Introduction
Regression is the process of fitting models to data. The process
depends on the model. If a model is parametric, regression estimates
the parameters from the data. If a model is linear in the parameters,
estimation is based on methods from linear algebra that minimize the
norm of a residual vector. If a model is nonlinear in the parameters,
estimation is based on search methods from optimization that minimize
the norm of a residual vector. Nonparametric models, like Regression Trees, use methods all their
own.
This chapter considers data and models with continuous predictors
and responses. Categorical predictors are the subject of Analysis of Variance. Categorical responses
are the subject of Classification.
 | Regression Analysis | | Linear Regression |  |
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