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R Companion for Applied Statistics I: Basic Bivariate Techniques [Mīkstie vāki]

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  • Formāts: Paperback / softback, 256 pages, height x width: 231x187 mm, weight: 490 g
  • Izdošanas datums: 21-May-2020
  • Izdevniecība: SAGE Publications Inc
  • ISBN-10: 1071806319
  • ISBN-13: 9781071806319
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R Companion for Applied Statistics I: Basic Bivariate TechniquesPalielināt
  • Formāts: Paperback / softback, 256 pages, height x width: 231x187 mm, weight: 490 g
  • Izdošanas datums: 21-May-2020
  • Izdevniecība: SAGE Publications Inc
  • ISBN-10: 1071806319
  • ISBN-13: 9781071806319
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781071806319.html
Keywords:
"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-whicheventually 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 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.

Recenzijas

Rascos 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. -- Jeffrey Savage 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. -- Lina Racicot

Preface xi
Acknowledgments xiii
About the Author xv
Chapter 1 Introduction: What Is R? 1(12)
Downloading R and RStudio
1(1)
Creating a Project Folder
1(2)
Getting Acquainted With the RStudio Environment
3(1)
Learning RStudio Panes
3(4)
Finding Help
7(1)
Appendix 1A: Preparing RStudio Project Folder
8(5)
Chapter 2 Basic Tasks in R 13(12)
Coding in R: Object-Oriented Programming
13(3)
Creating Data
16(3)
Exporting Data
19(1)
Importing Data
20(2)
Converting Variables
22(3)
Chapter 3 Frequency Tables 25(8)
Frequency Tables With Quantitative Variables
28(1)
Appendix 3A: R Instructions to Accompany Warner (2020a)
29(4)
Chapter 4 Descriptive Statistics 33(12)
Describing Central Tendency
34(2)
Describing Variability
36(3)
Appendix 4A: R Instructions to Accompany Warner (2020a)
39(4)
Appendix 4B: Mode Function
43(2)
Chapter 5 Visualizing Data: Bar Charts, Histograms, and Boxplots 45(22)
Visualizing Categorical Variables
45(4)
Visualizing Quantitative Variables
49(3)
Visualizing and Accounting for a Second Variable
52(7)
Appendix 5A: R Instructions to Accompany Warner (2020a)
59(8)
Chapter 6 Evaluating Score Locations: Introducing the Normal Distribution and z Scores 67(18)
Getting Familiar With New Data Frames and Variables
67(2)
Cumulative Percentage
69(4)
z Scores
73(2)
Addressing Normality
75(6)
Appendix 6A: R Instructions to Accompany Warner (2020a)
81(4)
Chapter 7 Sampling Error and Confidence Intervals 85(14)
Monte Carlo Simulations
85(5)
Confidence Intervals
90(3)
Appendix 7A: R Instructions to Accompany Warner (2020a)
93(6)
Chapter 8 One-Sample t Test: Introduction to Statistical Significance Tests 99(12)
Checking Assumptions
99(2)
Performing One-Sample t Tests
101(2)
Presenting Results
103(1)
Considering Alternatives
104(2)
Appendix 8A: R Instructions to Accompany Warner (2020a)
106(2)
Appendix 8B: One-Sample z Test
108(3)
Chapter 9 Significance Tests Continued: Effect Size and Power 111(4)
Estimating the Needed Sample Size
112(1)
Estimating the Obtained Power
113(2)
Chapter 10 Bivariate Pearson Correlation 115(14)
Checking Assumptions
115(2)
Performing Pearson's Bivariate Correlation
117(1)
Considering Alternatives
118(3)
Appendix 10A: R Instructions to Accompany Warner (2020a)
121(8)
Chapter 11 Bivariate Regression 129(10)
Checking Assumptions
129(2)
Performing Bivariate Regression
131(4)
Appendix 11A: R Instructions to Accompany Warner (2020a)
135(4)
Chapter 12 Independent-Samples tTest 139(16)
Checking Assumptions
139(3)
Performing Independent-Samples tTests
142(1)
Presenting Results
143(3)
Considering Alternatives
146(1)
Appendix 12A: R Instructions to Accompany Warner (2020a)
147(6)
Appendix 12B: Wilcoxon-Mann-Whitney II Test
153(2)
Chapter 13 One-Way Between-Subjects Analysis of Variance 155(18)
Checking Assumptions
155(4)
Performing One-Way Between-Subjects ANOVA Tests
159(2)
Presenting Results
161(3)
Considering Alternatives
164(2)
Appendix 13A: R Instructions to Accompany Warner (2020a)
166(7)
Chapter 14 Paired-Samples tTest 173(14)
Checking Assumptions
173(2)
Performing Paired-Samples t Tests
175(1)
Presenting Results
176(2)
Considering Alternatives
178(2)
Appendix 14A: R Instructions to Accompany Warner (2020a)
180(7)
Chapter 15 One-Way Repeated-Measures Analysis of Variance 187(14)
Checking Assumptions
187(3)
Performing One-Way Repeated-Measures ANOVA Tests
190(3)
Presenting Results
193(2)
Considering Alternatives
195(1)
Appendix 15A: R Instructions to Accompany Warner (2020a)
196(5)
Chapter 16 Factorial Analysis of Variance 201(16)
Checking Assumptions
201(2)
Performing TWo-Way Between-Subjects ANOVA Tests
203(1)
Presenting Results
204(2)
Considering Alternatives
206(3)
Appendix 16A: R Instructions to Accompany Warner (2020a)
209(6)
Appendix 16B: Converting Education Variable to Dichotomous Variable
215(2)
Chapter 17 Chi-Square (e) Test of Independence 217(14)
Checking Assumptions
217(1)
Performing Chi-Square (z2) Tests of Independence
218(2)
Presenting Results
220(1)
Considering Alternatives
221(3)
Appendix 17A: R Instructions to Accompany Warner (2020a)
224(7)
Chapter 18 Parting Thoughts About R 231(2)
Moving Forward
231(1)
Continuing to Learn R
232(1)
References 233
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 masters degree in clinical and counseling psychology from Midwestern State University, a masters 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.