Part 1 - Linear Regression
Read Lecture Eleven. Lecture Eleven provides information showing a strong positive correlation and a significant linear regression existed between the individual's salary and midpoint (used as a substitute for grade). This is not an unexpected outcome in a company. How useful are these in understanding what drives salary differences? Why? What examples of a linear regression might be useful in your personal or professional lives? Why? (This should be started on Day 3.)
Part 2 - Multiple Regression
Read Lecture Twelve. In Lecture Twelve, a multiple-regression equation was developed that showed the factors that influenced a person's salary and-almost as important-factors that did not influence salary. How do we interpret a multiple-regression equation? Pick one of the factors-whether statistically significant or not-used in the analysis, and describe its impact on salary, what the coefficient is and what it means, what its significance is, and whether you expected this outcome or not. (This should be completed by Day 5.)