An R companion for applied statistics I: basic bivariate techniques
Rasco, Danney
An R companion for applied statistics I: basic bivariate techniques - Thousand Oaks Sage Publications 2021 - xv, 234p.: ill. Includes references
Table of Contents
Chapter 1: Introduction: What is R?
Chapter 2: Basic Tasks in R
Chapter 3: Frequency Tables
Chapter 4: Descriptive Statistics
Chapter 5: Visualizing Data: Bar Charts, Histograms, and Boxplots
Chapter 6: Evaluating Score Locations: Introducing the Normal Distribution and z Scores
Chapter 7: Sampling Error and Confidence Intervals
Chapter 8: One-Sample t-Test: Introduction to Statistical Significance Tests
Chapter 9: Significance Tests Continued: Effect Size and Power
Chapter 10: Bivariate Pearson Correlation
Chapter 11: Bivariate Regression
Chapter 12: Independent-Samples t-Test
Chapter 13: One-Way Between-Subjects Analysis of Variance
Chapter 14: paired-samples t-Test
Chapter 15: One-Way Repeated-Measures Analysis of Variance
Chapter 16: Factorial Analysis of Variance
Chapter 17: Chi-Square Test of Independence
Chapter 18: Parting THoughts About R
An R Companion for Applied Statistics I: Basic Bivariate Techniques breaks the language of the R software down into manageable chunks in order to help students learn how to use it. R is a powerful, flexible, and free tool. However, the flexibility—which eventually becomes a great asset—can make the initial learning curve appear steep. This book introduces a few key aspects of the R tool. As readers become comfortable with these aspects, they develop a foundation from which to more thoroughly explore R and the packages available for it. This introduction does not explain every possible way to analyze data or perform a specific type of analysis. Rather, it focuses on the analyses that are traditionally included in an undergraduate statistics course and provide one or two ways to run these analyses in R. Datasets and scripts to run the examples are provided on an accompanying website. The book has been designed to be an R companion to Warner's Applied Statistics I, Third Edition, and includes end-of-chapter instructions for replicating the examples from that book in R. However, this text can also be used as a stand-alone R guide, without reference to the Warner text.
https://in.sagepub.com/en-in/sas/an-r-companion-for-applied-statistics-i/book271882
9781071806319
Commercial statistics
R computer programming language
Statistics
519.502855133 / R2R2-I
An R companion for applied statistics I: basic bivariate techniques - Thousand Oaks Sage Publications 2021 - xv, 234p.: ill. Includes references
Table of Contents
Chapter 1: Introduction: What is R?
Chapter 2: Basic Tasks in R
Chapter 3: Frequency Tables
Chapter 4: Descriptive Statistics
Chapter 5: Visualizing Data: Bar Charts, Histograms, and Boxplots
Chapter 6: Evaluating Score Locations: Introducing the Normal Distribution and z Scores
Chapter 7: Sampling Error and Confidence Intervals
Chapter 8: One-Sample t-Test: Introduction to Statistical Significance Tests
Chapter 9: Significance Tests Continued: Effect Size and Power
Chapter 10: Bivariate Pearson Correlation
Chapter 11: Bivariate Regression
Chapter 12: Independent-Samples t-Test
Chapter 13: One-Way Between-Subjects Analysis of Variance
Chapter 14: paired-samples t-Test
Chapter 15: One-Way Repeated-Measures Analysis of Variance
Chapter 16: Factorial Analysis of Variance
Chapter 17: Chi-Square Test of Independence
Chapter 18: Parting THoughts About R
An R Companion for Applied Statistics I: Basic Bivariate Techniques breaks the language of the R software down into manageable chunks in order to help students learn how to use it. R is a powerful, flexible, and free tool. However, the flexibility—which eventually becomes a great asset—can make the initial learning curve appear steep. This book introduces a few key aspects of the R tool. As readers become comfortable with these aspects, they develop a foundation from which to more thoroughly explore R and the packages available for it. This introduction does not explain every possible way to analyze data or perform a specific type of analysis. Rather, it focuses on the analyses that are traditionally included in an undergraduate statistics course and provide one or two ways to run these analyses in R. Datasets and scripts to run the examples are provided on an accompanying website. The book has been designed to be an R companion to Warner's Applied Statistics I, Third Edition, and includes end-of-chapter instructions for replicating the examples from that book in R. However, this text can also be used as a stand-alone R guide, without reference to the Warner text.
https://in.sagepub.com/en-in/sas/an-r-companion-for-applied-statistics-i/book271882
9781071806319
Commercial statistics
R computer programming language
Statistics
519.502855133 / R2R2-I