Multiple Regression: Case Problem Predicting Winnings for NASCAR Drivers

Anderson, D. R., Sweeney, D. J., Williams, T. A., Camm, J. D., Cochran, J. J., Fry, M. J., & Ohlmann. J. W. (2021). Essentials of modern business statistics with Microsoft® Excel® (8th ed.). Cengage Learning.

Run a Regression for the Data File NASCAR (Chapter 15) using the video How to Add Excel’s Data Analysis ToolPak (Links to an external site.) for assistance.
In a managerial report,

Suppose you wanted to predict Winnings ($) using only the number of poles won (Poles), the number of wins (Wins), the number of top five finishes (Top 5), or the number of top ten finishes (Top 10). Which of these four variables provides the best single predictor of winnings?
Develop an estimated regression equation that can be used to predict Winnings ($) given the number of poles won (Poles), the number of wins (Wins), the number of top five finishes (Top 5), and the number of top ten (Top 10) finishes. Test for individual significance, and then discuss your findings and conclusions.
Step 3: Discuss:

What did you find in your analysis of the data? Were there any surprising results? What recommendations would you make based on your findings? Include details from your managerial report to support your recommendations.

SAMPLE SOLUTION

The best way to determine which of the variables gives the best single predictor of the winnings is to do the correlation analysis that gives the correlation coefficient or the regression between the dependent and independent variables. From the analysis, top 10 gives the highest correlation coefficient showing that it gives the singe best winnings. The regression analysis is provided in the appendix section. The format for the estimated regression equation can be given as:
ŷ = bo+ b1x1+b2x2+…+bpxp
In the equation, ŷ represents the results for the dependent variable Winnings ($), bo is the value intercept correlation, b1 gives the correlation value of the poles, b2 is the correlation value representing the wins while the reset represents the correlation value of the top 5 and the top 10. Therefore, substituting the values given in the regression analysis outlined in the appendices section, the final equation can be given as:
ŷ = 3,140,367.09 + (-12,938.92) x1 + 13,544.81×2 + 71,629.39×3 + 117,070.58×4
There are a number of findings that can be underscored from the presented data analysis for the evaluated case study. The first conclusion is that both of the regression…