In Chapter 7, Exploring Association Rules with Apriori, we examined association rules with apriori
. In the previous chapter, we have notably examined statistical distribution and the relationships between two attributes using several measures of association. These didn't infer any causation between the attributes, only dependence. If we have normally distributed attributes and want to examine how one attribute affects another attribute, we can rely on simple linear regression instead. If we want to examine how several attributes affect an attribute, we can rely on multiple linear regressions.
In this chapter, we will notably:
Build and use our own simple linear regression algorithm
Create multiple linear regression models in R
Perform diagnostic tests of such models
Score new data using a linear regression model
Examine how well the model predicts the new data
Have a quick look at robust regression and bootstrapping