Summary
In this chapter, we discussed an overview of simple linear regression between one explanatory variable and one response variable. The topics we covered include the following:
- The OLS method for simple linear regression
- Coefficients of correlation and determination and their calculations and significance
- The assumptions required for least squares regression
- Methods of analysis for model and parameter significance
- Model validation
We looked closely at the concept of the square of error and how the sum of squared errors is meaningful for building and validating linear regression models. Then, we walked through the four pertinent assumptions required to make linear regression a stable solution. After, we provided an overview of four diagnostic plots and their interpretations with respect to assessing the presence of various issues related to heteroscedasticity, linearity, outliers, and serial correlation. We then walked through an example of using...
