Simple Regression Analysis

Case Study Project: Simple Regression AnalysisConsider you are creating a simple report. Your report includes simple regression analysisusing the data given below. With the appropriate outputs, you have to analyze and answer allquestions. Your report should be typed with the appropriate output copied in your report. youranswers and analyses should be supported by either hypothesis tastings, appropriate residualoutput analyses, and graph or plot outputs analyses. The new model output must be submittedas well with your analysis. Both regression outputs should be attached as an appendix in yourpaper. When you answer each question, clearly address based on what output you draw thatconclusion and show that output in your report.Submission: A Report and an Excel Data file with Megastat OutputsGoal & PurposeCEO paid more when the company performs better? This is a normal belief. However, is realdata support that hypothesis and the belief. By performing a simple regression analysis, wemay conclude the belief. Using the data and regression output to answer the followingquestions. First, use CEO Compensation and Returns to construct the simple regressionmodel.Data Description: (Compensation: CEO compensation in 1000s 198 CEOs from S&P 500 CompaniesLog Comp: log of CEO compensation in 1000sReturns (in %): Total shareholders returns – S& P 500 average in percent term (+ if the firmperforms above the S&P 500 average and if the firm performs below the S&P 500 average.)Consideration: This data is outdated. Recent data for S&P 500 companies can be different. Ifyou are interested in this matter, you can collect more recent data to do the same analysis.a) Write down the estimated model equation and interpret its coefficients in the context of thevariable.b) Estimated the CEO compensation for a companies’ returns with 12% using the estimatedmodel. (it means that this companys shareholder returns were 12% above the S&P 500average)c) What is the value of 2 ? Describe the fit of the model using the 2 

d) Is this regression model statistically significant? (To answer this question, you have to doan overall model test.)e) Are the coefficients of the model statistically significant? (To answer this question, youneed to do the individual t-tests for each coefficient) 
f) Discuss/analyze leverage, unusual observations, and outliers if there are any

g) Analyze three assumptions of the regression model using the outputs. State clearly whatoutput is used to test what assumption and draw that conclusion and how to draw thatconclusion. Your output that is used should be copied and pasted in your answer as well.In the data file, Logcomp is the log transformation of our Compensation data. Now useLogcomp as your new dependent variable and Returns as an independent variable to run theregression again.
 h) Using this output discuss f) and g) again. Compare and contrast the old model with a newmodel (log comp vs returns)

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