Unit 5 Assignment: Power Testing

Unit Outcomes:

Distinguish between valid and invalid analytical models based on power analysis.

Test power analysis models for correlation, ANOVA and t-tests.

Interpret the results of power tests.

Course Outcome:

IT527-4: Construct useable and effective data analytics models incorporating industry-recognized software and standard algorithms.

Purpose

This Assignment will enable you to practice using R Studio to conduct power testing on t-tests, ANOVAs, and correlations in order to determine the minimum sample sizes needed to conduct valid analytics using these methodologies.

Assignment Instructions

The Unit 5 Assignment will give you an opportunity to practice some of the analytics skills you learned in your Reading this week, and also to reflect on that learning. To fulfill the Unit 5 Assignment, complete the following steps:

Download the Loan Applicants comma separated values (CSV) file from Course Documents. Import the data into a new data frame called Loans in R Studio. Place a screenshot into a Word document showing that you have imported the data successfully. Label this screenshot appropriately.

You need to determine whether or not the Loan Applicants data set has a large enough sample size to determine whether or not the Monthly Income variable can be used to determine whether or not a loan application will be approved. Assume that a difference of \$500 per month in income will influence whether or not a loan will be approved. Use the ‘pwr’ function to determine whether or not the Loan Applicants data set has a large enough sample to have 95% confidence that Monthly Income really does influence the Make Loan decision, and 99% confidence that the t-test will not accidentally say that Monthly Income makes a difference when it really does not. Take a screenshot of your power analysis and place it in your Word document. Label it, and then explain whether or not the Loan Applicants data set has enough observations, and specifically explain how you know.

You need to understand if there is enough power in a model that checks for a significant difference between groups of loan applicants based on the number of lines of credit each person has. Assume that the effect size of all of the variables in the data set on the number of lines of credit each applicant has is 0.3. Use the ‘pwr’ function to determine whether or not the Loan Applicants data set has large enough samples at each number of lines of credit to have 95% confidence that an ANOVA analysis of the data really does indicate meaningful differences between groups of credit lines, and 99% confidence that the ANOVA will not accidentally say that number of lines of credit makes a difference when it really does not. Take a screenshot of your power analysis and place it in your Word document. Label it, and then explain whether or not the Loan Applicants data set has enough observations in each group of lines of credit, and specifically explain how you know.

You need to understand if there is enough power in a model that checks for strength of relationship between all of the attributes except Applicant ID and Make Loan. Assume that the effect size of all of these variables is .25. Use the ‘pwr’ function to determine whether or not the Loan Applicants data set has a large enough sample to have 95% confidence that a correlational analysis of the data really does indicate meaningful relationships between attributes, and 99% confidence that the correlation will not accidentally say that there really are significant relationships between attributes when there really are not. Take a screenshot of your power analysis and place it in your Word document. Label it, and then explain whether or not the Loan Applicants data set has enough observations for correlational analysis, and specifically explain how you know.

Assignment Requirements

Prepare your Assignment submission in Microsoft Word following standard APA formatting guidelines: Double spaced, Times New Roman 12-point font, and one-inch margins on all sides. Include a title page, table of contents, and references page. You do not need to write an abstract. Label all tables and figures. Cite sources appropriately both in the text of your writing (parenthetical citations) and on your references page (full APA citation format).

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