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Linear Models with Python

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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.

Author(s): Julian J. Faraway
Publisher: Chapman and Hall/CRC
Year: 2020

Language: English
Pages: 308
Tags: Statistics, Linear Models, Python

1.Introduction
2.Estimation
3.Inference
4.Prediction
5.Explanation
6.Diagnostics
7.Problems with the Predictors 8.Problems with the Errors
9.Transformation
10.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