Data Science Texts

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Linear Regression

Strongly Recommended Prerequisites

Recommended Prerequisites

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

  1. Introduction to Linear Regression Analysis

    Douglas C. Montgomery, Elizabeth A. Peck, G. Geoffrey Vining

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    Key Features

    • In-text exercises
    • Solution manual available
    • R and SAS code examples

    Key Topics

    • Box-Cox Transformation
    • Confidence Intervals
    • Diagnostics
    • Generalized Linear Models
    • Hypothesis Testing
    • Least-Squares Estimation
    • Leverage and Influence
    • Logistic Regression
    • Maximum-Likelihood 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
    • Variance-Stabilizing Transformations
    • Weighted Least-Squares


    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 ).