In Problem 14.6, you used full-time voluntary turnover(%), and total worldwide revenue ($billions) to predict number of full-time job openings (stored in Best Companies).
Develop a regression model to predict the number of full-time job openings that includes full-time voluntary turnover, total worldwide revenue, and the interaction of full-time voluntary turnover and total worldwide revenue.
a. At the 0.05 level of significance, is there evidence that the interaction term makes a significant contribution to the model?
b. Which regression model is more appropriate, the one used in this problem or the one used in Problem 14.6? Explain.

Problem 14.6

Human resource managers face the business problem of assessing the impact of factors on full-time job growth. A human resource manager is interested in the impact of full-time voluntary turnover a nd total worldwide revenues o n the number of full-time job openings at the beginning of a new year. Data are collected from a sample of 63" best companies to work for." The total number of full-time job openings as of February 2017 , the full-time voluntary turnover in the past year (in%), and the total worldwide revenue (in $ billion s) are recorded and stored in Best Companies.

a. State the multiple regression equation .
b. Interpret the meaning of the slopes, b₁ and b₂ , in this problem.
c. Interpret the meaning of the regression coefficient, b₀.
d. Which factor has the greatest effect on the number of full-time jobs added in the last year? Explain.