"A Chapman & Hall Book" -- title page. Includes bibliographical references (291-294 pages) and index.
Contents:
Introduction -- Estimation -- Inference -- Prediction -- Explanation -- Diagnostics -- Problems with predictors -- Problems with the error -- Transformation -- Model selection -- Shrinkage methods -- Insurance redlining-a complete example -- Missing data -- Categorical predictors -- One-factor models -- Models with several factors -- Experiments with blocks.
Summary:
"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. 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"-- Provided by publisher.
This resource is supported by the Institute of Museum and Library Services under the provisions of the Library Services and Technology Act as administered by State Library of Iowa.