In multiple regression, the relative size of the coefficients is not important. For example, your company may have a nationwide hiring program that focuses on hiring employees who have graduated from college in the past 3 years, and let’s say you want to know what attributes of those graduates has the biggest influence on sales ($M) in their first year on the job. You hypothesize that the factors that will influence first year sales to be undergraduate GPA (GPA), years of experience since graduation (EXP), the quality of their undergraduate institution (RANK), and their performance on the Wonderlic test (TEST). You estimate the regression as:
It is difficult to compare the size of the various coefficients because each of the independent variables is measured on a different scale. Undergraduate GPA is measured on a scale from 0.0 to 4.0. Experience ranges from 0 to 3. University ranking ranges from 1 to 4 (with 1 being the highest rank), and the Wonderlic test ranges from 0 to 50. Can you think of a way to compare the coefficients? If you are going to take this information to make a decision of where to focus your hiring, which element should you place the highest emphasis on?
The solution is comprised of detailed step-by-step calculations and explanation of the given problems related to Correlation and Regression Analysis. This solution provides students with a clear perspective of the underlying statistical aspects.