June 2019
Beginner to intermediate
598 pages
12h 10m
English
Content preview from R Cookbook, 2nd Edition
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Chapter 11. Linear Regression and ANOVA
In statistics, modeling is where we get down to business. Models quantify the relationships between our variables. Models let us make predictions.
A simple linear regression is the most basic model. It’s just two variables and is modeled as a linear relationship with an error term:
yi = β0 + β1xi + εi
We are given the data for x and y. Our mission is to fit the model, which will give us the best estimates for β0 and β1 (see Recipe 11.1).
That generalizes naturally to multiple linear regression, where we have multiple variables on the righthand side of the relationship (see Recipe 11.2):
yi = β0 + β1ui + β2vi + β3wi + εi
Statisticians call u, v, and w the predictors and y the response. Obviously, the model is useful only if there is a fairly linear relationship between the predictors and the response, but that requirement is much less restrictive than you might think. Recipe 11.12 discusses transforming your variables into a (more) linear relationship so that you can use the well-developed machinery of linear regression.
The beauty of R is that anyone can build these linear models. The models
are built by a function, lm, which returns a model object. From the
model object, we get the coefficients (βi) and regression
statistics. It’s easy. Really!
The horror of R is likewise that anyone can build these models. Nothing requires you to check that the model is reasonable, much less statistically significant. Before you blindly believe a model, ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.