In this exercise the students will compare a model that only contains position 1 to the regression model from DAX 4. They will then use this new model to test if the company discriminates by height. The students may refer to previous instructions for more details on how to run the analysis in SAS. A video is attached that demonstrates how to compare similar results. The conclusions will likely be the same as in the video, but the numbers will likely differ slightly. YOU MUST HAVE THE CORRECT CORRESPONDING NUMBERS TO YOUR DATA TO RECEIVE CREDIT.
This lab is meant to emphasize the evaluation of the model accuracy. There is a trend towards the increased use of machine learning which emphasizes model evaluation vs testing statistical samples. Regression is a commonly used model in machine learning.
Instructions
Use your labor data set to create a data set that contains only position 1.
Use this data set to run all analysis to test assumptions (summary descriptive statistics with max min, mean, etc, Regression Analysis Results Table that shows F, Pr, and R-square you produced in your analysis, correlation analysis ,and scatter plots of continuous variables (height, years of employment, and performance)
Note: An Example of Regression Analysis Results Table is in following link below (Example Only – Not DAX 5 results) that must be submitted along with all the other tables and figures created for DAX 5 to prove that a Regression Analysis was conducted: Example Regression Results Tables-2.pdfDownload Example Regression Results Tables-2.pdf
Run a linear regression of $/hr in SAS using Location as a classification variable and with performance and years of employment as continuous variables.
Run a linear regression of $/hr in SAS using Location as a classification variable and with height, performance, and years of employment as continuous variables.
Use the outputs to complete the memo. NOTE THE ASSUMPTION TESTS CAN BE LIMITED TO THE MODEL WITH HEIGHT.
Attachments
DAX 5 VideoLinks to an external site.
DAX 5 Memo 2020.docx
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