Overview

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 provides 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.

Full Product Details

Author:   Danney Rasco
Publisher:   SAGE Publications Inc
Imprint:   SAGE Publications Inc
Dimensions:   Width: 18.70cm , Height: 1.50cm , Length: 23.10cm
Weight:   0.490kg
ISBN:  

9781071806319

ISBN 10:   1071806319
Pages:   256
Publication Date:   21 May 2020
Audience:   College/higher education ,  Tertiary & Higher Education
Format:   Paperback
Publisher's Status:   Active
Availability:   To order  
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Table of Contents

Preface Acknowledgments About the Author Chapter 1: Introduction: What is R? Downloading R and RStudio Creating a Project Folder Getting Acquainted with the RStudio Environment Appendix 1A: Preparing RStudio Project Folder Chapter 2: Basic Tasks in R Coding in R: Object-Oriented Programming Creating Data Exporting Data Importing Data Converting Variables Summary of Key Functions Chapter 3: Frequency Tables Frequency Tables with Quantitative Variables Appendix 3A: R Instructions to Accompany Warner (2020a) Chapter 4: Descriptive Statistics Describing Central Tendency Describing Variability Appendix 4A: R Instructions to Accompany Warner (2020a) Appendix 4B: Mode Function Chapter 5: Visualizing Data: Bar Charts, Histograms, and Boxplots Visualizing Categorical Variables Visualizing Quantitative Variables Visualizing and Accounting for a Second Variable Appendix 5A: R Instructions to Accompany Warner (2020a) Chapter 6: Evaluating Score Locations: Introducing the Normal Distribution and z Scores Getting Familiar With New Data Frames and Variables Cumulative Percentage z Scores Addressing Normality Appendix 6A: R Instructions to Accompany Warner (2020a) Chapter 7: Sampling Error and Confidence Intervals Monte Carlo Simulations Confidence Intervals Appendix 7A: R Instructions to Accompany Warner (2020a) Chapter 8: One-Sample t Test: Introduction to Statistical Significance Tests Checking Assumptions Performing One-Sample t Tests Presenting Results Considering Alternatives Appendix 8A: R Instructions to Accompany Warner (2020a) Appendix 8B: One-Sample z Test Chapter 9: Significance Tests Continued: Effect Size and Power Estimating the Needed Sample Size Estimating the Obtained Power Chapter 10: Bivariate Pearson Correlation Checking Assumptions Performing Pearson′s Bivariate Correlation Considering Alternatives Appendix 10A: R Instructions to Accompany Warner (2020a) Chapter 11: Bivariate Regression Checking Assumptions Performing Bivariate Regression Appendix 11A: R Instructions to Accompany Warner (2020a) Chapter 12: Independent-Samples t Test Checking Assumptions Performing Independent-Samples t Tests Presenting Results Considering Alternatives Appendix 12A: R Instructions to Accompany Warner (2020a) Appendix 12B: Wilcoxon-Mann-Whitney U Test Chapter 13: One-Way Between-Subjects Analysis of Variance Checking Assumptions Performing One-Way Between-Subjects ANOVA Tests Presenting Results Considering Alternatives Appendix 13A: R Instructions to Accompany Warner (2020a) Chapter 14: Paired-Samples t Test Checking Assumptions Performing Paired-Samples t Tests Presenting Results Considering Alternatives Appendix 14A: R Instructions to Accompany Warner (2020a) Chapter 15: One-Way Repeated-Measures Analysis of Variance Checking Assumptions Performing One-Way Repeated-Measures ANOVA Tests Presenting Results Considering Alternatives Appendix 15A: R Instructions to Accompany Warner (2020a) Chapter 16: Factorial Analysis of Variance Checking Assumptions Performing Two-Way Between-Subjects ANOVA Tests Presenting Results Considering Alternatives Appendix 16A: R Instructions to Accompany Warner (2020a) Appendix 16B: Converting Education Variable to Dichotomous Variable Chapter 17: Chi-Square (?2) Test of Independence Checking Assumptions Performing Chi-Square (?2) Tests of Independence Presenting Results Considering Alternatives Appendix 17A: R Instructions to Accompany Warner (2020a) Chapter 18: Parting THoughts About R Moving Forward Continuing to Learn R References

Reviews

Rasco's text has taken the complexity out of using R for students who are learning the system. His engaging text gives step by step instructions with visuals. He thoroughly covers the relevance and assumptions of each statistical analysis. --Lina Racicot (09/25/2019) Rasco's An R Companion to Applied Statistics I is an excellent companion to Warner's seminal statistics text. If you've ever wanted to use R in place of commercial statistics, this is the book that will help you achieve that goal. --Jeffrey Savage (09/25/2019)

Rasco's An R Companion to Applied Statistics I is an excellent companion to Warner's seminal statistics text. If you've ever wanted to use R in place of commercial statistics, this is the book that will help you achieve that goal. --Jeffrey Savage (09/25/2019) Rasco's text has taken the complexity out of using R for students who are learning the system. His engaging text gives step by step instructions with visuals. He thoroughly covers the relevance and assumptions of each statistical analysis. --Lina Racicot (09/25/2019)

Author Information

Danney Rasco is an Assistant Professor in the Department of Psychology, Sociology, and Social Work at West Texas A&M University. As a self-professed stats nerd, he enjoys (yes, enjoys) teaching three or four sections of statistics each year and simply smiles and shrugs when students shake their heads at his enthusiasm and zeal for data and the beautiful sport of number crunching. In his “free” time, he plans statistics workshops because he is a glutton for punishment. This love for statistics and teaching (i.e., nerdiness) resulted in a Summer Teaching Assistant Fellowship from the University of New Hampshire, an Intellectual Contribution Award from the College of Education and Social Sciences at West Texas A&M University. Dr. Rasco has a master’s degree in clinical and counseling psychology from Midwestern State University, a master’s degree and PhD in social psychology from the University of New Hampshire, and a Cognate in College Teaching from the University of New Hampshire. One day he will buy frames, perhaps with the proceeds from this book, and display these degrees proudly on a wall.

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