Predicting Nascar Winnings Through Regression Analysis
Step 1: Read
Review Case Problem 2: Predicting Winnings for NASCAR Drivers Download Case Problem 2: Predicting Winnings for NASCAR Drivers from Chapter 15 of the ebook.
Step 2: Do
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.
Report: Predicting Winnings
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Report: Predicting Winnings
From the regression analysis, the single variable that predicts winning in NASCAR is the number of top ten finishes reported by the driver throughout the championship. In a model exploring the influence of the number of poles won, the number of wins, top-five finishes, and the overall number of top ten finishes on winning, only finishes in the top 10 had a statistically significant contribution to the model.
The tests for individual significance for the model indicate that only the top 10 (p=0.00147) had a significant level that was below the critical alpha of 0.05. The number of poles won (p=0.9047), number of wins (0.9039), and the number of top 5 wins (p=0.1677) ha...
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