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R Statistics Cookbook

You're reading from   R Statistics Cookbook Over 100 recipes for performing complex statistical operations with R 3.5

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Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781789802566
Length 448 pages
Edition 1st Edition
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Author (1):
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Francisco Juretig Francisco Juretig
Author Profile Icon Francisco Juretig
Francisco Juretig
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Table of Contents (12) Chapters Close

Preface 1. Getting Started with R and Statistics FREE CHAPTER 2. Univariate and Multivariate Tests for Equality of Means 3. Linear Regression 4. Bayesian Regression 5. Nonparametric Methods 6. Robust Methods 7. Time Series Analysis 8. Mixed Effects Models 9. Predictive Models Using the Caret Package 10. Bayesian Networks and Hidden Markov Models 11. Other Books You May Enjoy

Introduction

Unfortunately, parametric methods such as the t-test or ordinary least squares (OLS), make very strong assumptions about the distribution of the data. To some extent, they still work if the distributional assumptions are relaxed, but it really depends to which extent these assumptions are violated.

Nonparametric methods do not work with the usual parametrized distributions and are instead designed to work with any distribution. This gives them a distinct flexibility, and we are no longer required to check any distributional assumption on the data. If the data follows the same distribution that its parametric counterpart requires, they usually perform almost as well.

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