Linear models with python

Table of Contents
1.Introduction 2.Estimation 3.Inference 4.Prediction 5.Explanation 6.Diagnostics 7.Problems with the Predictors 8.Problems with the Errors 9.Transformation10.Model Selection 11.Shrinkage Methods 12.Insurance Redlining —A Complete Example 13.Missing Data 14.Categorical Predictors 15.One Factor Models 16.Models with Several Factors 17.Experiments with Blocks 18.About Python

Like its widely praised, best-selling companion version, Linear Models with R, this book replaces R with Python to seamlessly give a coherent exposition of the practice of linear modeling. Linear Models with Python offers up-to-date insight on essential data analysis topics, from estimation, inference and prediction to missing data, factorial models and block designs. Numerous examples illustrate how to apply the different methods using Python.

Features:

Python is a powerful, open source programming language increasingly being used in data science, machine learning and computer science. Python and R are similar, but R was designed for statistics, while Python is multi-talented.
This version replaces R with Python to make it accessible to a greater number of users outside of statistics, including those from Machine Learning.
A reader coming to this book from an ML background will learn new statistical perspectives on learning from data.
Topics include Model Selection, Shrinkage, Experiments with Blocks and Missing Data.
Includes an Appendix on Python for beginners.
Linear Models with Python explains how to use linear models in physical science, engineering, social science and business applications. It is ideal as a textbook for linear models or linear regression courses.