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Intermediate Statistics with R Mark Greenwood

By: Contributor(s): Material type: TextTextSeries: Open textbook libraryDistributor: Minneapolis, MN Open Textbook LibraryPublisher: Bozeman, Montana Montana State University [2021]Copyright date: ©2021Description: 1 online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
Subject(s): LOC classification:
  • QA1
  • QA37.3
  • QA273-280
Online resources:
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
Subject: 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.
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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

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.

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In English.

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