Regression modeling represents a powerful and elegant method for estimating the value of a continuous target variable. In this chapter, we introduce regression modeling through simple linear regression, where a straight line is used to approximate the relationship between a single continuous predictor variable and a single continuous response variable. Later, in Chapter 9, we turn to multiple regression, where several predictor variables are used to estimate a single response.
To develop the simple linear regression model, consider the Cereals data set,1 an excerpt of which is presented in Table 8.1. The Cereals data set contains nutritional information for 77 breakfast cereals, and includes the following variables:
Table 8.1 Excerpt from Cereals data set: eight fields, first 16 cereals
|100% Natural Bran||Q||8||120||3||5||15||33.9837|