Module 05 –
Hypothesis Tests Using Two Samples
Class Objectives:
· Identify whether two samples are independent or dependent.
· Compare the testing procedures for two sample tests.
· Test hypothesis about two population parameters.
Module 05 - Part 1
Last week we took one sample to see if it supported our alternative hypothesis. This week we are going to increase to TWO samples and see if there is a significant difference between them.
When would we use this?
· Two samples are __________________________________ if the sample values from one population are not related to or somehow naturally paired or matched with the sample values from the other population.
· Example:
· Two samples are _____________________________ (or consist of ______________________________________) if the sample values are somehow matched, where the matching is based on some inherent relationship.
· Example:
Hint: If the two samples have different sample sizes with no missing data, they must be independent. If the two samples have the same sample size, the samples may or may not be independent.
Put the variables in for each population in the table below.
Population 1
Population 2
Population Mean
Population Standard Deviation
Population Proportion
Sample Size
Sample Mean
Sample Standard Deviation
Sample Proportion
Note: We are going to approach the problem as if are unknown. This is the most common and means that we will be using the t test statistic.
· The test statistic is given by the formula below:
where we assume .
To calculate the degrees of freedom, pick the _______________________ n value and subtract 1.
We will be doing the same steps as before to test the hypothesis (either critical value or p-value test). There are just different formulas.
· The null hypothesis is given as _____________________________.
· The alternative hypothesis will be either ____________________________, ___________________________, or _____________________________.
Example 1. Data Set 26 “Cola Weights and Volumes” in Appendix B includes weights (lb) of the contents of cans of Diet Coke (n = 36, x = 0.78479 lb, s = 0.00439 lb) and of the contents of cans of regular Coke (n = 36, x = 0.81682 lb, s = 0.00751 lb). Use a 0.05 significance level to test the claim that the contents of cans of Diet Coke have weights with a mean that is less than the mean for regular Coke.
Example 2. Researchers from the University of British Columbia conducted trials to investigate the effects of color on creativity. Subjects with a red background were asked to think of creative uses for a brick; other subjects with a blue background were given the same task. Responses were scored by a panel of judges and results from scores of creativity are given below. Higher scores correspond to more creativity. The researchers make the claim that “blue enhances performance on a creative task.” Use a 0.05 significance level to test the claim that blue enhances perform ...
This chapter discusses two-sample hypothesis tests for comparing means and proportions between two independent populations or between paired/dependent samples. It provides examples of hypothesis tests to compare the means of two independent samples using the z-test if populations are normal and sample sizes are large, or the t-test if populations are normal but sample sizes are small. Tests are also shown to compare proportions between two independent populations using the z-test, and to compare means between paired samples using the t-test.
Predicting breast cancer: Adrian VallesAdrián Vallés
Performed and compared predictive modelling approaches (classification tree, logistic regression and random forest) to predict benign vs malignant breast cancers using R for the Data mining class (BANA 4080)
The document analyzes models for predicting loan default using a German credit dataset. It fits generalized linear models, generalized additive models, linear discriminant analysis, and classification trees to the data. Based on out-of-sample testing, the linear discriminant analysis model provided the best results with a minimum misclassification rate of 0.40 and maximum area under the ROC curve of 0.867. However, the performance of all the models was quite similar.
The document discusses different types of two-sample hypothesis tests, including tests comparing two population means of independent samples, two population proportions, and paired or dependent samples. It provides examples and step-by-step explanations of how to conduct two-sample t-tests, z-tests, and tests of proportions. Key points covered include determining the appropriate test statistic based on sample size and characteristics, stating the null and alternative hypotheses, test criteria, and decisions rules.
The following calendar-year information is taken from the December.docxcherry686017
The following calendar-year information is taken from the December 31, 2011, adjusted trial balance and other records of Azalea Company.
1. Each team member is to be responsible for computing one of the following amounts. You are not to duplicate your teammates' work. Get any necessary amounts from teammates. Each member is to explain the computation to the team in preparation for reporting to class.
a. Materials used.
b. Factory overhead.
c. Total manufacturing costs.
d. Total cost of goods in process.
e. Cost of goods manufactured.
2. Check your cost of goods manufactured with the instructor. If it is correct, proceed to part (3).
3. Each team member is to be responsible for computing one of the following amounts. You are not to duplicate your teammates' work. Get any necessary amounts from teammates. Each member is to explain the computation to the team in preparation for reporting to class.
a. Net sales.
b. Cost of goods sold.
c. Gross profit.
d. Total operating expenses.
e. Net income or loss before taxes.
CALCULATE T TEST
Calculate the “t” value for independent groups for the following data using the formula provided in the attached word document. Using the raw measurement data presented, determine whether or not there exists a statistically significant difference between the salaries of female and male human resource managers using the appropriate t-test. Develop a testable hypothesis, confidence level, and degrees of freedom. Report the required “t” critical values based on the degrees of freedom. Show calculations.
Answer
The null hypothesis tested is
H0: There is no significant difference between the average salaries of female and male human resource managers. (µ1= µ2)
The alternative hypothesis is
H1: There is significant difference between the average salaries of female and male human resource managers. (µ1≠ µ2)
The test statistic used is
12
12
2
~
NN
DM
MM
tt
S
+-
-
=
Where
22
1122
1212
(1)(1)
11
2
DM
NsNs
S
NNNN
éùéù
-+-
=+
êúêú
+-
ëûëû
Here M1 = 62,200, M2 = 63,700
s1 = 9330.95, s2 = 6912.95
N1 = 10, N2 = 10 (See the excel sheet)
Then,
(
)
(
)
22
(101)9330.95(101)6912.95
11
101021010
DM
S
éù
-+-
éù
=+
êú
êú
+-
ëû
êú
ëû
= 3672.267768
Therefore test statistic,
62,20063,700
3672.267768
t
-
=
= -0.408466946
Degrees of freedom = N1 + N2 – 2 = 10 + 10 – 2 = 18
Let the significance level be 0.05.
Rejection criteria: Reject the null hypothesis, if the calculated value of t is greater than the critical value of t at 0.05 significance level.
The critical values can be obtained from the student’s t tables with 18 d.f. at 0.05 significance level.
Upper critical value = 2.1
Lower critical value = -2.1
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Conclusion: Fails to reject the null hypothesis. The sample does not provide enough evidence to support the claim that there is significant difference ...
This document discusses hypothesis testing for two populations. It covers testing whether two independent population means or proportions are equal using z-tests or t-tests. It also addresses testing whether the mean difference between paired observations is equal to zero using a t-test. Examples are provided for each test. The learning objectives are to understand how to perform these different types of two-sample hypothesis tests using the appropriate test statistic and test assumptions.
Chemometrics-ANALYTICAL DATA SIGNIFICANCE TESTS.pptxHakimuNsubuga2
Significance tests are used to determine if differences between samples are statistically significant or could be attributed to random error. A significance test compares a test statistic to a critical value to evaluate the null hypothesis, which states there is no real difference between populations. Common tests include Student's t-test to compare means, paired t-tests for methods testing the same samples, and F-tests to check if variances are significantly different before pooling estimates. One-tailed tests check for differences in one direction, while two-tailed tests consider differences in either direction. Significance tests are widely applied to evaluate experimental results.
This document discusses different types of t-tests used to compare means: one sample t-tests, independent samples t-tests, and paired samples t-tests. It provides examples and steps for conducting each type of t-test in SPSS. Key points include that one sample t-tests compare a sample mean to a known value, independent samples t-tests compare means between two unrelated groups, and paired samples t-tests compare means within the same group across two time points or conditions. The document also outlines assumptions, how to interpret output and p-values, and how to report results for each t-test. Three cases are presented to demonstrate application of each t-test type.
This chapter discusses two-sample hypothesis tests for comparing means and proportions between two independent populations or between paired/dependent samples. It provides examples of hypothesis tests to compare the means of two independent samples using the z-test if populations are normal and sample sizes are large, or the t-test if populations are normal but sample sizes are small. Tests are also shown to compare proportions between two independent populations using the z-test, and to compare means between paired samples using the t-test.
Predicting breast cancer: Adrian VallesAdrián Vallés
Performed and compared predictive modelling approaches (classification tree, logistic regression and random forest) to predict benign vs malignant breast cancers using R for the Data mining class (BANA 4080)
The document analyzes models for predicting loan default using a German credit dataset. It fits generalized linear models, generalized additive models, linear discriminant analysis, and classification trees to the data. Based on out-of-sample testing, the linear discriminant analysis model provided the best results with a minimum misclassification rate of 0.40 and maximum area under the ROC curve of 0.867. However, the performance of all the models was quite similar.
The document discusses different types of two-sample hypothesis tests, including tests comparing two population means of independent samples, two population proportions, and paired or dependent samples. It provides examples and step-by-step explanations of how to conduct two-sample t-tests, z-tests, and tests of proportions. Key points covered include determining the appropriate test statistic based on sample size and characteristics, stating the null and alternative hypotheses, test criteria, and decisions rules.
The following calendar-year information is taken from the December.docxcherry686017
The following calendar-year information is taken from the December 31, 2011, adjusted trial balance and other records of Azalea Company.
1. Each team member is to be responsible for computing one of the following amounts. You are not to duplicate your teammates' work. Get any necessary amounts from teammates. Each member is to explain the computation to the team in preparation for reporting to class.
a. Materials used.
b. Factory overhead.
c. Total manufacturing costs.
d. Total cost of goods in process.
e. Cost of goods manufactured.
2. Check your cost of goods manufactured with the instructor. If it is correct, proceed to part (3).
3. Each team member is to be responsible for computing one of the following amounts. You are not to duplicate your teammates' work. Get any necessary amounts from teammates. Each member is to explain the computation to the team in preparation for reporting to class.
a. Net sales.
b. Cost of goods sold.
c. Gross profit.
d. Total operating expenses.
e. Net income or loss before taxes.
CALCULATE T TEST
Calculate the “t” value for independent groups for the following data using the formula provided in the attached word document. Using the raw measurement data presented, determine whether or not there exists a statistically significant difference between the salaries of female and male human resource managers using the appropriate t-test. Develop a testable hypothesis, confidence level, and degrees of freedom. Report the required “t” critical values based on the degrees of freedom. Show calculations.
Answer
The null hypothesis tested is
H0: There is no significant difference between the average salaries of female and male human resource managers. (µ1= µ2)
The alternative hypothesis is
H1: There is significant difference between the average salaries of female and male human resource managers. (µ1≠ µ2)
The test statistic used is
12
12
2
~
NN
DM
MM
tt
S
+-
-
=
Where
22
1122
1212
(1)(1)
11
2
DM
NsNs
S
NNNN
éùéù
-+-
=+
êúêú
+-
ëûëû
Here M1 = 62,200, M2 = 63,700
s1 = 9330.95, s2 = 6912.95
N1 = 10, N2 = 10 (See the excel sheet)
Then,
(
)
(
)
22
(101)9330.95(101)6912.95
11
101021010
DM
S
éù
-+-
éù
=+
êú
êú
+-
ëû
êú
ëû
= 3672.267768
Therefore test statistic,
62,20063,700
3672.267768
t
-
=
= -0.408466946
Degrees of freedom = N1 + N2 – 2 = 10 + 10 – 2 = 18
Let the significance level be 0.05.
Rejection criteria: Reject the null hypothesis, if the calculated value of t is greater than the critical value of t at 0.05 significance level.
The critical values can be obtained from the student’s t tables with 18 d.f. at 0.05 significance level.
Upper critical value = 2.1
Lower critical value = -2.1
0
.
4
0
.
3
0
.
2
0
.
1
0
.
0
X
D
e
n
s
i
t
y
-
2
.
1
0
0
.
0
2
5
2
.
1
0
0
.
0
2
5
0
D
i
s
t
r
i
b
u
t
i
o
n
P
l
o
t
T
,
d
f
=
1
8
Conclusion: Fails to reject the null hypothesis. The sample does not provide enough evidence to support the claim that there is significant difference ...
This document discusses hypothesis testing for two populations. It covers testing whether two independent population means or proportions are equal using z-tests or t-tests. It also addresses testing whether the mean difference between paired observations is equal to zero using a t-test. Examples are provided for each test. The learning objectives are to understand how to perform these different types of two-sample hypothesis tests using the appropriate test statistic and test assumptions.
Chemometrics-ANALYTICAL DATA SIGNIFICANCE TESTS.pptxHakimuNsubuga2
Significance tests are used to determine if differences between samples are statistically significant or could be attributed to random error. A significance test compares a test statistic to a critical value to evaluate the null hypothesis, which states there is no real difference between populations. Common tests include Student's t-test to compare means, paired t-tests for methods testing the same samples, and F-tests to check if variances are significantly different before pooling estimates. One-tailed tests check for differences in one direction, while two-tailed tests consider differences in either direction. Significance tests are widely applied to evaluate experimental results.
This document discusses different types of t-tests used to compare means: one sample t-tests, independent samples t-tests, and paired samples t-tests. It provides examples and steps for conducting each type of t-test in SPSS. Key points include that one sample t-tests compare a sample mean to a known value, independent samples t-tests compare means between two unrelated groups, and paired samples t-tests compare means within the same group across two time points or conditions. The document also outlines assumptions, how to interpret output and p-values, and how to report results for each t-test. Three cases are presented to demonstrate application of each t-test type.
Assessment 3 – Hypothesis, Effect Size, Power, and t Tests.docxcargillfilberto
Assessment 3 – Hypothesis, Effect Size, Power, and
t
Tests
Complete the following problems within this Word document. Do not submit other files. Show your work for problem sets that require calculations. Ensure that your answer to each problem is clearly visible. You may want to highlight your answer or use a different type color to set it apart.
Hypothesis, Effect Size, and Power
Problem Set 3.1: Sampling Distribution of the Mean Exercise
Criterion:
Interpret population mean and variance.
Instructions:
Read the information below and answer the questions.
Suppose a researcher wants to learn more about the mean attention span of individuals in some hypothetical population. The researcher cites that the attention span (the time in minutes attending to some task) in this population is normally distributed with the following characteristics: 20
36
. Based on the parameters given in this example, answer the following questions:
1. What is the population mean (μ)? __________________________
2. What is the population variance
? __________________________
3. Sketch the distribution of this population. Make sure you draw the shape of the distribution and label the mean plus and minus three standard deviations.
Problem Set 3.2: Effect Size and Power
Criterion:
Explain effect size and power.
Instructions:
Read each of the following three scenarios and answer the questions.
Two researchers make a test concerning the effectiveness of a drug use treatment. Researcher A determines that the effect size in the population of males is
d
= 0.36; Researcher B determines that the effect size in the population of females is
d
= 0.20. All other things being equal, which researcher has more power to detect an effect? Explain. ______________________________________________________________________
Two researchers make a test concerning the levels of marital satisfaction among military families. Researcher A collects a sample of 22 married couples (
n
= 22); Researcher B collects a sample of 40 married couples (
n
= 40). All other things being equal, which researcher has more power to detect an effect? Explain. ______________________________________________________________________
Two researchers make a test concerning standardized exam performance among senior high school students in one of two local communities. Researcher A tests performance from the population in the northern community, where the standard deviation of test scores is 110 (
); Researcher B tests performance from the population in the southern community, where the standard deviation of test scores is 60 (
). All other things being equal, which researcher has more power to detect an effect? Explain. ______________________________________________________________________
Problem Set 3.3: Hypothesis, Direction, and Population Mean
Criterion:
Explain the relationship between hypothesis, tests, and population mean.
Instructions:
Read the following and answer the questions.
This document summarizes the analysis of data from a pharmaceutical company to model and predict the output variable (titer) from input variables in a biochemical drug production process. Several statistical models were evaluated including linear regression, random forest, and MARS. The analysis involved developing blackbox models using only controlled input variables, snapshot models using all input variables at each time point, and history models incorporating changes in input variables over time to predict titer values. Model performance was compared using cross-validation.
MARKETING MANAGEMENT PHILOSOPHIES
CHAPTER 1 - ASSIGNMENT
Question 1.
Considering the differences of the philosophies, in some cases slight differences, select a company (product or service) and describe the current philosophy they pose for the customer. Include in your comments the level of customer value delivered by the company’s actions.
In other words, measure the company’s interaction with their customers against the Market Concept Philosophy. Does the company operate under the Market Concept Philosophy or do they lean more toward one of the other Philosophies.
Be specific with your examples.
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 variabl ...
Statistical modeling in pharmaceutical research and developmentPV. Viji
Statistical modeling in pharmaceutical research and development , Statistical Modeling , Descriptive Versus Mechanistic Modeling , Statistical Parameters Estimation , Confidence Regions , Non Linearity at the Optimum , Sensitivity Analysis , Optimal Design , Population Modeling
This document provides an overview of non-parametric tests presented by Ms. Prajakta Sawant. It discusses non-parametric tests as distribution-free statistical tests that do not require assumptions about the underlying population distribution. Common non-parametric tests described include the Wilcoxon rank-sum test, Kruskal-Wallis test, Spearman's rank correlation coefficient, and the chi-square test. Examples are provided for each test to illustrate their application and interpretation.
TEST #1Perform the following two-tailed hypothesis test, using a.docxmattinsonjanel
TEST #1
Perform the following two-tailed hypothesis test, using a .05 significance level:
· Intrinsic by Gender
· State the null and an alternate statement for the test
· Use Microsoft Excel (Data Analysis Tools) to process your data and run the appropriate test. Copy and paste the results of the output to your report in Microsoft Word.
· Identify the significance level, the test statistic, and the critical value.
· State whether you are rejecting or failing to reject the null hypothesis statement.
· Explain how the results could be used by the manager of the company.
TEST #2
Perform the following two-tailed hypothesis test, using a .05 significance level:
· Extrinsic variable by Position Type
· State the null and an alternate statement for the test
· Use Microsoft Excel (Data Analysis Tools) to process your data and run the appropriate test.
· Copy and paste the results of the output to your report in Microsoft Word.
· Identify the significance level, the test statistic, and the critical value.
· State whether you are rejecting or failing to reject the null hypothesis statement.
· Explain how the results could be used by the manager of the company.
GENERAL ANALYSIS (Research Required)
Using your textbook or other appropriate college-level resources:
· Explain when to use a t-test and when to use a z-test. Explore the differences.
· Discuss why samples are used instead of populations.
The report should be well written and should flow well with no grammatical errors. It should include proper citation in APA formatting in both the in-text and reference pages and include a title page, be double-spaced, and in Times New Roman, 12-point font. APA formatting is necessary to ensure academic honesty.
Be sure to provide references in APA format for any resource you may use to support your answers.
Making Inferences
When data are collected, various summary statistics and graphs can be used for describing data; however, learning about what the data mean is where the power of statistics starts. For example, is there really a difference between two leading cola products? Hypothesis testing is an example of making these types of inferences on data sets.
Hypothesis Tests
Claims are made all the time, such as a particular light bulb will last a certain number of hours.
Claims like this are tested with hypothesis testing. It is a straight forward procedure that consists of the following steps:
1. A claim is made.
2. A value for probability of significance is chosen.
3. Data are collected.
4. The test is performed.
5. The results are analyzed.
Hypothesis tests are performed on the mean of the population. µ
It is not possible to test the full population. For example, it would be impossible to test every light bulb. Instead, the hypothesis test is performed on a sample of the population.
Setting up a Hypothesis Test
When performing hypothesis testing, the test is setup with a null hypothesis (or claim) and the alternative hypothesis. ...
Hypothesis, Types of errors - Parametric and Non-Parametric tests - Critical ...Sundar B N
Hypothesis concept
Hypothesis Example
Hypothesis meaning
Hypothesis definition
Hypothesis formulation
Hypothesis types
Critical regions
One tailed test
Two tailed test
Types of errors
Type I error
Type II error
Parametric statistical test
Parametric statistical types
Non parametric statistical
Meaning
types
Difference
This document discusses non-parametric tests and how to use them to compare groups when assumptions of parametric tests are violated. It explains that non-parametric tests like the Wilcoxon and Kruskal-Wallis tests can be used when samples are small or data is not normally distributed. The Kruskal-Wallis test allows comparison of more than two groups by ranking all data and comparing mean ranks between groups. An example compares student grades under different teaching methods using both Kruskal-Wallis and ANOVA tests.
This document discusses various machine learning models for classifying hepatic injury status based on biological and chemical predictor variables. It finds that models using both predictor types perform best, with Partial Least Squares Discriminant Analysis (PLSDA) achieving the highest accuracy. When using only one type, chemical predictors yield more accurate models than biological predictors alone. Up-sampling the training data to address class imbalance improves performance over down-sampling. The top predictive variables differ between predictor types and injury classes.
The document discusses the sign test, a nonparametric hypothesis test that does not require assumptions about the population distribution. The sign test can be used to test claims involving matched pairs, nominal data with two categories, or the population median. The document provides guidelines for performing the sign test in each of these cases, including stating hypotheses, determining sample sizes and test statistics, and making conclusions. Examples are also given to illustrate the sign test for matched pairs, nominal data, and testing the population median.
Statistical Techniques in Business & Economics (McGRAV-HILL) 12 Edt. Chapter ...tarta
This chapter discusses sampling methods and the central limit theorem. It has five learning goals:
1) Explain why sampling is used instead of studying the entire population.
2) Describe methods for selecting a sample, including random sampling techniques.
3) Define and construct the sampling distribution of the sample mean.
4) Explain the central limit theorem and how it applies to sampling distributions.
5) Use the central limit theorem to find probabilities related to sample means.
This document discusses different techniques for selecting machine learning models, including random train/test splitting, resampling methods like k-fold cross-validation and bootstrap, and probabilistic measures. Resampling techniques like k-fold cross-validation estimate error by evaluating models on out-of-sample data. Probabilistic measures consider both a model's performance and complexity, seeking to balance fit and simplicity. Common probabilistic measures mentioned are the Akaike Information Criterion, Bayesian Information Criterion, Minimum Description Length, and Structural Risk Minimization.
This document provides information about non-parametric statistical tests. It discusses the Mann-Whitney U test, chi-square test, and how to perform chi-square tests in SPSS. Key points include:
- Non-parametric tests do not assume a specific data distribution and can be used for small sample sizes, ordinal data, or outliers. Examples include Mann-Whitney U, Kruskal-Wallis, and chi-square tests.
- Chi-square tests independence between two categorical variables. Assumptions include frequencies data and expected counts over 5 in 80% of cells.
- To perform a chi-square test in SPSS, select two categorical variables, choose crosstabs
A case study that explains how quality of data is much better in case of online surveys, with guidelines on how sampling and non-sampling errors are eliminated.
Multinomial logisticregression basicrelationshipsAnirudha si
This document provides an overview of multinomial logistic regression. It discusses how multinomial logistic regression compares multiple groups through binary logistic regressions. It describes how to interpret the results, including evaluating the overall relationship between predictors and the dependent variable and relationships between individual predictors and the dependent variable. Requirements and assumptions of the analysis are explained, such as the dependent variable being non-metric and cases-to-variable ratios. Methods for evaluating model accuracy and usefulness are also outlined.
Explains the concept of autovalidation that can be used to select predictive models with data from designed experiments where a true validation set is not available. Contains three case studies to demonstrate the approach
Class24 chi squaretestofindependenceposthocBetynatha Kb
This document provides an overview of the chi-square test of independence through 18 slides. It defines independence, demonstrates it, discusses expected frequencies, and outlines the 5 steps for conducting a chi-square test of independence: 1) checking assumptions, 2) stating hypotheses and significance level, 3) identifying the sampling distribution and test statistic, 4) computing the test statistic, and 5) making a decision and interpreting results. It also covers examining standardized residuals to identify which cells are contributing most to a significant result.
Class24 chi squaretestofindependenceposthoc(1)arvindmnnitmsw
This document provides an overview of the chi-square test of independence through 15 slides. It defines independence, demonstrates it using an example, and outlines the 5 steps for conducting a chi-square test of independence: 1) checking assumptions, 2) stating hypotheses and level of significance, 3) identifying the sampling distribution and test statistic, 4) computing the test statistic, and 5) making a decision and interpreting results. It also discusses how to identify which cells are contributing to a significant result using standardized residuals.
This document discusses regression diagnostic checking techniques applied to a study examining factors that influence babies' weight at birth. The study uses mothers' weights and ages as independent variables to predict babies' weight (dependent variable) using linear regression analysis. All regression assumptions (normality of residuals, no collinearity between independent variables, no outliers, linear model) were met based on the diagnostic checking techniques applied to the data.
This document discusses the independent t-test, which is used to evaluate mean differences between two independent samples from different populations. It describes the key characteristics of an independent-measures design, including that it uses separate samples without prior knowledge of the population parameters. The t-test follows four steps: stating hypotheses; finding critical values; computing the test statistic, which compares the sample mean difference to the standard error; and making a decision about whether to reject the null hypothesis of no mean difference. It also notes the importance of the homogeneity of variance assumption and alternatives if it is violated.
One aspect of epidemiology is the study of the epidemic, endemic, an.docxIlonaThornburg83
One aspect of epidemiology is the study of the epidemic, endemic, and pandemic occurrence of disease(s).
Some critics may argue diseases and conditions such as bird flu are endemic in many countries, and some may argue human immunodeficiency virus (HIV) or AIDS is a series of epidemics.
Using the South University Online Library or the Internet, research about the various epidemic, endemic, and pandemic occurrence of disease(s).
Based on your research and understanding, answer the following questions:
At what point does a disease become an epidemic, endemic, or pandemic? What are the parameters that define each of these states of a disease's effect?
Do you agree that bird flu, HIV, or AIDS could be described as a series of epidemics? Why or why not?
Should we study epidemiology and disease control as a complement to the provision of healthcare services? Why or why not?
Disease control has evolved since the discoveries and achievements of these epidemiological pioneers
—
Hippocrates, John Snow, Pasteur, and Koch. Explain the impact of at least one major historical contribution on the current status of epidemiological practices. How can history potentially shape and impact our future work in public health and clinical medicine? Explain.
.
Once you click the Assignment icon above, you will find links to Qui.docxIlonaThornburg83
Once you click the Assignment icon above, you will find links to Quiz 4, provided in two formats, a Word document and a PDF. You may type your work into the Word document, either using an equation editor or plain-text formatting, or you may write your work by hand and scan it.
Please remember to show all work following standard mathematical practice:
1) Each step should show the COMPLETE expression or equation, not just a piece of it.
2) Each new step should follow logically from the step above it, following rules of algebra.
3) Each new step should be beneath the previous step.
4) The equal sign, =, should only connect equal numbers or expressions.
due tonight - need by 8pm
.
More Related Content
Similar to Module 05 – Hypothesis Tests Using Two SamplesClass Objectives
Assessment 3 – Hypothesis, Effect Size, Power, and t Tests.docxcargillfilberto
Assessment 3 – Hypothesis, Effect Size, Power, and
t
Tests
Complete the following problems within this Word document. Do not submit other files. Show your work for problem sets that require calculations. Ensure that your answer to each problem is clearly visible. You may want to highlight your answer or use a different type color to set it apart.
Hypothesis, Effect Size, and Power
Problem Set 3.1: Sampling Distribution of the Mean Exercise
Criterion:
Interpret population mean and variance.
Instructions:
Read the information below and answer the questions.
Suppose a researcher wants to learn more about the mean attention span of individuals in some hypothetical population. The researcher cites that the attention span (the time in minutes attending to some task) in this population is normally distributed with the following characteristics: 20
36
. Based on the parameters given in this example, answer the following questions:
1. What is the population mean (μ)? __________________________
2. What is the population variance
? __________________________
3. Sketch the distribution of this population. Make sure you draw the shape of the distribution and label the mean plus and minus three standard deviations.
Problem Set 3.2: Effect Size and Power
Criterion:
Explain effect size and power.
Instructions:
Read each of the following three scenarios and answer the questions.
Two researchers make a test concerning the effectiveness of a drug use treatment. Researcher A determines that the effect size in the population of males is
d
= 0.36; Researcher B determines that the effect size in the population of females is
d
= 0.20. All other things being equal, which researcher has more power to detect an effect? Explain. ______________________________________________________________________
Two researchers make a test concerning the levels of marital satisfaction among military families. Researcher A collects a sample of 22 married couples (
n
= 22); Researcher B collects a sample of 40 married couples (
n
= 40). All other things being equal, which researcher has more power to detect an effect? Explain. ______________________________________________________________________
Two researchers make a test concerning standardized exam performance among senior high school students in one of two local communities. Researcher A tests performance from the population in the northern community, where the standard deviation of test scores is 110 (
); Researcher B tests performance from the population in the southern community, where the standard deviation of test scores is 60 (
). All other things being equal, which researcher has more power to detect an effect? Explain. ______________________________________________________________________
Problem Set 3.3: Hypothesis, Direction, and Population Mean
Criterion:
Explain the relationship between hypothesis, tests, and population mean.
Instructions:
Read the following and answer the questions.
This document summarizes the analysis of data from a pharmaceutical company to model and predict the output variable (titer) from input variables in a biochemical drug production process. Several statistical models were evaluated including linear regression, random forest, and MARS. The analysis involved developing blackbox models using only controlled input variables, snapshot models using all input variables at each time point, and history models incorporating changes in input variables over time to predict titer values. Model performance was compared using cross-validation.
MARKETING MANAGEMENT PHILOSOPHIES
CHAPTER 1 - ASSIGNMENT
Question 1.
Considering the differences of the philosophies, in some cases slight differences, select a company (product or service) and describe the current philosophy they pose for the customer. Include in your comments the level of customer value delivered by the company’s actions.
In other words, measure the company’s interaction with their customers against the Market Concept Philosophy. Does the company operate under the Market Concept Philosophy or do they lean more toward one of the other Philosophies.
Be specific with your examples.
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 variabl ...
Statistical modeling in pharmaceutical research and developmentPV. Viji
Statistical modeling in pharmaceutical research and development , Statistical Modeling , Descriptive Versus Mechanistic Modeling , Statistical Parameters Estimation , Confidence Regions , Non Linearity at the Optimum , Sensitivity Analysis , Optimal Design , Population Modeling
This document provides an overview of non-parametric tests presented by Ms. Prajakta Sawant. It discusses non-parametric tests as distribution-free statistical tests that do not require assumptions about the underlying population distribution. Common non-parametric tests described include the Wilcoxon rank-sum test, Kruskal-Wallis test, Spearman's rank correlation coefficient, and the chi-square test. Examples are provided for each test to illustrate their application and interpretation.
TEST #1Perform the following two-tailed hypothesis test, using a.docxmattinsonjanel
TEST #1
Perform the following two-tailed hypothesis test, using a .05 significance level:
· Intrinsic by Gender
· State the null and an alternate statement for the test
· Use Microsoft Excel (Data Analysis Tools) to process your data and run the appropriate test. Copy and paste the results of the output to your report in Microsoft Word.
· Identify the significance level, the test statistic, and the critical value.
· State whether you are rejecting or failing to reject the null hypothesis statement.
· Explain how the results could be used by the manager of the company.
TEST #2
Perform the following two-tailed hypothesis test, using a .05 significance level:
· Extrinsic variable by Position Type
· State the null and an alternate statement for the test
· Use Microsoft Excel (Data Analysis Tools) to process your data and run the appropriate test.
· Copy and paste the results of the output to your report in Microsoft Word.
· Identify the significance level, the test statistic, and the critical value.
· State whether you are rejecting or failing to reject the null hypothesis statement.
· Explain how the results could be used by the manager of the company.
GENERAL ANALYSIS (Research Required)
Using your textbook or other appropriate college-level resources:
· Explain when to use a t-test and when to use a z-test. Explore the differences.
· Discuss why samples are used instead of populations.
The report should be well written and should flow well with no grammatical errors. It should include proper citation in APA formatting in both the in-text and reference pages and include a title page, be double-spaced, and in Times New Roman, 12-point font. APA formatting is necessary to ensure academic honesty.
Be sure to provide references in APA format for any resource you may use to support your answers.
Making Inferences
When data are collected, various summary statistics and graphs can be used for describing data; however, learning about what the data mean is where the power of statistics starts. For example, is there really a difference between two leading cola products? Hypothesis testing is an example of making these types of inferences on data sets.
Hypothesis Tests
Claims are made all the time, such as a particular light bulb will last a certain number of hours.
Claims like this are tested with hypothesis testing. It is a straight forward procedure that consists of the following steps:
1. A claim is made.
2. A value for probability of significance is chosen.
3. Data are collected.
4. The test is performed.
5. The results are analyzed.
Hypothesis tests are performed on the mean of the population. µ
It is not possible to test the full population. For example, it would be impossible to test every light bulb. Instead, the hypothesis test is performed on a sample of the population.
Setting up a Hypothesis Test
When performing hypothesis testing, the test is setup with a null hypothesis (or claim) and the alternative hypothesis. ...
Hypothesis, Types of errors - Parametric and Non-Parametric tests - Critical ...Sundar B N
Hypothesis concept
Hypothesis Example
Hypothesis meaning
Hypothesis definition
Hypothesis formulation
Hypothesis types
Critical regions
One tailed test
Two tailed test
Types of errors
Type I error
Type II error
Parametric statistical test
Parametric statistical types
Non parametric statistical
Meaning
types
Difference
This document discusses non-parametric tests and how to use them to compare groups when assumptions of parametric tests are violated. It explains that non-parametric tests like the Wilcoxon and Kruskal-Wallis tests can be used when samples are small or data is not normally distributed. The Kruskal-Wallis test allows comparison of more than two groups by ranking all data and comparing mean ranks between groups. An example compares student grades under different teaching methods using both Kruskal-Wallis and ANOVA tests.
This document discusses various machine learning models for classifying hepatic injury status based on biological and chemical predictor variables. It finds that models using both predictor types perform best, with Partial Least Squares Discriminant Analysis (PLSDA) achieving the highest accuracy. When using only one type, chemical predictors yield more accurate models than biological predictors alone. Up-sampling the training data to address class imbalance improves performance over down-sampling. The top predictive variables differ between predictor types and injury classes.
The document discusses the sign test, a nonparametric hypothesis test that does not require assumptions about the population distribution. The sign test can be used to test claims involving matched pairs, nominal data with two categories, or the population median. The document provides guidelines for performing the sign test in each of these cases, including stating hypotheses, determining sample sizes and test statistics, and making conclusions. Examples are also given to illustrate the sign test for matched pairs, nominal data, and testing the population median.
Statistical Techniques in Business & Economics (McGRAV-HILL) 12 Edt. Chapter ...tarta
This chapter discusses sampling methods and the central limit theorem. It has five learning goals:
1) Explain why sampling is used instead of studying the entire population.
2) Describe methods for selecting a sample, including random sampling techniques.
3) Define and construct the sampling distribution of the sample mean.
4) Explain the central limit theorem and how it applies to sampling distributions.
5) Use the central limit theorem to find probabilities related to sample means.
This document discusses different techniques for selecting machine learning models, including random train/test splitting, resampling methods like k-fold cross-validation and bootstrap, and probabilistic measures. Resampling techniques like k-fold cross-validation estimate error by evaluating models on out-of-sample data. Probabilistic measures consider both a model's performance and complexity, seeking to balance fit and simplicity. Common probabilistic measures mentioned are the Akaike Information Criterion, Bayesian Information Criterion, Minimum Description Length, and Structural Risk Minimization.
This document provides information about non-parametric statistical tests. It discusses the Mann-Whitney U test, chi-square test, and how to perform chi-square tests in SPSS. Key points include:
- Non-parametric tests do not assume a specific data distribution and can be used for small sample sizes, ordinal data, or outliers. Examples include Mann-Whitney U, Kruskal-Wallis, and chi-square tests.
- Chi-square tests independence between two categorical variables. Assumptions include frequencies data and expected counts over 5 in 80% of cells.
- To perform a chi-square test in SPSS, select two categorical variables, choose crosstabs
A case study that explains how quality of data is much better in case of online surveys, with guidelines on how sampling and non-sampling errors are eliminated.
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This document provides an overview of multinomial logistic regression. It discusses how multinomial logistic regression compares multiple groups through binary logistic regressions. It describes how to interpret the results, including evaluating the overall relationship between predictors and the dependent variable and relationships between individual predictors and the dependent variable. Requirements and assumptions of the analysis are explained, such as the dependent variable being non-metric and cases-to-variable ratios. Methods for evaluating model accuracy and usefulness are also outlined.
Explains the concept of autovalidation that can be used to select predictive models with data from designed experiments where a true validation set is not available. Contains three case studies to demonstrate the approach
Class24 chi squaretestofindependenceposthocBetynatha Kb
This document provides an overview of the chi-square test of independence through 18 slides. It defines independence, demonstrates it, discusses expected frequencies, and outlines the 5 steps for conducting a chi-square test of independence: 1) checking assumptions, 2) stating hypotheses and significance level, 3) identifying the sampling distribution and test statistic, 4) computing the test statistic, and 5) making a decision and interpreting results. It also covers examining standardized residuals to identify which cells are contributing most to a significant result.
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This document provides an overview of the chi-square test of independence through 15 slides. It defines independence, demonstrates it using an example, and outlines the 5 steps for conducting a chi-square test of independence: 1) checking assumptions, 2) stating hypotheses and level of significance, 3) identifying the sampling distribution and test statistic, 4) computing the test statistic, and 5) making a decision and interpreting results. It also discusses how to identify which cells are contributing to a significant result using standardized residuals.
This document discusses regression diagnostic checking techniques applied to a study examining factors that influence babies' weight at birth. The study uses mothers' weights and ages as independent variables to predict babies' weight (dependent variable) using linear regression analysis. All regression assumptions (normality of residuals, no collinearity between independent variables, no outliers, linear model) were met based on the diagnostic checking techniques applied to the data.
This document discusses the independent t-test, which is used to evaluate mean differences between two independent samples from different populations. It describes the key characteristics of an independent-measures design, including that it uses separate samples without prior knowledge of the population parameters. The t-test follows four steps: stating hypotheses; finding critical values; computing the test statistic, which compares the sample mean difference to the standard error; and making a decision about whether to reject the null hypothesis of no mean difference. It also notes the importance of the homogeneity of variance assumption and alternatives if it is violated.
Similar to Module 05 – Hypothesis Tests Using Two SamplesClass Objectives (20)
One aspect of epidemiology is the study of the epidemic, endemic, an.docxIlonaThornburg83
One aspect of epidemiology is the study of the epidemic, endemic, and pandemic occurrence of disease(s).
Some critics may argue diseases and conditions such as bird flu are endemic in many countries, and some may argue human immunodeficiency virus (HIV) or AIDS is a series of epidemics.
Using the South University Online Library or the Internet, research about the various epidemic, endemic, and pandemic occurrence of disease(s).
Based on your research and understanding, answer the following questions:
At what point does a disease become an epidemic, endemic, or pandemic? What are the parameters that define each of these states of a disease's effect?
Do you agree that bird flu, HIV, or AIDS could be described as a series of epidemics? Why or why not?
Should we study epidemiology and disease control as a complement to the provision of healthcare services? Why or why not?
Disease control has evolved since the discoveries and achievements of these epidemiological pioneers
—
Hippocrates, John Snow, Pasteur, and Koch. Explain the impact of at least one major historical contribution on the current status of epidemiological practices. How can history potentially shape and impact our future work in public health and clinical medicine? Explain.
.
Once you click the Assignment icon above, you will find links to Qui.docxIlonaThornburg83
Once you click the Assignment icon above, you will find links to Quiz 4, provided in two formats, a Word document and a PDF. You may type your work into the Word document, either using an equation editor or plain-text formatting, or you may write your work by hand and scan it.
Please remember to show all work following standard mathematical practice:
1) Each step should show the COMPLETE expression or equation, not just a piece of it.
2) Each new step should follow logically from the step above it, following rules of algebra.
3) Each new step should be beneath the previous step.
4) The equal sign, =, should only connect equal numbers or expressions.
due tonight - need by 8pm
.
one day when you woke up you saw doreman in you room .he has a tim.docxIlonaThornburg83
one day when you woke up you saw doreman in you room .
he has a time machine, by using the time machine you tranported your self to the STONE AGE.
There you met some caveman you managed to speak to them as they spoke english:
write the conversation you had with them in english
please include the following in your conversation.
mention you question and the answers given by the caveman in bubbles:
1. the type of houses they lived in?( stoneage)
2. the natural vegetation they had ?
3. the type of tools they used ?
4.the food they ate ?
5.the type of dresses they wore ?400
.
One afternoon at work, Natalie received a phone call from her daught.docxIlonaThornburg83
One afternoon at work, Natalie received a phone call from her daughter’s teacher. It seemed that Brandi had got into trouble, and Natalie would need to meet with Brandi’s teacher and the school principal. Natalie could not imagine what the trouble could be. Brandi was a straight-A student, played soccer, and was part of the school band. She also helped out with chores at home. On the way to the school, Natalie decided she would not jump to conclusions but would hear Brandi’s side of the story. Then, she would let Brandi have a piece of her mind!
At school, Natalie met the school principal; Brandi’s teacher; and a crying, red-eyed Brandi. Brandi and two other girls had stolen a pack of cigarettes from a teacher’s purse and were caught smoking in the woods behind the school. Worse, one of the other girls had stolen the teacher’s prescription medication, though Brandi said she did not know anything about that. The principal and teacher said that this was a serious breach of trust and was against school policy. They knew Brandi and were “shocked” that she was involved in this activity. In private consultation with Natalie, they said that Brandi was involved with the wrong crowd, but there was still time to intervene before she developed a pattern of bad behavior.
Natalie left the meeting angry with Brandi, but also feeling guilty and responsible. She had been working extra hours and was often busy with her schoolwork. Perhaps she had neglected Brandi or missed important warning signs. She would ground Brandi, but more importantly, she would pay much closer attention to whom she befriended and where she went. Natalie decided she would establish a schedule where she would help the girls’ do their homework.
Natalie felt tired. After all the years of guidance and parenting, how could “two stupid tweens” undo all her hard work? She felt she had worked hard teaching Brandi and Jenny how to make good decisions and to know right from wrong. She worried what the next ten years would bring. She pondered the possibilities of other peer influences, alcohol, drugs, and boys.
Research differential association theory and social learning theory as applied to criminal behavior and crime using the textbook, the Argosy University online library resources, and the Internet. Select two scholarly, peer-reviewed articles for use in this assignment.
Based on the scenario, your readings and research, respond to the following:
How could Brandi’s behavior be explained using differential association theory?
How could Brandi’s behavior be explained using social learning theory?
What are the strengths and limitations of these two theories as applied to this example?
Be sure to support your responses using the selected resources.
Write your initial response in 4–6 paragraphs. Apply APA standards to citation of sources.
.
Once the United States got involved in World War I, what role did it.docxIlonaThornburg83
Once the United States got involved in World War I, what role did it play in winning the war and framing the peace that followed? Should the United States have stayed out of the war?
answer should be about six paragraphs long and include details and examples that support each of your points
.
Once a Delinquent, Always a Delinquent Please respond to the foll.docxIlonaThornburg83
"Once a Delinquent, Always a Delinquent" Please respond to the following:
Discuss whether or not you believe that labeling a child as a juvenile delinquent is a self-fulfilling prophesy. Justify your response.
Identify at least two (2) ways in which children adapt to parental power and oppression. Next, discuss the manner in which these adaptations may contribute to delinquent behavior
.
On page 118 of your textbook is a picture of the sculpture Pietà by .docxIlonaThornburg83
The document discusses a sculpture called Pietà by Michelangelo that is pictured in a textbook on page 118. It notes that Michelangelo's Renaissance period drew inspiration from Greek ideas, as the group has studied. It provides two discussion questions asking the reader to compare Pietà to either Hellenic or Hellenistic sculpture in 200-250 words, and to compare Pietà to David in terms of intent, subject matter, and mastery, stating a preference for one with reasons.
Once a disease is thought to be caused by an infectious agent, a r.docxIlonaThornburg83
Once a disease is thought to be caused by an infectious agent, a range of epidemiological techniques is used to determine the extent of transmission in a population and to find the most appropriate and responsive measures to control further transmission.
As a newly trained Epidemic Intelligence Service (EIS) officer, you are asked to develop a project to detect and control an outbreak of an infectious disease.
Identify an infectious disease that can be detected and controlled through screening. Describe how screening influences and enhances outbreak detection as well as control and prevention. Discuss how and where you would implement a screening initiative and who would be the core or target population.
Justify your response using examples and reasoning. Comment on the postings of at least two classmates, explaining whether you agree or disagree with their views.
Evaluation Criteria
:
Provided one example of an infectious disease.
Described how screening is used for the detection and control of outbreaks.
Discussed how and where a screening initiative would be implemented and who would be the core population.
Justified answers with appropriate research and reasoning by using examples and references from textbooks, the South University Online Library, and other acceptable references, citing the sources in APA format.
Commented on the postings of at least two classmates by asking questions, providing a point of view with a rationale, challenging a point of the discussion, or making a relationship between two or more points.
.
Once you have identified two questions that interest you, conduct an.docxIlonaThornburg83
Once you have identified two questions that interest you, conduct an Internet search of the key terms from these questions. During your search, find 1-2 sources that speak to the questions and provide a brief summary of what additional information you have found that answers your query.
What are the primary industries of Naples, Italy?
What role did the city of Alexandria play in the ancient world?
.
On December 31, 2015, Ms. Levine CPA, your manager and the treasurer.docxIlonaThornburg83
On December 31, 2015, Ms. Levine CPA, your manager and the treasurer of the U.S. division of the pharmaceutical company Meeack Corp. had just finished acquiring the United Kingdom drug company Zulu LLP, and, after utilizing her knowledge of the IFRS, realized the FASB and IASB designed a roadmap for convergence by 2015. She would like to know the reasons why the U.S. is not going to be converting to the IFRS by 2015.
Required:
Using the SEC staff report issued in July 2012, take a position and then argue and support for your manager at least three reasons why you
believe,
or
do not believe,
that the SEC is correct in its position to delay convergence.
Your well-written paper must be 2-3 pages, in addition to title and reference pages. Cite at least two peer-reviewed sources, in addition to the required reading for the module.
.
On Dumpster Diving” by Lars Eighner (50 Essays, p. 139-15.docxIlonaThornburg83
“On Dumpster Diving” by Lars Eighner (
50 Essays
, p. 139-151)
Due Date: Tuesday, October 18
1.
Eighner begins the essay by explaining where the term “Dumpster” originated. Why do you think he begins this way?
2.
What is Eighner’s opinion of college students? Why is scavenging around a college campus so effective for him?
3.
Define the following vocabulary words from the essay. Use each word in a sentence of your own. Briefly explain why the author of the essay chose to use these words.
a.
Scrounging (139)
i.
Definition:
ii.
Part of Speech:
iii.
Sentence:
iv.
Why does the author use this word?
b.
Sinecure (150)
i.
Definition:
ii.
Part of Speech:
iii.
Sentence:
iv.
Why does the author use this word?
4.
.
Ok so I have done all the calculations, graphs and interpritations m.docxIlonaThornburg83
Ok so I have done all the calculations, graphs and interpritations myself, unfortuanatly something came up and i can not do the last part "
Summarize your results from 1–14 in a report that is 3 pages or less in length and explain and interpret the results in ways that are understandable to someone who does not know statistics." I need someone that understand stats but also capable of writing. I have attached all my data and related documents.
.
Ok so I know this is extreme short notice but I have a final 6 page .docxIlonaThornburg83
Ok so I know this is extreme short notice but I have a final 6 page paper due tomorrow, it has to be on a major literary author before 1965, I was going to do Ernest Hemingway. First 2-3 pages include introduction, short biography some of the authors influences/who they influences, cultural & historical context (period of influence such as war), themes in major works, specific theme and plot summary of one major work written by this author. Last pages are to discuss four elements of fiction (literary analysis) Use 4 passages throughout this and then the conclusion. In MLA format.
.
Offenses and Punishment. Please respond to the following Explai.docxIlonaThornburg83
"Offenses and Punishment." Please respond to the following:
Explain with examples how the Eighth Amendment restricts the government’s authority to make something a crime.
Analyze Papachristou v. City of Jacksonville. As a public administrator, explain whether there should be a higher concern for public safety or for individual rights. Support your position with examples or evidence.
.
Omit all general journal entry explanations.Be sure to include c.docxIlonaThornburg83
Omit all general journal entry
explanations.
Be sure to include correct dollar signs, underlines and double underlines.
Question 1 (15 points) Statement of Cash Flows
The following is selected information from Murphy Company for the fiscal years ended December 31, 2015: Murphy Company had net income of $500,000. Depreciation was $50,000, purchases of plant assets were $ 250,000, and disposals of plant assets for $500,000 resulted in a $20,000 gain. Stock was issued in exchange for an outstanding note payable of $925,000. Accounts receivable decreased by $25,000. Accounts payable decreased by $10,000. Dividends of $200,000 were paid to shareholders. Murphy Company had interest expense of $5,000. Cash balance on January 1, 2015 was $250,000.
Requirements:Prepare Murphy Company's statement of cash flows for the year ended December 31, 2015 using the indirect method.
Hint (recall the 3 sections)
Question 2 (10 points)
On January 1, 2015, Baker Company purchased 10,000 shares of the stock of Murphy,
and did obtain significant influence
. The investment is intended as a long-term investment. The stock was purchased for $70,000, and represents a 25% ownership stake. Murphy made $20,000 of net income in 2015, and paid dividends of $10,000. The price of Murphy's stock increased from $20 per share at the beginning of the year, to $22 per share at the end of the year.
Requirements:
a.
Prepare the January 1 and December 31 general journal entries for Baker Company.
b.
How much should the Baker Company report on the balance sheet for the investment in Murphy at the end of 2015?
Question 3 (20 Points)
On December 31, 2016, Murphy Inc. had the following balances (all balances are normal):
Accounts
Amount
Preferred Stock, ($100 par value, 5% noncumulative, 50,000 shares authorized, 10,000 shares issued and outstanding)
$1,000,000
Common Stock ($10 par value, 200,000 shares authorized, 100,000 shares issued and outstanding)
$1,000,000
Paid-in Capital in Excess of par, Common
150,000
Retained Earnings
700,000
The following events occurred during 2016 and were not recorded:
a.
On January 1, Murphy declared a 5% stock dividend on its common stock when the market value of the common stock was $15 per share. Stock dividends were distributed on January 31 to shareholders as of January 25.
b.
On February 15, Murphy re-acquired 1,000 shares of common stock for $20 each.
c.
On March 31, Murphy reissued 250 shares of treasury stock for $25 each.
d.
On July 1, Murphy reissued 500 shares of treasury stock for $16 each.
e.
On October 1, Murphy declared full year dividends for preferred stock and $1.50 cash dividends for outstanding shares and paid shareholders on October 15.
f.
On December 15, Murphy split common stock 2 shares for 1.
g.
Net Income for 2016 was $275,000.
Requirements:
a.
Prepare journal entries for the transactions listed above.
b.
Prepare a Stockholders' section of a classified balance sheet as of December 31, 2016.
Question 4 (14 poi.
Offer an alternative explanation for how these patterns of criminal .docxIlonaThornburg83
Offer an alternative explanation for how these patterns of criminal activity and violence affected constitutional law and political freedom.
Having effectively established an early version of
Parlament
, the Anglo-Saxons created a "warlike" system founded on family bonds,
aggricultral
success, acquisition of funds and property, and control through legal means. (Roth, 2005) Crime was a serious matter as this could effect an individual's financial status/land holdings, family and personal reputation, and life. As each
kindship
/kingdom had their own laws; however, your "value" as a human would determine
werdild
(blood price) and options for punishments. (BBC, 2016) Blood
fueds
and vengeance based retaliations occurred. There were no police forces; however, there were "
tithings
" (groups of 10 to 12 men) who were responsible for each other and held accountable for each other's actions. (Roth, 2005) Therefore, if you were accused of
theift
, you and your tithing would appear before a community jury to hear a sentence of death or a fine; however, should you not appear you would then be stripped of your humanity/value and executed. (Roth, 2005)
If the Saxons were known for their death penalty, then the Norman's were known for verdicts of mutilation and forming the class system. (Roth, 2005) Unlike the Saxons, the Norman's legal system did establish a police system that was loyal to the monarch instead of the community or
kinship
. (Roth, 2005) Taking the power away from communities and families to uphold and
despence
the law,
constables
handed everything from "tax collection, arresting
malfectors
, transporting prisoners, and serving legal papers" to maintaining curfew and monarch regulations. (Roth, 2005) Instead of having to survive an ordeal, a
theft
would have to battle to prove his/her innocence or appoint someone to battle for them if the defendant was a woman, child, elderly, or ill individual. (Roth, 2005) If a woman stole an apple, her brother might have to battle the shop keepers.
However, the two systems were vastly different. In the Anglo-Saxon world, the kingdoms experienced more personal and kingdom based freedoms. Even though the death penalty was widely utilized, no positions existed that would be seeking out infractions or looking to punish someone (like a constable). A thief might loose his family and personal honor, face the wrath of his tithing, or have to endure his victim's family claiming their blood price. However, value and worth were placed on family honor, deeds, and contributions to the community. In the United States, this is similar to what we experienced before the civil war. The states had more power than the federal government over their laws and regulations; however, like the Saxons, there were major
inconsistencies
among states regarding policies, sentences for crimes, and even social attitudes towards certain crimes. The Anti Federalist movement in the United States is founded o.
Often, as a business operates, the partners bring some of their pers.docxIlonaThornburg83
Often, as a business operates, the partners bring some of their personal items for use by the partnership so that the partnership does not have to incur the expense of buying these items. These items are then extensively used by the partnership. Over time, do you see some potential sources of disagreement in doing this? What particular problem does this pose when these items are changed or added to in form or character at the expense of the partnership? How important is it that there be some written statement signed by the partners at the time a partner brings a personal asset to the partnership for use in the operation of the business?
.
Of all the GNR technologies (genetic engineering, nanotechnology and.docxIlonaThornburg83
Of all the GNR technologies (genetic engineering, nanotechnology and robotics), nanotechnology has the greatest potential for the destruction of our planet or even our solar system.
Do you agree with Ray Kurzweil that it is possible for society to enjoy the benefits of twenty-first century GNR technologies while mitigating and controlling the risks?
Why or Why Not?
attachments are the reading resources. should around 600 words.
.
Of the five management functions, which do you expect will experienc.docxIlonaThornburg83
Of the five management functions, which do you expect will experience the most dramatic changes in the next decade? Defend your answer. Which will have the least amount of change? Explain your answer. Respond substantively to two other learners.
Guided Response:
Your initial post should be at least 200 words in length. Support your claims with examples from required material(s) and/or other scholarly resources, and properly cite any references. Respond substantively to at least two of your classmates' posts.
The
five
functions
of
management—planning,
organizing,
staffing,
leading,
and
controlling-
have
many
close
linkages.
Planning
is
part
of
every
other
management
function.
Creating
and
maintaining
an
organization's
design
requires
planning.
One
of
the
first
steps
in
staffing
is
human
resource
planning.
Leading
requires
planning.
Leaders
rely
on
motivational
programs
that
are
planned
in
advance.
Teams
and
groups
use
plans
to
direct
activities.
Communication
systems
and
all
the
new
iterations
of
those
systems
necessitate
careful
planning
to
spot
new
trends
and
to
implement
changes
in
technologies.
Planning
is
the
basis
of
control
through
the
use
of
standards.
The
organizing
function
shares
similar
bonds
with
other
management
functions.
The
first
element
of
organizing,
job
design,
is
shared
with
the
staffing
function.
Job
specifications
established
in
the
job
design
aspect
of
organizing
are
used
to
recruit
and
select
employees.
Employees
who
fit
are
able
to
work
well
in
company-prescribed
teams
and
groups
and
to
communicate
effectively
within
the
system.
Staffing
shares
the
human
element
with
leading.
Staffing
involves
choosing
the
right
people.
Leading
includes
enticing
the
highest
levels
of
performance
from
those
people.
Controlling
has
one
element
in
common
with
staffing.
Both
are
involved
in
the
performance
appraisal
process
for
individual
employees.
Standards
link
controlling
and
planning.
Further,
controlling
begins
the
process
of
creating
the
next
set
of
plans.
.
Of the numerous forms of communication technologies presented in thi.docxIlonaThornburg83
Of the numerous forms of communication technologies presented in this course, predict the first form of technology to be phased out by a newer and improved technology. Explain the limitations of this technology and the reason for its speculated obsolesce.
Speculate the technology that will replace the previously mentioned technology above. Describe the features, capabilities, or basic advantages this technology will have over its predecessor.
.
How to Setup Default Value for a Field in Odoo 17Celine George
In Odoo, we can set a default value for a field during the creation of a record for a model. We have many methods in odoo for setting a default value to the field.
How to Download & Install Module From the Odoo App Store in Odoo 17Celine George
Custom modules offer the flexibility to extend Odoo's capabilities, address unique requirements, and optimize workflows to align seamlessly with your organization's processes. By leveraging custom modules, businesses can unlock greater efficiency, productivity, and innovation, empowering them to stay competitive in today's dynamic market landscape. In this tutorial, we'll guide you step by step on how to easily download and install modules from the Odoo App Store.
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.pptHenry Hollis
The History of NZ 1870-1900.
Making of a Nation.
From the NZ Wars to Liberals,
Richard Seddon, George Grey,
Social Laboratory, New Zealand,
Confiscations, Kotahitanga, Kingitanga, Parliament, Suffrage, Repudiation, Economic Change, Agriculture, Gold Mining, Timber, Flax, Sheep, Dairying,
A Free 200-Page eBook ~ Brain and Mind Exercise.pptxOH TEIK BIN
(A Free eBook comprising 3 Sets of Presentation of a selection of Puzzles, Brain Teasers and Thinking Problems to exercise both the mind and the Right and Left Brain. To help keep the mind and brain fit and healthy. Good for both the young and old alike.
Answers are given for all the puzzles and problems.)
With Metta,
Bro. Oh Teik Bin 🙏🤓🤔🥰
Elevate Your Nonprofit's Online Presence_ A Guide to Effective SEO Strategies...TechSoup
Whether you're new to SEO or looking to refine your existing strategies, this webinar will provide you with actionable insights and practical tips to elevate your nonprofit's online presence.
Contiguity Of Various Message Forms - Rupam Chandra.pptx
Module 05 – Hypothesis Tests Using Two SamplesClass Objectives
1. Module 05 –
Hypothesis Tests Using Two Samples
Class Objectives:
· Identify whether two samples are independent or dependent.
· Compare the testing procedures for two sample tests.
· Test hypothesis about two population parameters.
Module 05 - Part 1
Last week we took one sample to see if it supported our
alternative hypothesis. This week we are going to increase to
TWO samples and see if there is a significant difference
between them.
When would we use this?
· Two samples are __________________ ________________ if
the sample values from one population are not related to or
somehow naturally paired or matched with the sample values
from the other population.
· Example:
· Two samples are _____________________________ (or
consist of ______________________________________) if the
sample values are somehow matched, where the matching is
based on some inherent relationship.
· Example:
2. Hint: If the two samples have different sample sizes with no
missing data, they must be independent. If the two samples have
the same sample size, the samples may or may not be
independent.
Put the variables in for each population in the table below.
Population 1
Population 2
Population Mean
Population Standard Deviation
Population Proportion
Sample Size
Sample Mean
Sample Standard Deviation
Sample Proportion
Note: We are going to approach the problem as if are unknown.
This is the most common and means that we will be using the t
test statistic.
3. · The test statistic is given by the formula below:
where we assume .
To calculate the degrees of freedom, pick the
_______________________ n value and subtract 1.
We will be doing the same steps as before to test the hypothesis
(either critical value or p-value test). There are just different
formulas.
· The null hypothesis is given as
_____________________________.
· The alternative hypothesis will be either
____________________________,
___________________________, or
_____________________________.
Example 1. Data Set 26 “Cola Weights and Volumes” in
Appendix B includes weights (lb) of the contents of cans of Diet
Coke (n = 36, x = 0.78479 lb, s = 0.00439 lb) and of the
contents of cans of regular Coke (n = 36, x = 0.81682 lb, s =
0.00751 lb). Use a 0.05 significance level to test the claim that
the contents of cans of Diet Coke have weights with a mean that
is less than the mean for regular Coke.
4. Example 2. Researchers from the University of British
Columbia conducted trials to investigate the effects of color on
creativity. Subjects with a red background were asked to think
of creative uses for a brick; other subjects with a blue
background were given the same task. Responses were scored
by a panel of judges and results from scores of creativity are
given below. Higher scores correspond to more creativity. The
researchers make the claim that “blue enhances performance on
a creative task.” Use a 0.05 significance level to test the claim
that blue enhances performance on a creative task.
Example 3.A study of seat belt use involved children who were
hospitalized after motor vehicle crashes. For a group of 123
children who were wearing seat belts, the number of days in
intensive care units (ICU) has a mean of 0.83 and a standard
deviation of 1.77. For a group of 290 children who were not
wearing seat belts, the number of days spent in ICUs has a mean
of 1.39 and a standard deviation of 3.06. Use a 0.01 significance
level to test the claim that children wearing seat belts have a
lower mean length of time in an ICU than the mean for children
5. not wearing seat belts.
Module 05 - Part 2
Inferential statistics involves forming conclusions about
population parameters.
· These population parameters could be:
The activities that we could perform on two samples are
estimating the value of the population parameters using
confidence intervals and testing claims made about the
population parameters.
Independent Samples
Dependent Samples
Samples taken from two different populations, where the
selection process for one sample is independent of the selection
process for the other sample.
Samples taken from two populations where either (1) the
element samples is a member of both populations or (2) the
element samples in the second population is selected because it
is similar on all other characteristics, or “matched,” to the
element selected from the first population.
Example.
6. Example.
The hypothesis Test for two dependent samples is a bit different
because we need to use the difference from each matched pair to
test the claim.
The null and alternative hypotheses are different for dependent
samples as well.
· Null Hypothesis
___________________
· Alternative Hypothesis
____________________
The difference between the means is less than 0 (negative).
Thus, the first group has smaller mean values.
____________________
The difference between the means is greater than 0 (positive).
Thus, the first group has larger mean values.
· The test statistic for dependent samples uses the following
7. formula:
Example 4. Here we consider one aspect of how we treat women
and men differently based on their ages. Data Set 14 “Oscar
Winner Age” in Appendix B lists ages of actresses when they
won Oscars in the category of Best Actress, along with the ages
of actors when they won Oscars in the category of Best Actor.
The ages are matched according to the year that the awards were
presented. Table 9-2 includes a small random selection of the
available data so that we can better illustrate the procedures of
this section. Use the sample data in Table 9-2 with a 0.05
significance level to test the claim that for the population of
ages of Best Actresses and Best Actors, the differences have a
mean less than 0 (indicating that Best Actresses are generally
younger than Best Actors).
Example 5. A popular theory is that presidential candidates
have an advantage if they are taller than their main opponent.
Listed are heights (cm) of presidents along with the heights of
their main opponents. Use the sample data with a 0.05
significance level to test the claim that for the population of
8. heights of presidents and their main opponents, the differences
have a mean greater than 0 cm.
Module 05 SummaryVariables
· Independent Samples
· Dependent Samples
· – difference between the two values in a matched pair
· – population mean of all the differences of the population
· – sample mean of all the differences in the sample data
· – sample standard deviation of all the differences in the
sample data
· – number of pairs of dataExcel Formulas
· Independent Samples
· To calculate the test statistic:
· To calculate the degrees of freedom, pick the smaller n value
and subtract 1.
· Dependent Samples
· To calculate the test statistic in Excel: “= /(/SQRT(n))”
· To calculate the degrees of freedom:
2
Allisha Langdon Rasmussen College B094 Geometry
STA3215CBE - Statistics Allisha Wise Page 7
Example 1 (Indep)Diet CokeRegular Coken136n236x-bar
10.78479x-bar 20.81682s10.00439s20.00751test statCVp-value
Example 2 (Indep)Red BackgroundBlue Backgroundn135n236x-
bar 13.39x-bar 23.97s10.97s20.63test statCVp-value
Example 3 (Indep)Wearing SeatbeltsNOT Wearing
Seatbeltsn1123n2290x-bar 10.83x-bar 21.39s11.77s23.06test
statCVp-value
Example 4 (dep)Actress (years)Actor (years)Difference
9. d28622837Mean of Differences (d-bar)3136Standard Deviation
of Differences (s_d)29383529Test StatisticCritical Valuep-value
Example 5 (dep)Height of President (cm)Height of Main
Opponent (cm)Difference d185171178180Mean of Differences
(d-bar)175173Standard Deviation of Differences
(s_d)183175193188Test Statistic173178Critical Valuep-value
DataJob TitleSalaryAccountants and Auditors67280source:
http://www.bls.gov/Actuaries100580Administrative Law Judges,
Adjudicators, and Hearing
Officers144840Calculations/ValuesFormulas/AnswersAdministr
ative Services Managers97180MeanAdult Basic and Secondary
Education and Literacy Teachers and Instructors63940Standard
DeviationAdvertising and Promotions
Managers105130nAdvertising Sales Agents51740Aerospace
Engineering and Operations Technicians57140Aerospace
Engineers115220Agents and Business Managers of Artists,
Performers, and Athletes74580Agricultural and Food Science
Technicians40060Agricultural Inspectors54140Agricultural
Sciences Teachers, Postsecondary87390Air Traffic
Controllers114906Aircraft Cargo Handling
Supervisors50380Aircraft Structure, Surfaces, Rigging, and
Systems Assemblers51410Airfield Operations
Specialists59800Airline Pilots, Copilots, and Flight
Engineers115670Anthropologists and
Archeologists51720Appraisers and Assessors of Real
Estate52870Arbitrators, Mediators, and
Conciliators86430Architects, Except Landscape and
Naval81000Architectural and Civil Drafters62210Architecture
and Engineering Occupations73850Architecture Teachers,
Postsecondary73870Archivists76749Art Directors98924Art,
Drama, and Music Teachers, Postsecondary78700Athletic
Trainers45440Atmospheric and Space
Scientists93900Atmospheric, Earth, Marine, and Space Sciences
Teachers, Postsecondary96590Audiologists97230Avionics
Technicians47320Biomedical
10. Engineers101250Boilermakers76310Broadcast News
Analysts71040Brokerage Clerks57260Budget
Analysts75940Business and Financial Operations
Occupations64880Business Operations Specialists, All
Other67980Business Teachers, Postsecondary109800Buyers and
Purchasing Agents, Farm Products62290Camera and
Photographic Equipment Repairers32280Captains, Mates, and
Pilots of Water Vessels63890Cardiovascular Technologists and
Technicians59630Career/Technical Education Teachers, Middle
School69050Career/Technical Education Teachers, Secondary
School63430Cargo and Freight Agents40910Cartographers and
Photogrammetrists72120Chefs and Head Cooks47660Chemical
Engineers87200Chemical Equipment Operators and
Tenders45460Chemical Plant and System
Operators54920Chemical Technicians50360Chemistry Teachers,
Postsecondary96330Chemists59630Child, Family, and School
Social Workers58140Chiropractors87540Civil
Engineers91430Claims Adjusters, Examiners, and
Investigators66030Clinical, Counseling, and School
Psychologists76150Coil Winders, Tapers, and
Finishers36610Commercial and Industrial
Designers66710Commercial Pilots130059Communications
Equipment Operators, All Other43160Communications
Teachers, Postsecondary85310Community and Social Service
Occupations43790Community Health
Workers37190Compensation and Benefits
Managers123570Compensation, Benefits, and Job Analysis
Specialists67210Compliance Officers67637Computer and
Information Research Scientists121310Computer and
Information Systems Managers137140Computer and
Mathematical Occupations81640Computer Hardware
Engineers95500Computer Network Architects115050Computer
Network Support Specialists70940Computer Occupations, All
Other92960Computer Programmers84280Computer Science
Teachers, Postsecondary89290Computer Systems
Analysts90600Computer User Support
11. Specialists53680Conservation Scientists67540Construction and
Building Inspectors64150Construction
Managers99150Continuous Mining Machine
Operators55330Control and Valve Installers and Repairers,
Except Mechanical Door64960Conveyor Operators and
Tenders35110Cost Estimators69480Crane and Tower
Operators53980Credit Analysts72870Credit
Counselors46720Criminal Justice and Law Enforcement
Teachers, Postsecondary66980Curators66230Database
Administrators91730Dental Hygienists71930Derrick Operators,
Oil and Gas38120Detectives and Criminal
Investigators90890Diagnostic Medical
Sonographers74340Dietitians and Nutritionists60370Directors,
Religious Activities and Education43690Drafters, All
Other51790Economics Teachers,
Postsecondary137920Economists106280Editors58820Educa tion
Administrators, All Other79960Education Administrators,
Elementary and Secondary School103570Education
Administrators, Postsecondary110110Education Administrators,
Preschool and Childcare Center/Program81590Education
Teachers, Postsecondary65020Education, Training, and Library
Occupations47920Educational, Guidance, School, and
Vocational Counselors56550Electric Motor, Power Tool, and
Related Repairers63800Electrical and Electronics
Drafters69010Electrical and Electronics Engineering
Technicians68060Electrical and Electronics Installers and
Repairers, Transportation Equipment54060Electrical and
Electronics Repairers, Commercial and Industrial
Equipment55970Electrical and Electronics Repairers,
Powerhouse, Substation, and Relay81590Electrical
Engineers91870Electrical Power-Line Installers and
Repairers67430Electricians60590Electro-Mechanical
Technicians54700Electronics Engineers, Except
Computer100610Elementary School Teachers, Except Special
Education62620Elevator Installers and
Repairers88340Embalmers48770Emergency Management
12. Directors79270Engineering Technicians, Except Drafters, All
Other63250English Language and Literature Teachers,
Postsecondary81700Environmental Engineering
Technicians56810Environmental Engineers84870Environmental
Science and Protection Technicians, Including
Health45090Environmental Science Teachers,
Postsecondary92530Environmental Scientists and Specialists,
Including Health84320Epidemiologists85620Executive
Secretaries and Executive Administrative
Assistants55770Exercise Physiologists54300Explosives
Workers, Ordnance Handling Experts, and
Blasters62910Extruding, Forming, Pressing, and Compacting
Machine Setters, Operators, and Tenders38680Farm and Home
Management Advisors38940Film and Video
Editors62280Financial Analysts85660Financial Clerks, All
Other44080Financial Examiners101500Financial
Managers142370Financial Specialists, All Other87690Fire
Inspectors and Investigators58590Firefighters49620First-Line
Supervisors of Construction Trades and Extraction
Workers82160First-Line Supervisors of Correctional
Officers84290First-Line Supervisors of Farming, Fishing, and
Forestry Workers43150First-Line Supervisors of Fire Fighting
and Prevention Workers91930First-Line Supervisors of Helpers,
Laborers, and Material Movers, Hand49590First-Line
Supervisors of Landscaping, Lawn Service, and Groundskeeping
Workers54280First-Line Supervisors of Mechanics, Installers,
and Repairers66430First-Line Supervisors of Non-Retail Sales
Workers72920First-Line Supervisors of Office and
Administrative Support Workers58120First-Line Supervisors of
Police and Detectives101240First-Line Supervisors of
Production and Operating Workers60990First-Line Supervisors
of Protective Service Workers, All Other46280First-Line
Supervisors of Transportation and Material-Moving Machine
and Vehicle Operators58250Fish and Game Wardens75430Food
Service Managers51340Foreign Language and Literature
Teachers, Postsecondary73350Forensic Science
13. Technicians79630Forest and Conservation
Technicians46640Foresters65970Forestry and Conservation
Science Teachers, Postsecondary90080Fundraisers57720Funeral
Service Managers82590Gaming Supervisors32220Gas
Compressor and Gas Pumping Station Operators62720Gas Plant
Operators70130General and Operations
Managers124190Geography Teachers,
Postsecondary82530Geological and Petroleum
Technicians39180Geoscientists, Except Hydrologists and
Geographers70730Health and Safety Engineers, Except Mining
Safety Engineers and Inspectors84880Health Diagnosing and
Treating Practitioners, All Other67650Health
Educators41781Health Specialties Teachers,
Postsecondary136670Health Technologists and Technicians, All
Other45940Healthcare Practitioners and Technical
Occupations67470Healthcare Social Workers53600Hearing Aid
Specialists46970Historians84337History Teachers,
Postsecondary88590Hoist and Winch Operators80,660Home
Economics Teachers, Postsecondary74490Human Resources
Managers112430Human Resources Specialists61460Industrial
Engineering Technicians55460Industrial
Engineers82720Industrial Machinery Mechanics55930Industrial
Production Managers100480Information and Record Clerks, All
Other45700Information Security Analysts97360Installation,
Maintenance, and Repair Occupations45990Instructional
Coordinators66810Insurance Appraisers, Auto
Damage70380Insurance Sales Agents66080 Insurance
Underwriters76990Interior Designers62010Judges, Magistrate
Judges, and Magistrates58140Kindergarten Teachers, Except
Special Education55850Labor Relations
Specialists51870Landscape
Architects68960Lawyers140920Layout Workers, Metal and
Plastic42830Legal Occupations82900Legal Support Workers,
All Other60100Librarians56320Library Science Teachers,
Postsecondary78830Life Scientists, All Other82630Life,
Physical, and Social Science Occupations62840Loading
14. Machine Operators, Underground Mining41270Loan
Officers63040Locomotive Engineers71960Logging Workers, All
Other41940Logisticians74600Magnetic Resonance Imaging
Technologists70580Management Analysts92200Managers, All
Other88600Marine Engineers and Naval Architects82410Market
Research Analysts and Marketing Specialists62380Marketing
Managers122260Marriage and Family Therapists55600Materials
Engineers91510Mathematical Science Teachers,
Postsecondary78880Mechanical Drafters58540Mechanical
Engineering Technicians60220Mechanical
Engineers92040Media and Communication Equipment Workers,
All Other76540Medical and Clinical Laboratory
Technologists65770Medical and Health Services
Managers113030Medical Equipment Repairers58310Meeting,
Convention, and Event Planners52370Mental Health
Counselors46580Metal-Refining Furnace Operators and
Tenders44990Middle School Teachers, Except Special and
Career/Technical Education66630Millwrights57190Mine
Cutting and Channeling Machine Operators46250Mine Shuttle
Car Operators56930Mining and Geological Engineers, Including
Mining Safety Engineers93920Mining Machine Operators, All
Other69160Mixing and Blending Machine Setters, Operators,
and Tenders41970Mobile Heavy Equipment Mechanics, Except
Engines58950Model Makers, Metal and Plastic57100Morticians,
Undertakers, and Funeral Directors69800Multimedia Artists and
Animators59890Music Directors and Composers46260Natural
Sciences Managers113620Network and Computer Systems
Administrators87700Nuclear Engineers121650Nuclear Medicine
Technologists79440Nuclear Technicians88770Nurse
Practitioners101960Nursing Instructors and Teachers,
Postsecondary72450Occupational Health and Safety
Specialists75610Occupational Health and Safety
Technicians61740Occupational Therapists82290Occupational
Therapy Assistants61860Operations Research
Analysts90310Optometrists111790Orthotists and
Prosthetists82380Painters, Transportation
15. Equipment45230Paper Goods Machine Setters, Operators, and
Tenders37110Paralegals and Legal
Assistants56990Patternmakers, Metal and Plastic56260Personal
Financial Advisors121750Petroleum Pump System Operators,
Refinery Operators, and
Gaugers66550Pharmacists120280Philosophy and Religion
Teachers, Postsecondary78010Physical Therapist
Assistants58720Physical Therapists90040Physician
Assistants104730Physicists118520Physics Teachers,
Postsecondary89040Plant and System Operators, All
Other56830Plumbers, Pipefitters, and
Steamfitters77570Podiatrists195730Police and Sheriff's Patrol
Officers74870Political Science Teachers,
Postsecondary90250Postal Service Clerks49310Postal Service
Mail Carriers50160Postal Service Mail Sorters, Processors, and
Processing Machine Operators49820Postmasters and Mail
Superintendents75620Power Distributors and
Dispatchers84830Power Plant Operators79100Precision
Instrument and Equipment Repairers, All Other64170Private
Detectives and Investigators58290Probation Officers and
Correctional Treatment Specialists64300Producers and
Directors75970Production, Planning, and Expediting
Clerks48390Property, Real Estate, and Community Association
Managers66710Psychologists, All Other79010Psychology
Teachers, Postsecondary89680Public Relations and Fundraising
Managers115180Public Relations Specialists63620Pump
Operators, Except Wellhead Pumpers51520Purchasing Agents,
Except Wholesale, Retail, and Farm Products61760Purchasing
Managers111380Radiation Therapists84640Radio, Cellular, and
Tower Equipment Installers and Repairers49240Radiologic
Technologists63420Rail Yard Engineers, Dinkey Operators, and
Hostlers54790Railroad Conductors and Yardmasters65740Rail -
Track Laying and Maintenance Equipment Operators54600Real
Estate Brokers88750Real Estate Sales Agents59010Recreation
and Fitness Studies Teachers, Postsecondary60080Recreational
Vehicle Service Technicians34450Refractory Materials
16. Repairers, Except Brickmasons49210Registered
Nurses71730Reinforcing Iron and Rebar
Workers86290Respiratory Therapists56910Rolling Machine
Setters, Operators, and Tenders, Metal and Plastic38060Roof
Bolters, Mining58900Rotary Drill Operators, Oil and
Gas49720Sales Engineers98760Sales Managers75432Sales
Representatives, Services, All Other61930Sales
Representatives, Wholesale and Manufacturing, Except
Technical and Scientific Products69900Sales Representatives,
Wholesale and Manufacturing, Technical and Scientific
Products81950Secondary School Teachers, Except Special and
Career/Technical Education68380Securities, Commodities, and
Financial Services Sales Agents86070Service Unit Operators,
Oil, Gas, and Mining42200Set and Exhibit Designers54620Ship
Engineers57066Signal and Track Switch Repairers38120Social
and Community Service Managers63870Social Scientists and
Related Workers, All Other79960Social Work Teachers,
Postsecondary54580Social Workers, All Other65890Sociology
Teachers, Postsecondary87710Software Developers,
Applications96110Software Developers, Systems
Software106700Soil and Plant Scientists57080Sound
Engineering Technicians58660Special Education Teachers, All
Other59400Special Education Teachers, Kindergarten and
Elementary School65430Special Education Teachers, Middle
School62160Special Education Teachers, Secondary
School68560Speech-Language Pathologists78760Stationary
Engineers and Boiler
Operators79090Statisticians88190Surveyors60215Tank Car,
Truck, and Ship Loaders45470Tax Examiners and Collectors,
and Revenue Agents79850Technical
Writers67410Telecommunications Equipment Installers and
Repairers, Except Line Installers57580Tire Builders42500Tool
and Die Makers54680Training and Development
Managers101500Training and Development
Specialists59910Transportation Inspectors86790Transportation,
Storage, and Distribution Managers93250Urban and Regional
17. Planners79510Veterinarians93830Water and Wastewater
Treatment Plant and System Operators54560Web
Developers57450Wholesale and Retail Buyers, Except Farm
Products55080Writers and Authors53050Zoologists and
Wildlife Biologists62420
Question 1
1. Describe the 8 steps in the process for hypothesis testing.
Include an explanation of the decision criteria for rejecting the
null hypothesis for both the p-value method and the critical
value method.
Question 2Calculations/ ValuesFormulas/AnswersMean (x-
bar)Standard DeviationnmuTest StatisticCritical ValueP-value
2a. Write the null and alternative hypotheses symbolically and
identify which hypothesis is the claim. Then identify if the test
is left-tailed, right-tailed, or two-tailed and explain why.
2b. Identify and explain which test statistic you will use for
your hypothesis test: z or t? Find the value of the test statistic.
Provide your calculations in the cells designated to the right.
Explain your answers below.
2c. What is the critical value? Describe the rejection region of
this hypothesis test.
Provide your calculations in the cells designated to the right.
Explain your answers below.
2d. Using the critical value approach, should you reject the null
hypothesis or not reject the null hypothesis? Explain. After
18. making your decision, restate it in non-technical terms and
make a conclusion about the original claim.
2e. Calculate the p-value for this hypothesis test, and state the
hypothesis conclusion based on the p-value. Does this match
your results from the critical value method?
Provide your calculations in the cells designated to the right.
Explain your answers below.
Question 3Calculations/ValuesFormulas/AnswersMean (x-
bar)Standard DeviationnmuTest StatisticCritical ValueP-value
3a. Write the null and alternative hypotheses symbolically and
identify which hypothesis is the claim. Then identify if the test
is left-tailed, right-tailed, or two-tailed and explain why.
3b. Identify and explain which test statistic you will use for
your hypothesis test: z or t? Find the value of the test statistic.
Provide your calculations in the cells designated to the right.
Explain your answers below.
3c. What is the critical value? Describe the rejection region of
this hypothesis test.
Provide your calculations in the cells designated to the right.
Explain your answers below.
3d. Using the critical value approach, should you reject the null
hypothesis or not reject the null hypothesis? Explain. After
making your decision, restate it in non-technical terms and
19. make a conclusion about the original claim.
3e. Calculate the p-value for this hypothesis test, and state the
hypothesis conclusion based on the p-value. Does this match
your results from the critical value method?
Provide your calculations in the cells designated to the right.
Explain your answers below.