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Read more about Intermediate Statistics with R

Intermediate Statistics with R

(3 reviews)

Mark C. Greenwood, Montana State University

Copyright Year: 2021

Publisher: Montana State University

Language: English

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CC BY-NC

Reviews

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Reviewed by Wei Wei, Professor, Metropolitan State University on 2/23/24

The text covered both analysis of variance and regression, the two major areas in statistics, along with some categorical analysis method, Chi-square tests. The author introduced each chapter in detailed approaches. The author even wrote their own... read more

Reviewed by Eric Smith, Professor, Virginia Tech on 3/6/22

The text covers material for a one-semester long class on applied statistics. I teach a course on biological statistics and it covers most of the areas that I teach in my course. Some topics I spend some time on are slightly covered – ANCOVA,... read more

Reviewed by Evidence Matangi, Assistant Professor, Taylor University on 11/15/21

R introduction is concise, data sets introduction is clear with objectives formulating the the questions to answer from the data. Plots for categorical variables would compliment those for quantitative variables. Grammar of graphics is condensed,... read more

Table of Contents

  • 1 Preface 
  • 2 (R)e-Introduction to statistics
  • 3 One-Way ANOVA 
  • 4 Two-Way ANOVA 
  • 5 Chi-square tests 
  • 6 Correlation and Simple Linear Regression
  • 7 Simple linear regression inference
  • 8 Multiple linear regression
  • 9 Case studies

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About the Book

Introductory statistics courses prepare students to think statistically but cover relatively few statistical methods. Building on the basic statistical thinking emphasized in an introductory course, a second course in statistics at the undergraduate level can explore a large number of statistical methods. This text covers more advanced graphical summaries, One-Way ANOVA with pair-wise comparisons, Two-Way ANOVA, Chi-square testing, and simple and multiple linear regression models. Models with interactions are discussed in the Two-Way ANOVA and multiple linear regression setting with categorical explanatory variables. Randomization-based inferences are used to introduce new parametric distributions and to enhance understanding of what evidence against the null hypothesis “looks like”. Throughout, the use of the statistical software R via Rstudio is emphasized with all useful code and data sets provided within the text. This is Version 3.0 of the book.

About the Contributors

Author

Mark C. Greenwood, Montana State University
 

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