BUS 308 Week 5 DQ 2 Regression

At times we can generate a regression equation to explain outcomes.  For example, an employee’s salary can often be explained by their pay grade, appraisal rating, education level, etc.  What variables might explain or predict an outcome in your department or life?  If you generated a regression equation, how would you interpret it and the residuals from it?

“Regression allows a precise mathematical prediction of the value of one variable from the value of another with which it is correlated” (Tanner, 2013).  Some variables that might predict an outcome in my life is having a college degree and the zip code I live in.  For example, my college degree might predict my pay grade and job title.  The area I live in might predict if I have/will have a college degree or not and what my income will likely be around or even how many kids I have/will have.  “The objective is to build a regression model that can describe, predict, and control the dependent variable based on the independent variable” (Sheedy, 2013).   If I were to build a regression model around my zip code and the number of children I could interpret the results of living in X zip code where one can reasonably estimate how many children I have/will have, but only if there is a correlation between zip code and children in a household.

References

Sheedy, Jason. (2013). Instructor Guidance. Statistics for Managers. Ashford University.

Tanner, D. E., & Youssef–Morgan, C. M. (2013).  Statistics for Managers. San Diego, CA: Bridgepoint Education, Inc.