-tail
2.063898562
45
24
1.140
1.043
Conclusion: Do not reject Ho; mean equals 45
Conclusion: Do not reject Ho; mean equals 45
45
77
1.087
1.149
Is this a 1 or 2 tail test?
Is this a 1 or 2 tail test?
- why?
- why?
P-value is:
P-value is:
45
55
1.052
1.145
Is P-value > 0.05?
Is P-value > 0.05?
45
65
1.157
1.140
Why do we not reject Ho?
Why do we not reject Ho?
Interpretation:
<1 point>
2
Based on our sample data set, perform a 2-sample t-test to see if the pop.
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BUSI 620Questions for Critical Thinking 3Salvatore’s Chapter.docx
BUSI 620
Questions for Critical Thinking 3
Salvatore’s Chapter 6:
a. Discussion Questions: 1, 7, and 15.
b. Problems: 7 and appendix problems 1 and 3 (pp. 256–257).
Note:
1. Revised P7: Just construct the diffusion index from month 2 to 3. In this problem, we have three leading indicators. The diffusion index from month 1 to 2 is 66.7 (=2/3) because two indicators move up and move down (see p. 236).
2. Appendix problem 1: Delete “Eliminating the data for 2000.” You need to calculate the moving average forecasts and RMSEs for year 2000, not the whole data period.
3. Appendix problem 3: Compare RMSEs for moving average and exponential forecasts to answer “Is this a better forecast than the moving average” (see also p. 234)? Use 166.63, the mean of all 36 months, as the initial forecast for Jan. 1998 for both exponential smoothing forecasts.
Salvatore’s Chapter 7:
a. Discussion Questions: 3, 11, and 12.
b. Problems: 4, 12, and 13.
Note:
1. P4: Ms. Smith should hire workers as long as their marginal revenue product (MRP) exceeds their marginal resource cost (MRC) and until MRP=MRC.
MRP=MR x MP = P x MP = $10 x MP (use information in the problem to calculate MP). MRC=wages=$40.
2. P12(a): Calculate Q when L=1and K=1, and L=2 and K=2. Then compare and answer the question about the returns to scale.
3. P12(b): Given K=1, show the change in Q if L changes from 1 to 2 and 2 to 3. Answer the question about diminishing returns.
4. P13(a): See figure (7-4) on page 276.
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 midpoint37220.956232295216.21FA42241.0432332100815.70FA3341.096313075513.60FB18361.1613131801115.61FB20341.0963144701614.81FB39351.129312790615.51FB7411.0254032100815.70FC13421.0504030100214.71FC22571.187484865613.80FD24501.041483075913.81FD45551.145483695815.20FD17691.2105727553130FE48651.1405734901115.31FE28751.119674495914.41FF43771.1496742952015.51FF19241.043233285104.61MA25241.0432341704040MA40251.086232490206.30MA2270.870315280703.90MB32280.903312595405.60MB34280.903312680204.91 ...
Week 3 Lecture 9 Effect Size When we reject the null h.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 size measures have different interpretation values than Cohen’s d (from the t-
test). According to Nandy (2012), a small eta squared effect size has a value of 0.01, a medium
of 0.06, and a large value of 0.14 or more. This means we have a large effect size, and the
variables of salary and grade interaction are the most likely cause of our rejecting the null
hypothesis rather than the sample size.
Side note: Eta squared can also be interpreted as the percent of “differences between
group scores that can be explained by the independent variable” (Tanner & Youssef-Morgan,
2013, p. 123). This is consistent with our saying the variable interactions caused the outcome.
Different Forms of ANOVA
Just as the t-test has several forms, so does the ANOVA test. Excel has three versions
available. While we will focus only on the single factor test, a brief description of the other two
versions will be presented.
ANOVA: Two factor without replication
The ANOVA – two factor without replication tests mean differences from two different
variables at the same time. If we are interested in knowing if the mean salary differs by grade
and also by gender, we can perform one two-factor test rather than two separate tests. As
mentioned in lecture two for this week, this is more efficient and maintains our desired alpha
significance level.
Excel Example. To test the mean salaries by grade and gender at the same time, we
would set up our hypothesis test as follows.
Step 1: Ho1: All salary means are equal across grades.
Ha1: At least one mean differs.
Ho2: All gender (male and female) means are equal.
Ha2: At least one mean differs.
Note that in this test, we need to have a hypothesis statement pair for each variable being
tested.
Step 2: ANOVA: Two sample without replication.
Step 3: Reject the null hypothesis if the p-value is < alpha = .05.
Step 4: Perform the test. W.
The exam is composed of two parts vocabulary and short essay. You.docx
The exam is composed of two parts: vocabulary and short essay. Your answers must be typed using standard format 12 inch font, Times New Roman with one inch margins.The maximum points you can earn on this exam are 100 points. For every day late 5 points will be deducted. Good luck!
Part I: Vocabulary (40 points)
Briefly define the following terms used by the philosophers we studied and discussed in class and lecture:
1. Hip Hop’s 5 Elements
2. Sociogeny
3. Nihilism
4. Racial Identity
5. Ethnic Identity
6. Asymmetrical Invisibility
7. Prohibited Actions
8. Permissible Actions
9. Obligatory Actions
10. Supererogatory Actions
11. Classical Racialism
12. Critical Racialism
13. Primary Goods
14. Social Goods
15. Objectification
16. The gaze
17. Gender Performativity
18. Authenticity
19. Binary thinking
20. Eminem Enima
Part II: Short Essay (60 points)
Answer the question below. Do not write a title page or works cited page. Do not go over 3 double-spaced pages. This exercise is asking you to synthesize different views, put them in conversation with each other and draw out implications of the author’s arguments.
1. 1. Do Lil Kim’s raps challenge sexist binaries according to Kathryn Gines and Cheryl Keyes?
You must support your interpretation of how these philosophers respond to this question by providing some direct quotes from their articles with MLA style parenthetical citations. Please quote sparingly and be very selective in the few short quotes you use. No large block quotes that take up a lot of space or a flurry of little quotes that drown out your own words/voice. Also remember to be mindful of drawing out the implications of a quote. Interpret what it means because just citing a piece of textual evidence is not always sufficient. In an explanation you have to briefly unpack the quote so the reader can understand the force of the argument.
Top of Form
Bottom of Form
Sheet1Score:Week 3ANOVA and Paired T-testAt this point we know the following about male and female salaries.a.Male and female overall average salaries are not equal in the population.b.Male and female overall average compas are equal in the population, but males are a bit more spread out.c.The male and female salary range are almost the same, as is their age and service.d. Average performance ratings per gender are equal.Let's look at some other factors that might influence pay - education(degree) and performance ratings.<1 point>1Last week, we found that average performance ratings do not differ between males and females in the population.Now we need to see if they differ among the grades. Is the average performace rating the same for all grades?(Assume variances are equal across the grades for this ANOVA.)You can use these columns to place grade Perf Ratings if desired.ABCDEFNull Hypothesis:Alt. Hypothesis:Place B17 in Outcome range box.Interpretation:What is the p-value:Is P-value < 0.05?Do we REJ or Not reject the null?If the null hypothesis was rejected, what is the ...
1Create a correlation table for the variables in our data set. (Us.docx
1
Create a correlation table for the variables in our data set. (Use analysis ToolPak function Correlation.)
a. Interpret the results.
What variables seem to be important in seeing if we pay males and females equally for equal work?
2
Below is a regression analysis for salary being predicted/explained by the other variables in our sample
(Mid,
age, ees, sr, raise, and deg variables.) (Note: since salary and compa are different ways of
expressing an employee’s salary, we do not want to have both used in the same regression.)
Ho: The regression equation is not significant.
Ha: The regression equation is significant.
Ho: The regression coefficient for each variable is not significant
Ha: The regression coefficient for each variable is significant
Sal
The analysis used Sal as the y (dependent variable) and
SUMMARY OUTPUT
mid, age, ees, sr, g, raise, and deg as the dependent
variables (entered as a range).
Regression Statistics
Multiple R
0.99215498
R Square
0.9843715
Adjusted R Square
0.98176675
Standard Error
2.59277631
Observations
50
ANOVA
df
SS
MS
F
Significance F
Regression
7
17783.7
2540.52
377.914
8.44043E-36
Residual
42
282.345
6.72249
Total
49
18066
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
-4.009
3.775
-1.062
0.294
-11.627
3.609
-11.627
3.609
Mid
1.220
0.030
40.674
0.000
1.159
1.280
1.159
1.280
Age
0.029
0.067
0.439
0.663
-0.105
0.164
-0.105
0.164
EES
-0.096
0.047
-2.020
0.050
-0.191
0.000
-0.191
0.000
SR
-0.074
0.084
-0.876
0.386
-0.244
0.096
-0.244
0.096
G
2.552
0.847
3.012
0.004
0.842
4.261
0.842
4.261
Raise
0.834
0.643
1.299
0.201
-0.462
2.131
-0.462
2.131
Deg
1.002
0.744
1.347
0.185
-0.500
2.504
-0.500
2.504
Interpretation:
Do you reject or not reject the regression null hypothesis?
Do you reject or not reject the null hypothesis for each variable?
What is the regression equation, using only significant variables if any exist?
What does result tell us about equal pay for equal work for males and females?
3
Perform a regression analysis using compa as the dependent variable and the same independent
variables as used in question 2.
Show the result, and interpret your findings by answering the same questions.
Note: be sure to include the appropriate hypothesis statements.
4
Based on all of your results to date, is gender a factor in the pay practices of this company?
Why or why not?
Which is the best variable to use in analyzing pay practices - salary or compa?
Why?
.
Excel Files AssingmentsCopy of Student_Assignment_File.11.01..docx
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 ...
Running head Organization behaviorOrganization behavior 2.docx
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 ...
Chapter 18 – Pricing Setting in the Business WorldThere are few .docx
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.
4DDBA 8307 Week 7 Assignment TemplateJohn DoeD.docx
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.
1NOTE This is a template to help you format Project Part .docx
1
NOTE: This is a template to help you format Project Part A. I have put some explanations in red. Please delete these before submitting the assignment. All text in your submission should be black.
NOTE: This assignment has been changed from what is in the Canvas shell. You are to analyze only two variables instead of 3; and only 2 pairings instead of 3.
ALSO YOU MUST USE THE VARIABLES SPECIFIED IN THIS TEMPLATE.
Project Part A: Descriptive Statistics
By
Put your name here
MATH534 – Applied Managerial Statistics
Prof. Bhupinder Sran
Keller Graduate School of Management
Put date of submission here
1. Introduction
Provide the purpose of this report and an overview of its content. About 2-4 sentences.
2. Variable Name: SALES
2.1 Numerical analysis
Use the table below to display the main statistics. You can add more rows for more statistics IF you want to.
Please don’t put a narrative here.
Mean
Put the mean here
Median
Put the median here
Mode
Put the mode here
Standard deviation
Put the standard deviation here
2.2 Graphical analysis
Display one graph or chart that is appropriate for this data. A histogram, stem and leaf diagram or frequency distribution are good options here. However, you may select your own type.
Please don’t put a narrative here.
2.3 Analysis
Provide a 3-5 sentence analysis pointing out some main findings of the analysis of this variable.
Don’t just restate the numbers. Try to explain how these findings might be useful for management or the organization.
3. Variable Name: CALLS
3.1 Numerical analysis
Use the table below to display the main statistics. You can add more rows for more statistics IF you want to.
Please don’t put a narrative here.
Mean
Put the mean here
Median
Put the median here
Mode
Put the mode here
Standard deviation
Put the standard deviation here
3.2 Graphical analysis
Display one graph or chart that is appropriate for this data. A histogram, stem and leaf diagram or frequency distribution are good options here. However, you may select your own type.
Please don’t put a narrative here.
3.3 Analysis
Provide a 3-5 sentence analysis pointing out some main findings of the analysis of this variable.
Don’t just restate the numbers. Try to explain how these findings might be useful for management or the organization.
4. Variable Pairing: SALES vs CALLS
4.1 Graphical Analysis
Since SALES and CALLS are both numeric, create a scatter plot and place it here.
Please don’t put a narrative here.
4.2 Analysis
Provide a 3-5 sentence analysis pointing out some main findings of the analysis of the relationship between the variables.
5. Variable Pairing: SALES and TYPE
5.1 Graphical Analysis
Display a graph showing the relationship between the two variables. TYPE is not a numeric variable, so you can’t use a scatter plot. Consider using something that displays th.
Can you write these 2 art history questions I uploaded 2 pictures f.docx
Can you write these 2 art history questions? I uploaded 2 pictures for this question
I uploaded two pictures, see the picture files and focus on only this pic that is the topic of this art.
And all answers are should be separated!!
The words should be 80-150 words each section.
No plagiarism!
1. Sarcophagus of Junius Bassus, from Rome, Italy
1
.
Description
-
describe what you see- imagine that the person you are describing it to is not there or is blind.
How will you make the work of art visible through verbal description?
2
.
Summary this text
- summarize
this
text
what i wrote on this question!!
you think are the most important pieces of information that you gleaned from the textbook- read the book closely and put it in your own words.
- Summary this ->
JUNIUS BASSUS SARCOPHAGUS Another pagan convert to Christianity was the city prefect of Rome, Junius Bassus, who, according to the inscription on his sarcophagus (FIG. 8-7), was baptized just before he died in 359. The sarcophagus is of eclectic format—decorated only on three sides in the western Roman manner (see Chapter 7), but divided into two registers of five compartments, each framed by columns in the tradition of Asiatic sarcophagi (FIG. 7-61). In contrast to the Santa Maria Antiqua sarcophagus (FIG. 8-6), the deceased does not appear on the body of the coffin. Instead, stories from the Old and New Testaments fill the niches. Christ has pride of place and appears in the central compartment of each register: as a teacher enthroned between his chief apostles, Saints Peter and Paul (above), and triumphantly entering Jerusalem on a donkey (below). Appropriately, the sculptor placed the scene of Christ’s heavenly triumph above that of his earthly triumph. Both compositions owe a great deal to official Roman art. In the center of the upper zone, Christ, like an enthroned Roman emperor, sits above a personification of the sky god holding a billowing mantle over his head, indicating that Christ is ruler of the universe. The scene below derives from portrayals of Roman emperors entering cities on horseback, but Christ’s steed and the absence of imperial attributes contrast sharply with the imperial models the sculptor used as compositional sources. All of the Old Testament scenes on the Junius Bassus sarcophagus are precursors of New Testament events (see “Jewish Subjects in Christian Art,” page 213). Adam and Eve, for example, are in the second niche from the left on the lower level. Their Original Sin of eating the apple in the Garden of Eden ultimately necessitated Christ’s sacrifice for the salvation of humankind. To the right of the entry into Jerusalem is Daniel, unscathed by flanking lions, saved by his faith. At the upper left, Abraham is about to sacrifice Isaac. Christians believe this Old Testament story was a prefiguration of God’s sacrifice of his own son, Jesus. The Crucifixion itself, however, does not appear on the Junius Bassus sarcophagus. Indeed, th.
Can you write the Annotation 2 for me, please I choose Energy sou.docx
Can you write the Annotation 2 for me, please? I choose Energy source of Children This is my topic.
You have to put work cited first and after that you have to write 150 words summary wath two qouts from the essay and after that you have to write your opinion for gust one to two lines. After that you have to write Two questions about that summery no yes or no. You can choose two questions of How, What,Where, Why, When. this is my order. You have to read this essay I will put to you because you have to read this resoerc. You have to put two of he said "............................................" with page number , and second he said, "......................................." with his last name and page number.
I took these information from my college website and you have to read it.
Works Cited
Verbrugghe, Adronie, et al. "The Glucose and Insulin Response to Isoenergetic Reduction of Dietary Energy Sources in a True Carnivore: The Domestic Cat (Felis Catus)."
The British journal of nutrition
104.2 (2010): 214-21.
ProQuest.
Web. 24 Sep. 2014.
The glucose and insulin response to isoenergetic reduction of dietary energy
sources in a true carnivore: the domestic cat (
Felis catus
)
Adronie Verbrugghe
1
*, Myriam Hesta
1
, Stephanie Van Weyenberg
1
, Georgios A. Papadopoulos
1
,
Kris Gommeren
2
, Sylvie Daminet
2
, Tim Bosmans
2
, Ingeborgh Polis
2
, Johan Buyse
3
and Geert P. J. Janssens
1
1
Laboratory of Animal Nutrition, Faculty of Veterinary Medicine, Ghent University, Heidestraat 19, B-9820 Merelbeke, Belgium
2
Department of Small Animal Medicine, Faculty of Veterinary Medicine, Ghent University, Salisburylaan 133, B-9820 Merelbeke,
Belgium
3
Laboratory of Livestock Physiology, Immunology and Genetics of Domestic Animals, Department of Biosystems, K.U. Leuven,
Kasteelpark Arenberg 30, B-3001 Heverlee, Belgium
(Received 11 August 2009 – Revised 12 January 2010 – Accepted 13 January 2010 – First published online 2 March 2010)
The present study assessed the effect of separate reduction of each energy-delivering nutrient – protein, fat and carbohydrate – on glucose
tolerance and insulin response in a strict carnivore: the domestic cat (
Felis catus
). Three isoenergetic, home-made diets with the following
energetic distribution, low protein (LP): protein 28% of metabolisable energy; fat 43 %; nitrogen-free extract 29 %; low fat: 47, 27 and 25 %;
low carbohydrate (LC): 45, 48 and 7%, were tested in a 3
£
3 Latin square design. Nine healthy normal-weight cats were randomly assigned
to each of the diets in a random order at intervals of 3 weeks. At the end of each testing period, intravenous glucose tolerance tests were performed.
Plasma glucose concentrations and area under the glucose curve showed no differences. Area under the insulin curve was lower when cats were fed
the LP diet, and the second insulin peak tended to be delayed when the LC diet was fed. In contrast to other studies, in which energy.
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Final Exam Due Friday, Week EightInstructions Each response is.docx
Final Exam Due Friday, Week Eight
Instructions: Each response is worth a maximum of 50 points. Number and state the question. Space and then give your response. Each response will be a minimum 175 and maximum 225 words. Utilize the book, at least one resource beyond the course book and your personal examples in each response. This means that beyond the course text you should have a minimum of 4 references for this midterm exam. Create appropriate headings/subheading for each response and then give your detailed answer to the question.
Use Word doc. or docx only. Times New Roman, 12 font and double-space everything. Include a cover page and reference page. Return exam via email attachment NLT. Follow APA 6th ed. formatting requirements.
NO LATE EXAMS WILL BE ACCEPTED!
1. In determining rates of pay, how are the decisions made as to who gets paid $7.00 per hour and who will receive $1 million for a years work?
2. How can team effort result in improved organizational profitability?
3. What is reverse discrimination and how does it influence the design of an executive compensation plan?
4. With benefits consuming approximately 40 percent of the compensation dollar of most organizations, among many managers the “fringe benefit” concept still exists. Develop a detailed outline of the various components of a typically benefits program, indicating their importance and scope. Develop the outline so that it can be used for a presentation. (This response length may vary, so ensure you meet the requirements as stated.)
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 midpoint37220.956232295216.21FA42241.0432332100815.70FA3341.096313075513.60FB18361.1613131801115.61FB20341.0963144701614.81FB39351.129312790615.51FB7411.0254032100815.70FC13421.0504030100214.71FC22571.187484865613.80FD24501.041483075913.81FD45551.145483695815.20FD17691.2105727553130FE48651.1405734901115.31FE28751.119674495914.41FF43771.1496742952015.51FF19241.043233285104.61MA25241.0432341704040MA40251.086232490206.30MA2270.87 ...
ScoreWeek 5 Correlation and Regressio.docx
Score:
Week 5
Correlation and Regression
<1 point>
1.
Create a correlation table for the variables in our data set. (Use analysis ToolPak or StatPlus:mac LE function Correlation.)
a.
Reviewing the data levels from week 1, what variables can be used in a Pearson's Correlation table (which is what Excel produces)?
b. Place table here (C8):
c.
Using r = approximately .28 as the signicant r value (at p = 0.05) for a correlation between 50 values, what variables are
significantly related to Salary?
To compa?
d.
Looking at the above correlations - both significant or not - are there any surprises -by that I
mean any relationships you expected to be meaningful and are not and vice-versa?
e.
Does this help us answer our equal pay for equal work question?
<1 point>
2
Below is a regression analysis for salary being predicted/explained by the other variables in our sample (Midpoint,
age, performance rating, service, gender, and degree variables. (Note: since salary and compa are different ways of
expressing an employee’s salary, we do not want to have both used in the same regression.)
Plase interpret the findings.
Ho: The regression equation is not significant.
Ha: The regression equation is significant.
Ho: The regression coefficient for each variable is not significant
Note: technically we have one for each input variable.
Ha: The regression coefficient for each variable is significant
Listing it this way to save space.
Sal
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.9915591
R Square
0.9831894
Adjusted R Square
0.9808437
Standard Error
2.6575926
Observations
50
ANOVA
df
SS
MS
F
Significance F
Regression
6
17762.3
2960.38
419.1516
1.812E-36
Residual
43
303.7003
7.0628
Total
49
18066
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
-1.749621
3.618368
-0.4835
0.631166
-9.046755
5.5475126
-9.04675504
5.54751262
Midpoint
1.2167011
0.031902
38.1383
8.66E-35
1.1523638
1.2810383
1.152363828
1.28103827
Age
-0.004628
0.065197
-0.071
0.943739
-0.136111
0.1268547
-0.13611072
0.1268547
Performace Rating
-0.056596
0.034495
-1.6407
0.108153
-0.126162
0.0129695
-0.12616237
0.01296949
Service
-0.0425
0.084337
-0.5039
0.616879
-0.212582
0.1275814
-0.2125.
DataSalCompaMidAgeEESSERGRaiseDegGen1Gr1581.017573485805.70METhe o.docx
DataSalCompaMidAgeEESSERGRaiseDegGen1Gr1581.017573485805.70METhe 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)? 2270.870315280703.90MBNote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.3341.096313075513.61FB4661.15757421001605.51METhe column labels in the table mean:5470.9794836901605.71MDID – Employee sample number Sal – Salary in thousands 6761.1346736701204.51MFAge – Age in yearsEES – Appraisal rating (Employee evaluation score)7411.0254032100815.71FCSER – Years of serviceG – Gender (0 = male, 1 = female) 8231.000233290915.81FAMid – salary grade midpoint Raise – percent of last raise9771.149674910010041MFGrade – job/pay gradeDeg (0= BS\BA 1 = MS)10220.956233080714.71FAGen1 (Male or Female)Compa - salary divided by midpoint, a measure of salary that removes the impact of grade11231.00023411001914.81FA12601.0525752952204.50METhis data should be treated as a sample of employees taken from a company that has about 1,000 13421.0504030100214.70FCemployees using a random sampling approach.14241.04323329012161FA15241.043233280814.91FA16471.175404490405.70MCMac Users: The homework in this course assumes students have Windows Excel, and17691.2105727553131FEcan load the Analysis ToolPak into their version of Excel.18361.1613131801115.60FBThe analysis tool pak has been removed from Excel for Windows, but a free third-party 19241.043233285104.61MAtool that can be used (found on an answers Microsoft site) is:20341.0963144701614.80FBhttp://www.analystsoft.com/en/products/statplusmacle21761.1346743951306.31MFLike the Microsoft site, I make cannot guarantee the program, but do know that 22571.187484865613.81FDStatplus is a respected statistical package.You may use other approaches or tools23231.000233665613.30FAas desired to complete the assignments.24501.041483075913.80FD25241.0432341704040MA26241.043232295216.20FA27401.000403580703.91MC28751.119674495914.40FF29721.074675295505.40MF30491.0204845901804.30MD31241.043232960413.91FA32280.903312595405.60MB33641.122573590905.51ME34280.903312680204.91MB35241.043232390415.30FA36231.000232775314.30FA37220.956232295216.20FA38560.9825745951104.50ME39351.129312790615.50FB40251.086232490206.30MA41431.075402580504.30MC42241.0432332100815.71FA43771.1496742952015.50FF44601.0525745901605.21ME45551.145483695815.21FD46651.1405739752003.91ME47621.087573795505.51ME48651.1405734901115.31FE49601.0525741952106.60ME50661.1575738801204.60MEhttp://www.analystsoft.com/en/products/statplusmacle
Week 1Week 1.Describing the data.<Use right click on the row numbers at the left to insert rows below each question for your results and comments.>1Using the Excel Analysis ToolPak function descriptive statistics, generate and show the descriptive statistics for each appropriate variable in the sample data set.a. For which variables in the data set does this function not work correctly for? W.
ScoreWeek 2Testing means - T-tests.docx
Score:
Week 2
Testing means - T-tests
Q3
In questions 2 and 3, be sure to include the null and alternate hypotheses you will be testing.
Ho
Female
Male
Female
In the first 3 questions use alpha = 0.05 in making your decisions on rejecting or not rejecting the null hypothesis.
45
34
1.017
1.096
45
41
0.870
1.025
<1 point>
1
Below are 2 one-sample t-tests comparing male and female average salaries to the overall sample mean.
45
23
1.157
1.000
(Note: a one-sample t-test in Excel can be performed by selecting the 2-sample unequal variance t-test and making the second variable = Ho value -- see column S)
45
22
0.979
0.956
Based on our sample, how do you interpret the results and what do these results suggest about the population means for male and female average salaries?
45
23
1.134
1.000
Males
Females
45
42
1.149
1.050
Ho: Mean salary = 45
Ho: Mean salary = 45
45
24
1.052
1.043
Ha: Mean salary =/= 45
Ha: Mean salary =/= 45
45
24
1.175
1.043
45
69
1.043
1.210
Note: While the results both below are actually from Excel's t-Test: Two-Sample Assuming Unequal Variances,
45
36
1.134
1.161
having no variance in the Ho variable makes the calculations default to the one-sample t-test outcome - we are tricking Excel into doing a one sample test for us.
45
34
1.043
1.096
Male
Ho
Female
Ho
45
57
1.000
1.187
Mean
52
45
Mean
38
45
45
23
1.074
1.000
Variance
316
0
Variance
334.6666667
0
45
50
1.020
1.041
Observations
25
25
Observations
25
25
45
24
0.903
1.043
Hypothesized Mean Difference
0
Hypothesized Mean Difference
0
45
75
1.122
1.119
df
24
df
24
45
24
0.903
1.043
t Stat
1.968903827
t Stat
-1.913206357
45
24
0.982
1.043
P(T<=t) one-tail
0.03030785
P(T<=t) one-tail
0.033862118
45
23
1.086
1.000
t Critical one-tail
1.71088208
t Critical one-tail
1.71088208
45
22
1.075
0.956
P(T<=t) two-tail
0.060615701
P(T<=t) two-tail
0.067724237
45
35
1.052
1.129
t Critical two-tail
2.063898562
t Critical two-tail
2.063898562
45
24
1.140
1.043
Conclusion: Do not reject Ho; mean equals 45
Conclusion: Do not reject Ho; mean equals 45
45
77
1.087
1.149
Is this a 1 or 2 tail test?
Is this a 1 or 2 tail test?
- why?
- why?
P-value is:
P-value is:
45
55
1.052
1.145
Is P-value > 0.05?
Is P-value > 0.05?
45
65
1.157
1.140
Why do we not reject Ho?
Why do we not reject Ho?
Interpretation:
<1 point>
2
Based on our sample data set, perform a 2-sample t-test to see if the pop.
Ash bus 308 week 2 problem set new
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BUSI 620Questions for Critical Thinking 3Salvatore’s Chapter.docx
BUSI 620
Questions for Critical Thinking 3
Salvatore’s Chapter 6:
a. Discussion Questions: 1, 7, and 15.
b. Problems: 7 and appendix problems 1 and 3 (pp. 256–257).
Note:
1. Revised P7: Just construct the diffusion index from month 2 to 3. In this problem, we have three leading indicators. The diffusion index from month 1 to 2 is 66.7 (=2/3) because two indicators move up and move down (see p. 236).
2. Appendix problem 1: Delete “Eliminating the data for 2000.” You need to calculate the moving average forecasts and RMSEs for year 2000, not the whole data period.
3. Appendix problem 3: Compare RMSEs for moving average and exponential forecasts to answer “Is this a better forecast than the moving average” (see also p. 234)? Use 166.63, the mean of all 36 months, as the initial forecast for Jan. 1998 for both exponential smoothing forecasts.
Salvatore’s Chapter 7:
a. Discussion Questions: 3, 11, and 12.
b. Problems: 4, 12, and 13.
Note:
1. P4: Ms. Smith should hire workers as long as their marginal revenue product (MRP) exceeds their marginal resource cost (MRC) and until MRP=MRC.
MRP=MR x MP = P x MP = $10 x MP (use information in the problem to calculate MP). MRC=wages=$40.
2. P12(a): Calculate Q when L=1and K=1, and L=2 and K=2. Then compare and answer the question about the returns to scale.
3. P12(b): Given K=1, show the change in Q if L changes from 1 to 2 and 2 to 3. Answer the question about diminishing returns.
4. P13(a): See figure (7-4) on page 276.
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 midpoint37220.956232295216.21FA42241.0432332100815.70FA3341.096313075513.60FB18361.1613131801115.61FB20341.0963144701614.81FB39351.129312790615.51FB7411.0254032100815.70FC13421.0504030100214.71FC22571.187484865613.80FD24501.041483075913.81FD45551.145483695815.20FD17691.2105727553130FE48651.1405734901115.31FE28751.119674495914.41FF43771.1496742952015.51FF19241.043233285104.61MA25241.0432341704040MA40251.086232490206.30MA2270.870315280703.90MB32280.903312595405.60MB34280.903312680204.91 ...
Week 3 Lecture 9 Effect Size When we reject the null h.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 size measures have different interpretation values than Cohen’s d (from the t-
test). According to Nandy (2012), a small eta squared effect size has a value of 0.01, a medium
of 0.06, and a large value of 0.14 or more. This means we have a large effect size, and the
variables of salary and grade interaction are the most likely cause of our rejecting the null
hypothesis rather than the sample size.
Side note: Eta squared can also be interpreted as the percent of “differences between
group scores that can be explained by the independent variable” (Tanner & Youssef-Morgan,
2013, p. 123). This is consistent with our saying the variable interactions caused the outcome.
Different Forms of ANOVA
Just as the t-test has several forms, so does the ANOVA test. Excel has three versions
available. While we will focus only on the single factor test, a brief description of the other two
versions will be presented.
ANOVA: Two factor without replication
The ANOVA – two factor without replication tests mean differences from two different
variables at the same time. If we are interested in knowing if the mean salary differs by grade
and also by gender, we can perform one two-factor test rather than two separate tests. As
mentioned in lecture two for this week, this is more efficient and maintains our desired alpha
significance level.
Excel Example. To test the mean salaries by grade and gender at the same time, we
would set up our hypothesis test as follows.
Step 1: Ho1: All salary means are equal across grades.
Ha1: At least one mean differs.
Ho2: All gender (male and female) means are equal.
Ha2: At least one mean differs.
Note that in this test, we need to have a hypothesis statement pair for each variable being
tested.
Step 2: ANOVA: Two sample without replication.
Step 3: Reject the null hypothesis if the p-value is < alpha = .05.
Step 4: Perform the test. W.
The exam is composed of two parts vocabulary and short essay. You.docx
The exam is composed of two parts: vocabulary and short essay. Your answers must be typed using standard format 12 inch font, Times New Roman with one inch margins.The maximum points you can earn on this exam are 100 points. For every day late 5 points will be deducted. Good luck!
Part I: Vocabulary (40 points)
Briefly define the following terms used by the philosophers we studied and discussed in class and lecture:
1. Hip Hop’s 5 Elements
2. Sociogeny
3. Nihilism
4. Racial Identity
5. Ethnic Identity
6. Asymmetrical Invisibility
7. Prohibited Actions
8. Permissible Actions
9. Obligatory Actions
10. Supererogatory Actions
11. Classical Racialism
12. Critical Racialism
13. Primary Goods
14. Social Goods
15. Objectification
16. The gaze
17. Gender Performativity
18. Authenticity
19. Binary thinking
20. Eminem Enima
Part II: Short Essay (60 points)
Answer the question below. Do not write a title page or works cited page. Do not go over 3 double-spaced pages. This exercise is asking you to synthesize different views, put them in conversation with each other and draw out implications of the author’s arguments.
1. 1. Do Lil Kim’s raps challenge sexist binaries according to Kathryn Gines and Cheryl Keyes?
You must support your interpretation of how these philosophers respond to this question by providing some direct quotes from their articles with MLA style parenthetical citations. Please quote sparingly and be very selective in the few short quotes you use. No large block quotes that take up a lot of space or a flurry of little quotes that drown out your own words/voice. Also remember to be mindful of drawing out the implications of a quote. Interpret what it means because just citing a piece of textual evidence is not always sufficient. In an explanation you have to briefly unpack the quote so the reader can understand the force of the argument.
Top of Form
Bottom of Form
Sheet1Score:Week 3ANOVA and Paired T-testAt this point we know the following about male and female salaries.a.Male and female overall average salaries are not equal in the population.b.Male and female overall average compas are equal in the population, but males are a bit more spread out.c.The male and female salary range are almost the same, as is their age and service.d. Average performance ratings per gender are equal.Let's look at some other factors that might influence pay - education(degree) and performance ratings.<1 point>1Last week, we found that average performance ratings do not differ between males and females in the population.Now we need to see if they differ among the grades. Is the average performace rating the same for all grades?(Assume variances are equal across the grades for this ANOVA.)You can use these columns to place grade Perf Ratings if desired.ABCDEFNull Hypothesis:Alt. Hypothesis:Place B17 in Outcome range box.Interpretation:What is the p-value:Is P-value < 0.05?Do we REJ or Not reject the null?If the null hypothesis was rejected, what is the ...
1Create a correlation table for the variables in our data set. (Us.docx
1
Create a correlation table for the variables in our data set. (Use analysis ToolPak function Correlation.)
a. Interpret the results.
What variables seem to be important in seeing if we pay males and females equally for equal work?
2
Below is a regression analysis for salary being predicted/explained by the other variables in our sample
(Mid,
age, ees, sr, raise, and deg variables.) (Note: since salary and compa are different ways of
expressing an employee’s salary, we do not want to have both used in the same regression.)
Ho: The regression equation is not significant.
Ha: The regression equation is significant.
Ho: The regression coefficient for each variable is not significant
Ha: The regression coefficient for each variable is significant
Sal
The analysis used Sal as the y (dependent variable) and
SUMMARY OUTPUT
mid, age, ees, sr, g, raise, and deg as the dependent
variables (entered as a range).
Regression Statistics
Multiple R
0.99215498
R Square
0.9843715
Adjusted R Square
0.98176675
Standard Error
2.59277631
Observations
50
ANOVA
df
SS
MS
F
Significance F
Regression
7
17783.7
2540.52
377.914
8.44043E-36
Residual
42
282.345
6.72249
Total
49
18066
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
-4.009
3.775
-1.062
0.294
-11.627
3.609
-11.627
3.609
Mid
1.220
0.030
40.674
0.000
1.159
1.280
1.159
1.280
Age
0.029
0.067
0.439
0.663
-0.105
0.164
-0.105
0.164
EES
-0.096
0.047
-2.020
0.050
-0.191
0.000
-0.191
0.000
SR
-0.074
0.084
-0.876
0.386
-0.244
0.096
-0.244
0.096
G
2.552
0.847
3.012
0.004
0.842
4.261
0.842
4.261
Raise
0.834
0.643
1.299
0.201
-0.462
2.131
-0.462
2.131
Deg
1.002
0.744
1.347
0.185
-0.500
2.504
-0.500
2.504
Interpretation:
Do you reject or not reject the regression null hypothesis?
Do you reject or not reject the null hypothesis for each variable?
What is the regression equation, using only significant variables if any exist?
What does result tell us about equal pay for equal work for males and females?
3
Perform a regression analysis using compa as the dependent variable and the same independent
variables as used in question 2.
Show the result, and interpret your findings by answering the same questions.
Note: be sure to include the appropriate hypothesis statements.
4
Based on all of your results to date, is gender a factor in the pay practices of this company?
Why or why not?
Which is the best variable to use in analyzing pay practices - salary or compa?
Why?
.
Excel Files AssingmentsCopy of Student_Assignment_File.11.01..docx
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 ...
Running head Organization behaviorOrganization behavior 2.docx
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 ...
Chapter 18 – Pricing Setting in the Business WorldThere are few .docx
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.
4DDBA 8307 Week 7 Assignment TemplateJohn DoeD.docx
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.
1NOTE This is a template to help you format Project Part .docx
1
NOTE: This is a template to help you format Project Part A. I have put some explanations in red. Please delete these before submitting the assignment. All text in your submission should be black.
NOTE: This assignment has been changed from what is in the Canvas shell. You are to analyze only two variables instead of 3; and only 2 pairings instead of 3.
ALSO YOU MUST USE THE VARIABLES SPECIFIED IN THIS TEMPLATE.
Project Part A: Descriptive Statistics
By
Put your name here
MATH534 – Applied Managerial Statistics
Prof. Bhupinder Sran
Keller Graduate School of Management
Put date of submission here
1. Introduction
Provide the purpose of this report and an overview of its content. About 2-4 sentences.
2. Variable Name: SALES
2.1 Numerical analysis
Use the table below to display the main statistics. You can add more rows for more statistics IF you want to.
Please don’t put a narrative here.
Mean
Put the mean here
Median
Put the median here
Mode
Put the mode here
Standard deviation
Put the standard deviation here
2.2 Graphical analysis
Display one graph or chart that is appropriate for this data. A histogram, stem and leaf diagram or frequency distribution are good options here. However, you may select your own type.
Please don’t put a narrative here.
2.3 Analysis
Provide a 3-5 sentence analysis pointing out some main findings of the analysis of this variable.
Don’t just restate the numbers. Try to explain how these findings might be useful for management or the organization.
3. Variable Name: CALLS
3.1 Numerical analysis
Use the table below to display the main statistics. You can add more rows for more statistics IF you want to.
Please don’t put a narrative here.
Mean
Put the mean here
Median
Put the median here
Mode
Put the mode here
Standard deviation
Put the standard deviation here
3.2 Graphical analysis
Display one graph or chart that is appropriate for this data. A histogram, stem and leaf diagram or frequency distribution are good options here. However, you may select your own type.
Please don’t put a narrative here.
3.3 Analysis
Provide a 3-5 sentence analysis pointing out some main findings of the analysis of this variable.
Don’t just restate the numbers. Try to explain how these findings might be useful for management or the organization.
4. Variable Pairing: SALES vs CALLS
4.1 Graphical Analysis
Since SALES and CALLS are both numeric, create a scatter plot and place it here.
Please don’t put a narrative here.
4.2 Analysis
Provide a 3-5 sentence analysis pointing out some main findings of the analysis of the relationship between the variables.
5. Variable Pairing: SALES and TYPE
5.1 Graphical Analysis
Display a graph showing the relationship between the two variables. TYPE is not a numeric variable, so you can’t use a scatter plot. Consider using something that displays th.
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Final Exam Due Friday, Week EightInstructions Each response is.docx
Final Exam Due Friday, Week EightInstructions Each response is.docx
DataSalCompaMidAgeEESSERGRaiseDegGen1Gr1581.017573485805.70METhe o.docx
DataSalCompaMidAgeEESSERGRaiseDegGen1Gr1581.017573485805.70METhe o.docx
BUSI 620Questions for Critical Thinking 3Salvatore’s Chapter.docx
BUSI 620Questions for Critical Thinking 3Salvatore’s Chapter.docx
Week 3 Lecture 9 Effect Size When we reject the null h.docx
Week 3 Lecture 9 Effect Size When we reject the null h.docx
The exam is composed of two parts vocabulary and short essay. You.docx
The exam is composed of two parts vocabulary and short essay. You.docx
1Create a correlation table for the variables in our data set. (Us.docx
1Create a correlation table for the variables in our data set. (Us.docx
Excel Files AssingmentsCopy of Student_Assignment_File.11.01..docx
Excel Files AssingmentsCopy of Student_Assignment_File.11.01..docx
Running head Organization behaviorOrganization behavior 2.docx
Running head Organization behaviorOrganization behavior 2.docx
Chapter 18 – Pricing Setting in the Business WorldThere are few .docx
Chapter 18 – Pricing Setting in the Business WorldThere are few .docx
4DDBA 8307 Week 7 Assignment TemplateJohn DoeD.docx
4DDBA 8307 Week 7 Assignment TemplateJohn DoeD.docx
1NOTE This is a template to help you format Project Part .docx
1NOTE This is a template to help you format Project Part .docx
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Can you write these 2 art history questions I uploaded 2 pictures f.docx
Can you write these 2 art history questions? I uploaded 2 pictures for this question
I uploaded two pictures, see the picture files and focus on only this pic that is the topic of this art.
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1. Sarcophagus of Junius Bassus, from Rome, Italy
1
.
Description
-
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JUNIUS BASSUS SARCOPHAGUS Another pagan convert to Christianity was the city prefect of Rome, Junius Bassus, who, according to the inscription on his sarcophagus (FIG. 8-7), was baptized just before he died in 359. The sarcophagus is of eclectic format—decorated only on three sides in the western Roman manner (see Chapter 7), but divided into two registers of five compartments, each framed by columns in the tradition of Asiatic sarcophagi (FIG. 7-61). In contrast to the Santa Maria Antiqua sarcophagus (FIG. 8-6), the deceased does not appear on the body of the coffin. Instead, stories from the Old and New Testaments fill the niches. Christ has pride of place and appears in the central compartment of each register: as a teacher enthroned between his chief apostles, Saints Peter and Paul (above), and triumphantly entering Jerusalem on a donkey (below). Appropriately, the sculptor placed the scene of Christ’s heavenly triumph above that of his earthly triumph. Both compositions owe a great deal to official Roman art. In the center of the upper zone, Christ, like an enthroned Roman emperor, sits above a personification of the sky god holding a billowing mantle over his head, indicating that Christ is ruler of the universe. The scene below derives from portrayals of Roman emperors entering cities on horseback, but Christ’s steed and the absence of imperial attributes contrast sharply with the imperial models the sculptor used as compositional sources. All of the Old Testament scenes on the Junius Bassus sarcophagus are precursors of New Testament events (see “Jewish Subjects in Christian Art,” page 213). Adam and Eve, for example, are in the second niche from the left on the lower level. Their Original Sin of eating the apple in the Garden of Eden ultimately necessitated Christ’s sacrifice for the salvation of humankind. To the right of the entry into Jerusalem is Daniel, unscathed by flanking lions, saved by his faith. At the upper left, Abraham is about to sacrifice Isaac. Christians believe this Old Testament story was a prefiguration of God’s sacrifice of his own son, Jesus. The Crucifixion itself, however, does not appear on the Junius Bassus sarcophagus. Indeed, th.
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Works Cited
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ProQuest.
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Adronie Verbrugghe
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1
1
Laboratory of Animal Nutrition, Faculty of Veterinary Medicine, Ghent University, Heidestraat 19, B-9820 Merelbeke, Belgium
2
Department of Small Animal Medicine, Faculty of Veterinary Medicine, Ghent University, Salisburylaan 133, B-9820 Merelbeke,
Belgium
3
Laboratory of Livestock Physiology, Immunology and Genetics of Domestic Animals, Department of Biosystems, K.U. Leuven,
Kasteelpark Arenberg 30, B-3001 Heverlee, Belgium
(Received 11 August 2009 – Revised 12 January 2010 – Accepted 13 January 2010 – First published online 2 March 2010)
The present study assessed the effect of separate reduction of each energy-delivering nutrient – protein, fat and carbohydrate – on glucose
tolerance and insulin response in a strict carnivore: the domestic cat (
Felis catus
). Three isoenergetic, home-made diets with the following
energetic distribution, low protein (LP): protein 28% of metabolisable energy; fat 43 %; nitrogen-free extract 29 %; low fat: 47, 27 and 25 %;
low carbohydrate (LC): 45, 48 and 7%, were tested in a 3
£
3 Latin square design. Nine healthy normal-weight cats were randomly assigned
to each of the diets in a random order at intervals of 3 weeks. At the end of each testing period, intravenous glucose tolerance tests were performed.
Plasma glucose concentrations and area under the glucose curve showed no differences. Area under the insulin curve was lower when cats were fed
the LP diet, and the second insulin peak tended to be delayed when the LC diet was fed. In contrast to other studies, in which energy.
Can you write the Annotation one for me, please I choose Energy sou.docx
Can you write the Annotation one for me, please? I choose Energy source of Children This is my topic.
You have to put work cited first and after that you have to write 150 words summary and after that you have to write your opinion for gust one to two lines. After that you have to write Two questions about that summery no yes or no. You can choose two questions of How, What,Where, Why, When. this is my order. You have to read this essay I will put to you because you have to read this resoerc.
I took these information from my college website and these are four pages you have to read. You will see the file with work cited and I put some explain to you.
T h e J o u r n a l o f D e v e l o p i n g A r e a s
Volume 48 No. 4 Fall 2014
INTERACTIONS AMONG POVERTY, ACCESS TO MODERN ENERGY SOURCES, AND GENDER IN NIGERIA
Fidelis O. Ogwumike
University of Ibadan, Nigeria
Uche M. Ozughalu
University of Nigeria, Nigeria
ABSTRACT
This study examines the interactions among poverty, access to modern energy sources and gender in Nigeria. Descriptive statistics and logistic regression technique are used. The 2010 Nigeria Living Standard Survey data set obtained from the National Bureau of Statistics is used. The findings of the study show, among other things, that contrary to what happens in many countries of the world, both incidences of poverty and lack of access to modern energy sources are more pronounced among male-headed households than among female-headed households. The logistic regression estimates show, among other things, that poverty reduces the odds in favor of having access to modern energy sources and while female headship of household reduces the odds in favor of being in poverty, it increases the odds in favor of having access to modern energy sources. The forgoing should serve as invaluable guide to the Nigerian government and policy makers.
JEL Classifications:
D10, I32, J16, Q43, R20
Keywords:
Poverty, Modern Energy Sources, Gender, Logit Model, Nigeria
Corresponding Author’s Email Address:
[email protected]
INTRODUCTION
A major socio-economic problem plaguing Nigeria is poverty. In recent times poverty has become pervasive in Nigeria. Associated with the poverty problem in Nigeria is the gross inadequacy in access to modern energy sources. Most Nigerian households use inferior/environmentally harmful sources of energy for cooking and lighting (National Bureau of Statistics, 2005). The use of such inferior sources of energy - like firewood, charcoal, crop residue, animal waste and sawdust - causes environmental pollution which ultimately leads to reduction in health status and productivity.
Both poverty and access to energy sources have gender dimension. Women are said to be more vulnerable to poverty than men. Despite the great role women play in nation-building, they do not receive commensurate economic reward. It is noteworthy that the great role women play in the development process is clearly ref.
Can you write these four art history questions I uploaded a pictu.docx
Can you write these four art history questions? I uploaded a picture for this question
I uploaded a picture, see the picture file and focus on only this pic that is the topic of this art.
The words should be 80-150 words each section. No plagiarism!!
C
offin of Tutankhamen
1
.
Description
-
describe what you see- imagine that the person you are describing it to is not there or is blind.
How will you make the work of art visible through verbal description?
2
.
Summary this text
- summarize this text
what you think are the most important pieces of information that you gleaned from the textbook- read the book closely and put it in your own words.
-summary this.
->TOMB OF TUTANKHAMEN The principal item that Carter found in Tutankhamen’s tomb is the enshrined body of the pharaoh himself. The royal mummy reposed in the innermost of three coffins, nested one within the other. The innermost coffin (FIG. 3-34) was the most luxurious of the three. Made of beaten gold (about a quarter ton of it) and inlaid with semiprecious stones such as lapis lazuli, turquoise, and carnelian, it is a supreme monument to the sculptor’s and goldsmith’s crafts. The portrait mask (FIG. 3-1), which covered the king’s face, is also made of gold with inlaid semiprecious stones. It is a sensitive portrayal of the serene adolescent king dressed in his official regalia, including the nemes headdress and false beard. The general effects of the mask and of the tomb treasures as a whole are of grandeur and richness expressive of Egyptian power, pride, and affluence.
3
.
Research
- go online and find additional information about the image and provide the links for that information for your peers. Make sure to summarize and do not simply cut and paste or give us just the URLs.
4
.
Contemporary Culture
- relate the theme to something that you find in our current visual vernacular on popular culture-
Why is this image relevant today?
.
Can you watch one video about Literary and write a summary about tha.docx
Can you watch one video about Literary and write a summary about that, please? I want 250 words. Can you put the work cited of that video you will watch, please? You can choose any video?
literary events committee and literary Arts club presents
Inky Squib
Event is wheelchair accessible. Accommodation (including interpreative service) is available contact jun white at whiteje butte.edu
Due date is 10/27/2014
.
Can you Help me to create report projectDesign push pull .docx
Can you Help me to create report project:
Design push pull inverter using 28 nm using short channel analog design
specs :
voltage: 1 v
voltage gain : 200
3dB frequency : 2.5 MHZ
the circuit model (Analog push-pull amplifier) is the ONLY BASIC MODEL IN PAGE 13 lof
chapter 5 ( CMOS circuit design ) by philip allen
,,
Then we must need to add (new modified equations )of short channel ID ( saturation and linear case ) , with Change the Vin in each case
To find W/L Scaling
these new equation ( which contains ( Ec L ) in this referance book 2015
CMOS Digital Integrated Circuits (2015) ,S.M.Kang, Y.leblebici
he need from me the report with hand calculation
.
Can you write four these art history questions i uploaded a pictu.docx
Can you write four these art history questions? i uploaded a picture for this question
I uploaded a picture, see the picture file and focus on only this pic that is the topic of this art.
The words should be 80-150 words each section.
TOPIC: Lion Gate,Hattusa (modern Boghazköy), Turkey, ca. 1400 bce.
1.Description- describe what you see- imagine that the person you are describing it to is not there or is blind. How will you make the work of art visible through verbal description?
2. Summary
and paraphrase this paragraph
- summarize what you think are the most important pieces of information that you gleaned from the textbook- read the book closely and put it in your own words.
The Hittites conquered and sacked Babylon around 1595 BCE. Their fortified capital at Boghazköy in Anatolia had seven-foot-tall stone lions guarding the main gateway.
LION GATE, HATTUSA The Babylonian Empire toppled in the face of an onslaught by the Hittites, an Anatolian people who conquered and sacked Babylon around 1595 BCE. They then retired to their homeland, leaving Babylon in the hands of the Kassites. Remains of the strongly fortified capital city of the Hittites still may be seen at Hattusa near modern Boghazköy, Turkey. Constructed of large blocks of heavy stone—a striking contrast to the brick architecture of Mesopotamia—the walls and towers of the Hittites effectively protected them from attack. Symbolically guarding the gateway (FIG. 2-18) to the Hattusa citadel are two huge (seven-foothigh) lions. Their simply carved forequarters project from massive stone blocks on either side of the entrance. These Hittite guardian beasts are early examples of a theme that was to be echoed on many Near Eastern gates. Notable are those of Assyria (FIG. 2-21), one of the greatest empires of the ancient world, and of the reborn Babylon (FIG. 2-24) in the first millennium BCE. But the idea of protecting a city, palace, temple, or tomb from evil by placing wild beasts or fantastic monsters before an entranceway was not unique to the Near Eastern world. Examples abound in Egypt, Greece, Italy, and elsewhere.
3. Research- go online and find additional information about the image and provide the links for that information for your peers. Make sure to summarize and do not simply cut and paste or give us just the URLs.
4. Contemporary Culture- relate the theme to something that you find in our current visual vernacular on popular culture - Why is this image relevant today?
.
Can you please help on this Give some ideasAssignment—Reflecti.docx
Can you please help on this? Give some ideas?
Assignment—Reflection on Diversity
Choose one of the following topics to discuss. Reflect upon your chosen topic in a one- to two-page essay, double-spaced, in APA format. Make sure to include a title page. Based on the topic you choose, describe what the experience was like, how it made you feel and think, and what you learned from it. Connect your discussion to at least three concepts, examples, and/or quotes from the course readings or lectures.
1.
Explore a time when you felt like "other"—when you were made to feel invisible, excluded, or too visible.
2.
Explore a time when you perceived someone or some group as "other" (when you noticed someone or some group was outside or excluded).
3.
Explore a time when a connection was made between you and an "other."
and
Your assignment is to write a brief two- to three-page essay paper, double-spaced, on the importance of understanding cultural, ethnic, and gender differences by managers and professionals in a business setting. Connect your observations and ideas to the materials and readings covered so far in the class. Don't forget to include your experience with the ball and sock experiment in your paper, and be sure to relate the experiment to the importance of understanding diversity and applying this knowledge in the workplace. Please follow APA formatting requirements for this assignment, including a title page, and proofread to be sure that you have no spelling or grammatical errors.
.
Can someone please help me out I had posted this paper over a week .docx
Can someone please help me out? I had posted this paper over a week ago to have done by tonight. an individual had responded, I paid a $15 down payment and they never completed it and I can not seem to find them online any more. I am in desperate need of some help. Please help me. I can not pay anything until Thursday.
For your third Individual Project, you will be writing
an essay of 1,000 words or more about two additional poems from the assigned reading list
. Please select two works from the list of fifteen—excluding the one you already reflected on in Part A of the Phase 3 DB assignment—and answer the questions below. Again, be sure to begin your paper with an engaging introduction and clear thesis statement, develop each point in the body of your paper using examples and quotes from the poems, and conclude your paper with a restatement of your thesis and closing remarks. Also, make sure you maintain your credibility by including in-text citations and a reference list correctly formatted in APA style.
Imagery: What visual images can you identify in both poems? Comment on the details you notice about objects, places, people, colors, textures and so forth. Which of your other senses are stimulated by the poets’ descriptions?
Figures of Speech: List the specific metaphors, similes, puns and other figures of speech each poet uses and how they contribute to the poem’s overall meaning. (Remember, figurative language is not literal but rather suggestive of something else. For example, the metaphor, “Jack is a pig,” is not a reference to an actual animal with hooves but rather someone named Jack who has pig-like qualities or mannerisms.)
Symbolism: Identify the symbols you notice in each poem. What abstract concepts (e.g., love, death, truth) might the concrete objects (e.g., persons, places, things) suggest?
Language and Word Choice: Every word in a poem has been included (or left out) after much deliberation, as poets choose their words very carefully. Remark on the following in each poem: Does the poet use informal or formal language? Does he or she write in an older dialect or contemporary English? Provide examples.
Tone: What tone does each poet take (e.g., sad, humorous, sarcastic, loving, etc.) toward his or her subject matter?
Themes: What are the main messages of both poems? Give reasons for your answers.
Sound: Read both poems aloud. What do you notice about their rhythms, rhyme schemes and musicality? How does listening to the sound of a poem differ from merely reading it as words on a page?
Final Thoughts: Poetry can enlighten and/or evoke deep emotion in readers. Express the impact each of the two poems you have analyzed for this assignment had on you. What insights did you gain about life or human nature, and what feelings did each piece stir in you? Has your view of poetry changed in any way since reading and analyzing the Phase 3 poems? Explain your answer
.
Can someone please do my assignment for me its a Education classCr.docx
Can someone please do my assignment for me its a Education class
Creative Teaching: Designing Culturally Relevant Instruction
For the Final Project, you will assume the role of a classroom teacher applying for a $10,000 Teacher Creativity Fellowship Grant. This award provides financial resources to teachers, which allows the recipient to spend time during the summer months developing an instructional experience that will be implemented and evaluated during the upcoming fall semester. This year’s grant recipients are awarded funding on the condition they develop instructional experiences and programs to support the learning of 21st century skills, specifically, creative and innovative skills.
To further prepare for your Final Project, imagine you teach in a culturally diverse school, and you recognize a need to develop more culturally relevant instructional experiences that promote creative and innovative thinking. After carefully examining the information in the request for proposal for the grant described above, you realize the funding awards are based on the clarity and details of a ten-part form submitted for evaluation. After careful deliberation, you decide to apply for the grant to finance your time and help you obtain the resources needed to develop the proposed instructional experiences. A primary component of your plan requires consideration of the culture and specific needs of each child enrolled in your class (or one of your classes) for the upcoming semester.
Writing the Final Project
Create your Final Project to meet the content and written communication expectations below. The content expectations include the ten parts of the request for proposal. If you have questions about the expectations for this assignment, use the Ask Your Instructor discussion section in the left-navigation pane of your course.
Content Expectations:
Part I: Audience and Rationale (1 point): Write an overview of the class and target population, including the age range, grade and/or subject area, and other relevant attributes of the culture in which school is situated.
Part II: Outcomes (1 point): List the instructional outcomes (i.e., learning objectives) of the instructional experience including (a) Content Outcomes, (b) 21st century skills with an emphasis on creativity and innovation and the International Society for Technology in Education technology skills, and (c) cultural competencies explicitly facilitated within the planned learning experience.
Part III: Assessment (1 point): Describe how you will measure the degree to which the learners have acquired your targeted outcomes. You do not need to develop the assessments, just describe what they would be in some detail.
Part IV. Context Description (2 points): Describe the nature of the overall instructional context established by addressing (a) creativity (i.e., learners involved in making or creating something), (b) problem solving (i.e., overall activity focuses on solving a specific or ill-defin.
C19 07142011 111853 Page 18719CHAPTER NINETEENProjec.docx
C19 07/14/2011 11:18:53 Page 187
19CHAPTER NINETEEN
Project Reviews
Subbu Murthy
T
HE PURPOSE OF PROJECT reviews depends on the project life cycle (see
Figure 19.1). Understanding the purpose of a review is as important as the
review itself. In the early stages of the project, reviews are typically held to
assess the project impact across the portfolio of other projects, evaluate alternatives,
and make decisions to continue the project or abandon them.
In the planning stages, reviews are held to assess the project costs, schedule, and
risks. They are also held to establish the high-level scope and interfaces with other
projects and to evaluate resource allocations. The reviews in the early stage and the
planning phase play a key role in prioritizing and sequencing the project.
In the execution stage, reviews are focused on understanding the project specifica-
tions (requirements, design, etc.), assessing the progress of the project, and assessing
project quality.
Postimplementation reviews are also crucial as they serve to assess overall
performance and review the key lessons learned. They also help understand the true
causes of variance. In a majority of IT projects, poor specifications and scope creep are
the two strong determinants of cost and schedule variances.
Project reviews share four characteristics: (1) They are measurable, (2) they have
specific goals, (3) they deliver direct or indirect benefits to customers or stakeholders,
and (4) they are triggered by a specific milestone or a preestablished schedule.
187
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EBSCO Publishing : eBook Collection (EBSCOhost) - printed on 5/3/2020 12:29 PM via STRAYER UNIVERSITY
AN: 391434 ; Lane, Dean.; The Chief Information Officer's Body of Knowledge : People, Process, and Technology
Account: strayer.main.eds-live
C19 07/14/2011 11:18:53 Page 188
CONCEPTS
Every review must have a purpose, and the purpose must be aligned to the specific
project needs. In addition to identifying the purpose of the review, some of the
fundamental aspects of a project review to consider are the frequency of the review
(weekly versus monthly or quarterly), granularity (detailed versus high level), and
whether reviews are scheduled (weekly, monthly, quarterly) or event driven (such as
completion of a project milestone). The audience for the project review depends on the
purpose of the review. Management, technical architects, developers, quality assurance
personnel, users, finance, and other functions may have to be involved, depending on
the type of review.
While the frequency of review and granularity of reviews depends on .
Calculate the following TEN financial ratiosQuick RatioInvent.docx
Calculate
the following TEN financial ratios:
Quick Ratio
Inventory Turnover
Accounts Receivable Turnover
Total Debt Ratio
Debt to Equity Ratio
Gross Profit Margin
Net Profit Margin
Return on Assets (ROA)
Return on Equity (ROE)
Earnings Per Share (EPS)
Write
a summary of your analysis to include the following for
EACH
ratio:
What does the ratio measure?
How is it calculated?
What is considered a "good" or acceptable figure for the ratio?
What is your calculation for the ratio as it relates to XYZ? Is their result acceptable or not?
.
Calendars are tools that can be assigned to the project and to indiv.docx
Calendars are tools that can be assigned to the project and to individual resources. The calendars can change the length of the workdays and schedule individual vacations. To work with the calendars, we will:
Change the working and non-working time on Standard Calendar.
Change the working and non-working time on a Resource Calendar.
Create new base calendars.
Assign different base calendars to resources.
Guidelines
Complete Lesson 4.
Print the
Project Summary
Report.
Print the
Gantt Chart View
. (Include only those columns up to and including the Finish Date column and the Gantt chart. Print this on two pages wide.)
Print the
Who Does What When
Report. (Print this on three pages.)
Print the
Task
Usage
Report.
Deliverables
All of the following deliverables are required (in a zip file) in order to receive full credit:
Completed MyLab4_XXX.mpp
Completed Review Questions
Printouts/Screenshots contained in Week_4_Printouts_XXX.docx
.
Call to Action Review The Surgeon General’s Vision for a Hea.docx
Call to Action
Review
The Surgeon General’s Vision for a Healthy and Fit Nation 2010
. Then, locate a news article or video related to obesity. Identify the source and evaluate the impact of the report, in relation to the media attention and focus on the topic of obesity. How did the report and media coverage create a sense of urgency to move the audience from complacency to action? In what ways do persuasive arguments and presentations directed toward general audiences create a call to action for reform?
Your initial post should be at least 250 words in length. Support your claims with examples from required material(s) and/or other scholarly sources, and properly cite any references. Respond to at least two of your classmates’ posts by Day 7.
.
By Saturday, November 8, 2014, of Module 2, post in the .docx
By
Saturday, November 8, 2014
, of
Module 2
, post
in the
Discussion Area
at least one current-day advertisement where the
Argument by Authority
fallacy is present. Describe the syllogism that makes this a fallacy. Take care to be certain that you post an
Argument by Authority
fallacy only
.
.
Can anyone do this assignment in 5 hours by 11pm for 40 dollars plea.docx
Can anyone do this assignment in 5 hours by 11pm for 40 dollars please
You are the assistant director of human resources. Janice Marshall has asked you to review the Code of Ethics to determine if John has violated its provisions. You are to prepare a paper that addresses the following:
1. Evaluate International Widgets' Code of Ethics at the conclusion of the You Decide scenario. Do any provisions prohibit John's behavior? Please explain.
2. In addition to a possible ethics violation, could International Widgets pursue other legal avenues against John and/or its competitor? Review your textbook and library references to assist you in answering these questions.
3. Is John in a fiduciary relationship with International Widgets? Why or why not? Is he an agent of International Widgets? Identify the duties and responsibilities of an agent and whether or not John is fulfilling those obligations. Review your textbook and library references to assist you in answering these questions.
4. What action should Gloria take involving John?
00:00
In this scenario, you will evaluate the Code of Ethics to determine if John Anderson has violated International Widgets' Code of Ethics by engaging in business with a competitor.
00:00
The human resources department worked with management to carefully craft a Code of Ethics. All employees received a copy of the code and were required to sign off stating that they had read the code and agreed to abide by its conditions. If an issue concerning an employee occurs, the human resources department investigates. As the assistant director of human resources, you are asked to evaluate an employee's behavior. You meet with Gloria Smithson to obtain all information needed to begin your investigation.
00:00
Gloria has now been in business for 2 years. Her business is called International Widgets, and she employs more than 300 people in the business headquarters. In the early stages of the business, she created a Code of Ethics to govern employees' behavior. Recently, one of her salespeople, John Anderson, seems to be underperforming. Historically, he has been a stellar employee and salesperson, bringing in new clients every month. However, over the last 6 months, not only has he not brought in any new business, but 30% of his accounts have gone to a competitor. Even with this downturn, John was on the road 5 days a week and continued to submit expense reports. Is this ethical?
00:00
Janice is the director of human resources at International Widgets. Recently, Janice met with Gloria to let her know of a rumor in the company that John was actually steering some of his accounts to a competitor because the competitor gave him a kickback.
00:00
John has been employed by International Widgets for 14 months. He likes his job but was recently approached by a competitor with an offer he could not refuse. The competitor wanted to expand his domestic market and asked John to work with him.
CALL TO ACTIONReview The Surgeon General’s Vision for a Healthy an.docx
CALL TO ACTION
Review The Surgeon General’s Vision for a Healthy and Fit Nation 2010. Then, locate a news article or video related to obesity. Identify the source and evaluate the impact of the report, in relation to the media attention and focus on the topic of obesity. How did the report and media coverage create a sense of urgency to move the audience from complacency to action? In what ways do persuasive arguments and presentations directed toward general audiences create a call to action for reform?
Your initial post should be at least 250 words in length. Support your claims with examples from required material(s) and/or other scholarly sources, and properly cite any references.
.
By Saturday, November 1, 2014, respond to the discussion questio.docx
By
Saturday, November 1, 2014
, respond to the discussion question. Submit your responses to the appropriate
Discussion Area
. Use the same
Discussion Area
to comment on your classmates' submissions by
Saturday, November 1, 2014
, and continue the discussion until
Wednesday, November 5, 2014
of the week.
Consider and discuss how the phenomena of prosocial behavior and pure altruism relate to each other and how they differ from each other.
Pure altruism is a specific kind of prosocial behavior where your sole motivation is to help a person in need without seeking benefit for yourself. It is often viewed as a truly selfless form of behavior.
Provide an example each of prosocial behavior and pure altruism.
.
By Tuesday, December 23, 2014, draft a paper that critically ana.docx
By
Tuesday, December 23, 2014
, draft a paper that critically analyzes a current, domestic or foreign political issue of importance to the United States. Students will (1) identify a current political event to analyze; (2) research and summarize the different approaches to understanding the political issue selected; (3) provide a clear, personal analysis of the issue and an explanation of the core values and assumptions that informs their analysis. The student will be expected to support their discussion with primary texts and from pre-approved secondary sources (including, but not limited to the course text, online lectures, and a list of suggested online resources). Your paper should be 6–8 pages in length and include proper APA citation.
.
By Tuesday, July 7, 2015, using the textbook and the South Unive.docx
By
Tuesday, July 7, 2015
, using the textbook and the South University Online Library, write a 1500-word paper using APA standards that focuses on the following: Interview two different individuals regarding their positions in society. Analyze their responses regarding:
Identify each person’s class, race, and gender, supporting your work with the text and/or outside resources.
What role has class, race, and gender played in their lives? How do you see these stratifiers as playing a role, even if the interviewee is unaware of it?
Apply one of the sociological perspectives to the individuals’ lives. Why did you choose this particular perspective? How does it explain each person’s life and life choices?
What are some of the benefits and limitations to using interview as a research methodology?
Analyze each person’s specific components of culture and relate them to his/her stratified position in society.
.
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can i get a quote. i got one from barzzy but she never replied.docx
- 1. can i get a quote. i got one from barzzy but she never replied At this point we know the following about male and female salaries. a. Male and female overall average salaries are not equal in the population.
- 2. b. Male and female overall average compas are equal in the population, but males are a bit more spread out. c. The male and female salary range are almost the same, as is their age and service. d. Average performance ratings per gender are equal.
- 3. Let's look at some other factors that might influence pay - education(degree) and performance ratings. 1 Last week, we found that average performance ratings do not differ between males and females in the population.
- 4. Now we need to see if they differ among the grades. Is the average performace rating the same for all grades? (Assume variances are equal across the grades for this ANOVA.) You can use these columns to place grade Perf Ratings if desired. A B C D E F Null Hypothesis:
- 5. Alt. Hypothesis: Place B17 in Outcome range box.
- 6. Interpretation: What is the p-value:
- 7. Is P-value < 0.05? Do we REJ or Not reject the null?
- 8. If the null hypothesis was rejected, what is the effect size value (eta squared): Meaning of effect size measure:
- 9. What does that decision mean in terms of our equal pay question:
- 10. 2 While it appears that average salaries per each grade differ, we need to test this assumption.
- 11. Is the average salary the same for each of the grade levels? (Assume equal variance, and use the analysis toolpak function ANOVA.) Use the input table to the right to list salaries under each grade level.
- 12. Null Hypothesis: If desired, place salaries per grade in these columns Alt. Hypothesis: A B C
- 13. D E F
- 14. Place B55 in Outcome range box.
- 23. What is the p-value:
- 24. Is P-value < 0.05? Do you reject or not reject the null hypothesis: If
- 25. the null hypothesis was rejected, what is the effect size value (eta squared): Meaning of effect size measure:
- 26. Interpretation:
- 27. 3 The table and analysis below demonstrate a 2-way ANOVA with replication. Please interpret the results.
- 28. BA MA Ho: Average compas by gender are equal Male 1.017 1.157 Ha: Average compas by gender are not equal 0.870 0.979 Ho: Average compas are equal for each degree
- 29. 1.052 1.134 Ha: Average compas are not equal for each degree 1.175 1.149 Ho: Interaction is not significant
- 30. 1.043 1.043 Ha: Interaction is significant 1.074 1.134 1.020 1.000 Perform analysis:
- 31. 0.903 1.122 0.982 0.903 Anova: Two-Factor With Replication
- 43. Sample 0.00225502 1 0.002255 0.383482 0.538939 4.061706 (This is the row variable or gender.) Columns 0.00623352 1 0.006234 1.060054 0.30883 4.061706 (This is the column variable or Degree.) Interaction
- 46. Interpretation:
- 47. For Ho: Average compas by gender are equal Ha: Average compas by gender are not equal What is the p-value:
- 48. Is P-value < 0.05? Do you reject or not reject the null hypothesis: If the null hypothesis was rejected, what is the effect size value (eta squared):
- 49. Meaning of effect size measure:
- 50. For Ho: Average compas are equal for all degrees Ha: Average compas are not equal for all grades What is the p-value:
- 51. Is P-value < 0.05? Do you reject or not reject the null hypothesis: If the null hypothesis was rejected, what is the effect size value
- 52. (eta squared): Meaning of effect size measure:
- 53. For: Ho: Interaction is not significant Ha: Interaction is significant What is the p-value:
- 54. Is P-value < 0.05? Do you reject or not reject the null hypothesis:
- 55. If the null hypothesis was rejected, what is the effect size value (eta squared): Meaning of effect size measure:
- 56. What do these decisions mean in terms of our equal pay question:
- 58. Place data values in these columns 4 Many companies consider the grade midpoint to be the "market rate" - what is needed to hire a new employee. Salary Midpoint Does the company, on average, pay its existing employees at or above the market rate?
- 59. Null Hypothesis:
- 60. Alt. Hypothesis:
- 61. Statistical test to use:
- 62. Place the cursor in B160 for test.
- 70. What is the p-value:
- 71. Is P-value < 0.05? What else needs to be checked on a 1-tail in order to reject the null?
- 72. Do we REJ or Not reject the null? If the null hypothesis was rejected, what is the effect size value: NA Meaning of effect size measure: NA
- 73. Interpretation:
- 74. 5. Using the results up thru this week, what are your conclusions about gender equal pay for equal work at this point?