In this chapter, we learned how to perform an accurate regression analysis in a MATLAB environment. First, we explored simple linear regression, how to define it, and how to get an OLS estimation. Then, we looked at several methods of measuring the intercept and slope of a straight line.
Next, we discovered the linear regression model builder; it creates an object inclusive of training data, model description, diagnostic information, and fitted coefficients for a linear regression. Then, we understood how to correctly interpret the results of the simulation and how to reduce outlier effects with robust regression.
So, we explored multiple linear regression techniques; several functions were analyzed to compare the relative results. We learned how to create models with response variables that depend on more than one predictor. Thus, we resolved a multiple linear regression with a categorical predictor example.
With polynomial regression, we approached a model in which some predictors...