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R Companion for Applied Statistics I : Basic Bivariate Techniques by Danney Rasco (2020, Trade Paperback)
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About this product
Product Identifiers
PublisherSAGE Publications, Incorporated
ISBN-101071806319
ISBN-139781071806319
eBay Product ID (ePID)2309903967
Product Key Features
Number of Pages256 Pages
Publication NameR Companion for Applied Statistics I : Basic Bivariate Techniques
LanguageEnglish
SubjectCommerce, Statistics
Publication Year2020
TypeTextbook
Subject AreaSocial Science, Business & Economics
AuthorDanney Rasco
FormatTrade Paperback
Dimensions
Item Height0.5 in
Item Weight15 Oz
Item Length9.2 in
Item Width7.4 in
Additional Product Features
Intended AudienceCollege Audience
LCCN2019-047203
Dewey Edition23
TitleLeadingAn
ReviewsRascos An R Companion to Applied Statistics I is an excellent companion to Warners seminal statistics text. If youve ever wanted to use R in place of commercial statistics, this is the book that will help you achieve that goal., 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 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, Rascos 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.
Dewey Decimal519.502855133
Table Of ContentPreface 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
SynopsisAn 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 Warners 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., 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., 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 Warners 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.
LC Classification NumberHF1017.R37 2020
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