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(Ebook PDF) Statistical Regression and Classification From Linear Models to Machine Learning 1st Edition by Norman Matloff 1498710916 9781498710916 Full chapter

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Statistical Regression and Classification From Linear Models to Machine Learning 1st Edition Norman Matloff Digital Instant Download

Author(s): Norman Matloff
ISBN(s): 9781498710916, 1498710913
Edition: 1
File Details: PDF, 4.61 MB
Year: 2017
Language: english
SKU: EB-6838814 Category: Tag:

Statistical Regression and Classification From Linear Models to Machine Learning 1st Edition by Norman Matloff – Ebook PDF Instant Download/Delivery: 1498710916, 978-1498710916
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Product details:

ISBN 10:         1498710916
ISBN 13: 978-1498710916
Author:          Norman Matloff

This text provides a modern introduction to regression and classification with an emphasis on big data and R. Each chapter is partitioned into a main body section and an extras section. The main body uses math stat very sparingly and always in the context of something concrete, which means that readers can skip the math stat content entirely if they wish. The extras section is for those who feel comfortable with analysis using math stat.
 

Statistical Regression and Classification From Linear Models to Machine Learning 1st Table of contents:

Linear Regression Models

  • Simple and multiple linear regression.
  • Assumptions and diagnostics in linear regression analysis.

Generalized Linear Models

  • Extending linear regression to handle non-normal data distributions.
  • Logistic and Poisson regression models.

Nonparametric Models

  • Approaches that do not rely on strict assumptions about the underlying data distribution.
  • Methods like kernel regression and decision trees.

Model Parsimony

  • The principle of using the simplest model that adequately explains the data.
  • Techniques for model selection and comparison.

Use of Regression for Understanding

  • Leveraging regression models to interpret relationships in data.
  • Methods for hypothesis testing and interpretation of coefficients.

Large Data

  • Addressing challenges of regression analysis with large datasets.
  • Computational techniques and software tools for big data.

Miscellaneous Topics

  • Other advanced topics in regression and model fitting.

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Norman Matloff,Statistical Regression,Classification,Linear Models,Machine Learning 1st