Prompt: The board of directors at a large corporation wants to base their divisi

Question

Prompt: The board of directors at a large corporation wants to base their division managers' pay raises on the profit performance of their respective divisions. They have asked you to evaluate the performance and raises at other companies and propose a formula for calculating the percentage increase in base pay based on the percentage change in the division's profit. You collected information from 50 divisions at similar companies and performed a linear regression on the percentage change in the division profits vs. the percentage change in the manager’s salary. Use what you have learned about linear regression to answer the following questions. Click here to download the output from the Excel ToolPak, Regression Tool. Response Parameters What is the regression equation from the Summary Output? Is this a useful model? How do you know? Are the assumptions of regression satisfied? How did you verify them? Does change in division profit appear to be a good predictor for the manager’s pay raise? Why do you think that? One of your company’s divisions had a –0.51 percent change in profits last year, while another had a 20 percent increase. What is the predicted percentage change in salary for these two division managers?

Explanation / Answer

I am not sure what do you expect me to explain without the data set but let me give you an understanding of linear regression using following syntax in R. I hope this will help you with the given problem.

data_file <- read.csv(“file path”, sep=“,”) ## you need to fill the file path with the directory to your data set

fit <- lm(Y~X1+X2+X3 , data = data_file) ## you can add any many variables as you want, i am just taking here 3. You ## need to check the significance level of each parameters and remove the less significant one to perform the ## regression again to give better results.

summary(fit)

Summary gives us the p value attached to the coefficient population and intercept which helps us to determine the significance of parameter in contributing to our model. High p value means low significance.

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