Linear regression is a topic that one will encounter many times and in many different places in data science as a didactic precursor to more advanced techniques. If you're actually going to use linear regression in practice, you should have a dedicated resource, as there are many practical complications and solutions to those complications that you will not learn from a more general book.
Recommended Books

Introduction to Linear Regression Analysis
Douglas C. Montgomery, Elizabeth A. Peck, G. Geoffrey Vining
Key Features
 Intext exercises
 Solution manual available
 R and SAS code examples
Key Topics
 BoxCox Transformation
 Confidence Intervals
 Diagnostics
 Generalized Linear Models
 Hypothesis Testing
 LeastSquares Estimation
 Leverage and Influence
 Logistic Regression
 MaximumLikelihood Estimation
 Model Adequacy Checking
 Model Validation
 Multicollinearity
 Multiple Linear Regression
 Nonlinear Regression
 Nonparametric Regression
 Outliers
 PRESS Statistic
 Poisson Regression
 Polynomial Regression
 Prediction
 Random Regressors
 Residual Analysis
 Robust Regression
 Simple Linear Regression
 Time Series
 Transformations
 Variable Selection
 VarianceStabilizing Transformations
 Weighted LeastSquares
Description
This book gives a fairly standard introduction to simple and multiple linear regression, and then it devotes most of the text to dealing with their practical problems. Detecting and dealing with multicolinearity and outliers as well as many diagnostics and other practical topics occupy the majority of the book. Generalized linear models are introduced, but they really need their own treatment (we recommend some here ).