Part 1Write a 250- to 300-word response to the following?Why are measures of variability important when interpreting data?Use at least 1 scholarly source and cite in APA Format.Part 2 Respond in 150-200 words.The devotion our difference is a great thing to know. The difference and if it is significant or not is an important aspect of research. this could be done by performing many different tests. Do you know what we would run to find out the variance?Part 3Write a 250- to 300-word response to the following:How do you choose which measure of variation to use and what considerations may have an impact on your decision?Use at least 1 scholarly source.Part 4Respond in 150-200 wordsThe type of research and the data being analyzed will affect the appropriate measure. Do you think they could be interchanged, or do you think the wrong measure would screw up a study?

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. InstructionsUse 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 (Example Only - Not DAX 5 results) that must be submitted to prove a Regression Analysis was conducted. Example Regression Results Tables.pdfPreview the documentRun 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. AttachmentsDAX 5 Video (Links to an external site.)DAX 5 Memo 2020.docxPreview the documentDeliverableBusiness MemoRubricDAX5 (Fall 2020)DAX5 (Fall 2020)Criteria Ratings PtsThis criterion is linked to a Learning OutcomeBusiness Memo submitted on time.10.0 ptsFull Marks0.0 ptsNo Marks10.0 ptsThis criterion is linked to a Learning OutcomeCorrect salutation, date, subject line, etc.5.0 ptsFull Marks0.0 ptsNo Marks5.0 ptsThis criterion is linked to a Learning OutcomeParagraph 2 provides an explanation of any steps and discusses if the linear model is appropriate to run on this data. Tables and graphs should be referenced.20.0 ptsFull Marks15.0 ptsMostly Correct10.0 ptsPartially Correct5.0 ptsMostly Incorrect0.0 ptsNo Marks20.0 ptsThis criterion is linked to a Learning OutcomeAppropriate tables and graphs. This should include graphs/plots to verify the assumptions, a summary table, the parameter estimates table, and a correlation table.20.0 ptsFull Marks15.0 ptsMostly Correct10.0 ptsPartially correct5.0 ptsMostly incorrect0.0 ptsNo Marks20.0 ptsThis criterion is linked to a Learning OutcomeParagraph 3 summarizes the results discussing whether the model is significant, states which parameter estimates are significant and interprets them, discusses the root MSE, R-Squared, and if the results are meaningful. Tables and graphs should be referenced. Comparisons should be made between the DAX 4 and DAX 5 model and between a Model with and without height.25.0 ptsFull Marks19.0 ptsMostly Correct13.0 ptsPartially Correct7.0 ptsMostly Incorrect0.0 ptsNo Marks25.0 ptsThis criterion is linked to a Learning OutcomeParagraph 4 is a summary Paragraph with a conclusion, a recommendation, and the impact of the recommendation.The conclusion is the plain language answer to the research question. It should also provide a recommendation to include position 2 in the model or not and for what action to take against a potential lawsuit for height discrimination.20.0 ptsFull Marks15.0 ptsMostly Correct10.0 ptsPartially Correct5.0 ptsMostly incorrect0.0 ptsNo Marks20.0 ptsTotal Points: 100.0

A rehabilitation center researcher was interested in examining the relationship between physical fitness prior to surgery of persons undergoing corrective knee surgery and time required in physical therapy until successful rehabilitation. Patient records in the rehabilitation center were examined, and 24 male subjects ranging in age from 18 to 30 years who had undergone similar corrective knee surgery during the past year were selected for the study. The number of days required for successful completion of physical therapy and the prior physical fitness status (below average, average, above average) for each patient follow.(1) Using SAS to test whether the mean number of days for successful completion of physical therapy differ significantly among the three groups. (clarify your notation, specify hypothesis, test statistics, rejection region or p-value, and your conclusion).(2) Write down your fitted model. What do the estimated coefficients represent? (Hint: beta0 is mean of reference group, etc.)Note: Group 1 has 8 individuals, group 2 has 10 and group 3 has 6 individuals. To read in the data, you’ll need to organize the data in the format like EX1701.DAT (or page 5 of slides “onewayANOVA-2020.ppt”) as following :Group daysBA 29BA 42BA 38…AV 30AV 35…