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1145085252 |
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OCoLC |
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20250722155041.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS--GENERAL INFORMATION |
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200222s2020 njum ob u001 0 eng |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781119549918 |
| Qualifying information |
electronic book |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
1119549914 |
| Qualifying information |
electronic book |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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9781119549956 |
| Qualifying information |
electronic book |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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1119549957 |
| Qualifying information |
electronic book |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781119549963 |
| Qualifying information |
electronic book |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
1119549965 |
| Qualifying information |
electronic book |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| Cancelled/invalid ISBN |
9781119549932 |
| Qualifying information |
hardcover |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| Cancelled/invalid ISBN |
1119549930 |
| 035 ## - SYSTEM CONTROL NUMBER |
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| System control number |
(OCoLC)1145085252 |
| Canceled/invalid control number |
(OCoLC)1148175701 |
| 042 ## - AUTHENTICATION CODE |
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| Authentication code |
pcc |
| 050 04 - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
QA279 |
| Item number |
.D45776 2020 |
| 082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
519.5/302855133 |
| Edition number |
23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Preferred name for the person |
Denis, Daniel J., |
| Dates associated with a name |
1974- |
| Authority record control number |
http://id.loc.gov/authorities/names/n2015026464 |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
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| Title |
Univariate, bivariate, and multivariate statistics using R : |
| Remainder of title |
quantitative tools for data analysis and data science / |
| Statement of responsibility, etc |
Daniel J. Denis. |
| 264 #1 - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Place of publication, distribution, etc |
Hoboken, NJ : |
| Name of publisher, distributor, etc |
Wiley, |
| Date of publication, distribution, etc |
2020. |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
1 online resource (xvii, 366 pages) |
| 336 ## - CONTENT TYPE |
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text |
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txt |
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rdacontent. |
| 337 ## - MEDIA TYPE |
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computer |
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c |
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rdamedia. |
| 338 ## - CARRIER TYPE |
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| Carrier type term |
online resource |
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cr |
| Source |
rdacarrier. |
| 340 ## - PHYSICAL MEDIUM |
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| Source |
rdacc |
| Authority record control number or standard number |
http://rdaregistry.info/termList/RDAColourContent/1003. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc |
Includes bibliographical references and index. |
| 505 0# - CONTENTS |
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| Formatted contents note |
Table of Contents<br/>Preface xiii<br/><br/>1 Introduction to Applied Statistics 1<br/><br/>1.1 The Nature of Statistics and Inference 2<br/><br/>1.2 A Motivating Example 3<br/><br/>1.3 What About “Big Data”? 4<br/><br/>1.4 Approach to Learning R 7<br/><br/>1.5 Statistical Modeling in a Nutshell 7<br/><br/>1.6 Statistical Significance Testing and Error Rates 10<br/><br/>1.7 Simple Example of Inference Using a Coin 11<br/><br/>1.8 Statistics is for Messy Situations 13<br/><br/>1.9 Type I versus Type II Errors 14<br/><br/>1.10 Point Estimates and Confidence Intervals 15<br/><br/>1.11 So What Can We Conclude from One Confidence Interval? 18<br/><br/>1.12 Variable Types 19<br/><br/>1.13 Sample Size, Statistical Power, and Statistical Significance 22<br/><br/>1.14 How “p < 0.05” Happens 23<br/><br/>1.15 Effect Size 25<br/><br/>1.16 The Verdict on Significance Testing 26<br/><br/>1.17 Training versus Test Data 27<br/><br/>1.18 How to Get the Most Out of This Book 28<br/><br/>Exercises 29<br/><br/>2 Introduction to R and Computational Statistics 31<br/><br/>2.1 How to Install R on Your Computer 34<br/><br/>2.2 How to Do Basic Mathematics with R 35<br/><br/>2.2.1 Combinations and Permutations 38<br/><br/>2.2.2 Plotting Curves Using curve() 39<br/><br/>2.3 Vectors and Matrices in R 41<br/><br/>2.4 Matrices in R 44<br/><br/>2.4.1 The Inverse of a Matrix 47<br/><br/>2.4.2 Eigenvalues and Eigenvectors 49<br/><br/>2.5 How to Get Data into R 52<br/><br/>2.6 Merging Data Frames 55<br/><br/>2.7 How to Install a Package in R, and How to Use It 55<br/><br/>2.8 How to View the Top, Bottom, and “Some” of a Data File 58<br/><br/>2.9 How to Select Subsets from a Dataframe 60<br/><br/>2.10 How R Deals with Missing Data 62<br/><br/>2.11 Using ls( ) to See Objects in the Workspace 63<br/><br/>2.12 Writing Your Own Functions 65<br/><br/>2.13 Writing Scripts 65<br/><br/>2.14 How to Create Factors in R 66<br/><br/>2.15 Using the table() Function 67<br/><br/>2.16 Requesting a Demonstration Using the example() Function 68<br/><br/>2.17 Citing R in Publications 69<br/><br/>Exercises 69<br/><br/>3 Exploring Data with R: Essential Graphics and Visualization 71<br/><br/>3.1 Statistics, R, and Visualization 71<br/><br/>3.2 R’s plot() Function 73<br/><br/>3.3 Scatterplots and Depicting Data in Two or More Dimensions 77<br/><br/>3.4 Communicating Density in a Plot 79<br/><br/>3.5 Stem-and-Leaf Plots 85<br/><br/>3.6 Assessing Normality 87<br/><br/>3.7 Box-and-Whisker Plots 89<br/><br/>3.8 Violin Plots 95<br/><br/>3.9 Pie Graphs and Charts 97<br/><br/>3.10 Plotting Tables 98<br/><br/>Exercises 99<br/><br/>4 Means, Correlations, Counts: Drawing Inferences Using Easy-to-Implement Statistical Tests 101<br/><br/>4.1 Computing z and Related Scores in R 101<br/><br/>4.2 Plotting Normal Distributions 105<br/><br/>4.3 Correlation Coefficients in R 106<br/><br/>4.4 Evaluating Pearson’s r for Statistical Significance 110<br/><br/>4.5 Spearman’s Rho: A Nonparametric Alternative to Pearson 111<br/><br/>4.6 Alternative Correlation Coefficients in R 113<br/><br/>4.7 Tests of Mean Differences 114<br/><br/>4.7.1 t-Tests for One Sample 114<br/><br/>4.7.2 Two-Sample t-Test 115<br/><br/>4.7.3 Was the Welch Test Necessary? 117<br/><br/>4.7.4 t-Test via Linear Model Set-up 118<br/><br/>4.7.5 Paired-Samples t-Test 118<br/><br/>4.8 Categorical Data 120<br/><br/>4.8.1 Binomial Test 120<br/><br/>4.8.2 Categorical Data Having More Than Two Possibilities 123<br/><br/>4.9 Radar Charts 126<br/><br/>4.10 Cohen’s Kappa 127<br/><br/>Exercises 129<br/><br/>5 Power Analysis and Sample Size Estimation Using R 131<br/><br/>5.1 What is Statistical Power? 131<br/><br/>5.2 Does That Mean Power and Huge Sample Sizes Are “Bad?” 133<br/><br/>5.3 Should I Be Estimating Power or Sample Size? 134<br/><br/>5.4 How Do I Know What the Effect Size Should Be? 135<br/><br/>5.4.1 Ways of Setting Effect Size in Power Analyses 135<br/><br/>5.5 Power for t-Tests 136<br/><br/>5.5.1 Example: Treatment versus Control Experiment 137<br/><br/>5.5.2 Extremely Small Effect Size 138<br/><br/>5.6 Estimating Power for a Given Sample Size 140<br/><br/>5.7 Power for Other Designs – The Principles Are the Same 140<br/><br/>5.7.1 Power for One-Way ANOVA 141<br/><br/>5.7.2 Converting R2 to f 143<br/><br/>5.8 Power for Correlations 143<br/><br/>5.9 Concluding Thoughts on Power 145<br/><br/>Exercises 146<br/><br/>6 Analysis of Variance: Fixed Effects, Random Effects, Mixed Models, and Repeated Measures 147<br/><br/>6.1 Revisiting t-Tests 147<br/><br/>6.2 Introducing the Analysis of Variance (ANOVA) 149<br/><br/>6.2.1 Achievement as a Function of Teacher 149<br/><br/>6.3 Evaluating Assumptions 152<br/><br/>6.3.1 Inferential Tests for Normality 153<br/><br/>6.3.2 Evaluating Homogeneity of Variances 154<br/><br/>6.4 Performing the ANOVA Using aov() 156<br/><br/>6.4.1 The Analysis of Variance Summary Table 157<br/><br/>6.4.2 Obtaining Treatment Effects 158<br/><br/>6.4.3 Plotting Results of the ANOVA 159<br/><br/>6.4.4 Post Hoc Tests on the Teacher Factor 159<br/><br/>6.5 Alternative Way of Getting ANOVA Results via lm() 161<br/><br/>6.5.1 Contrasts in lm() versus Tukey’s HSD 163<br/><br/>6.6 Factorial Analysis of Variance 163<br/><br/>6.6.1 Why Not Do Two One-Way ANOVAs? 163<br/><br/>6.7 Example of Factorial ANOVA 166<br/><br/>6.7.1 Graphing Main Effects and Interaction in the Same Plot 171<br/><br/>6.8 Should Main Effects Be Interpreted in the Presence of Interaction? 172<br/><br/>6.9 Simple Main Effects 173<br/><br/>6.10 Random Effects ANOVA and Mixed Models 175<br/><br/>6.10.1 A Rationale for Random Factors 176<br/><br/>6.10.2 One-Way Random Effects ANOVA in R 177<br/><br/>6.11 Mixed Models 180<br/><br/>6.12 Repeated-Measures Models 181<br/><br/>Exercises 186<br/><br/>7 Simple and Multiple Linear Regression 189<br/><br/>7.1 Simple Linear Regression 190<br/><br/>7.2 Ordinary Least-Squares Regression 192<br/><br/>7.3 Adjusted R2 198<br/><br/>7.4 Multiple Regression Analysis 199<br/><br/>7.5 Verifying Model Assumptions 202<br/><br/>7.6 Collinearity Among Predictors and the Variance Inflation Factor 206<br/><br/>7.7 Model-Building and Selection Algorithms 209<br/><br/>7.7.1 Simultaneous Inference 209<br/><br/>7.7.2 Hierarchical Regression 210<br/><br/>7.7.2.1 Example of Hierarchical Regression 211<br/><br/>7.8 Statistical Mediation 214<br/><br/>7.9 Best Subset and Forward Regression 217<br/><br/>7.9.1 How Forward Regression Works 218<br/><br/>7.10 Stepwise Selection 219<br/><br/>7.11 The Controversy Surrounding Selection Methods 221<br/><br/>Exercises 223<br/><br/>8 Logistic Regression and the Generalized Linear Model 225<br/><br/>8.1 The “Why” Behind Logistic Regression 225<br/><br/>8.2 Example of Logistic Regression in R 229<br/><br/>8.3 Introducing the Logit: The Log of the Odds 232<br/><br/>8.4 The Natural Log of the Odds 233<br/><br/>8.5 From Logits Back to Odds 235<br/><br/>8.6 Full Example of Logistic Regression 236<br/><br/>8.6.1 Challenger O-ring Data 236<br/><br/>8.7 Logistic Regression on Challenger Data 240<br/><br/>8.8 Analysis of Deviance Table 241<br/><br/>8.9 Predicting Probabilities 242<br/><br/>8.10 Assumptions of Logistic Regression 243<br/><br/>8.11 Multiple Logistic Regression 244<br/><br/>8.12 Training Error Rate Versus Test Error Rate 247<br/><br/>Exercises 248<br/><br/>9 Multivariate Analysis of Variance (MANOVA) and Discriminant Analysis 251<br/><br/>9.1 Why Conduct MANOVA? 252<br/><br/>9.2 Multivariate Tests of Significance 254<br/><br/>9.3 Example of MANOVA in R 257<br/><br/>9.4 Effect Size for MANOVA 259<br/><br/>9.5 Evaluating Assumptions in MANOVA 261<br/><br/>9.6 Outliers 262<br/><br/>9.7 Homogeneity of Covariance Matrices 263<br/><br/>9.7.1 What if the Box-M Test Had Suggested a Violation? 264<br/><br/>9.8 Linear Discriminant Function Analysis 265<br/><br/>9.9 Theory of Discriminant Analysis 266<br/><br/>9.10 Discriminant Analysis in R 267<br/><br/>9.11 Computing Discriminant Scores Manually 270<br/><br/>9.12 Predicting Group Membership 271<br/><br/>9.13 How Well Did the Discriminant Function Analysis Do? 272<br/><br/>9.14 Visualizing Separation 275<br/><br/>9.15 Quadratic Discriminant Analysis 276<br/><br/>9.16 Regularized Discriminant Analysis 278<br/><br/>Exercises 278<br/><br/>10 Principal Component Analysis 281<br/><br/>10.1 Principal Component Analysis Versus Factor Analysis 282<br/><br/>10.2 A Very Simple Example of PCA 283<br/><br/>10.2.1 Pearson’s 1901 Data 284<br/><br/>10.2.2 Assumptions of PCA 286<br/><br/>10.2.3 Running the PCA 288<br/><br/>10.2.4 Loadings in PCA 290<br/><br/>10.3 What Are the Loadings in PCA? 292<br/><br/>10.4 Properties of Principal Components 293<br/><br/>10.5 Component Scores 294<br/><br/>10.6 How Many Components to Keep? 295<br/><br/>10.6.1 The Scree Plot as an Aid to Component Retention 295<br/><br/>10.7 Principal Components of USA Arrests Data 297<br/><br/>10.8 Unstandardized Versus Standardized Solutions 301<br/><br/>Exercises 304<br/><br/>11 Exploratory Factor Analysis 307<br/><br/>11.1 Common Factor Analysis Model 308<br/><br/>11.2 A Technical and Philosophical Pitfall of EFA 310<br/><br/>11.3 Factor Analysis Versus Principal Component Analysis on the Same Data 311<br/><br/>11.3.1 Demonstrating the Non-Uniqueness Issue 311<br/><br/>11.4 The Issue of Factor Retention 314<br/><br/>11.5 Initial Eigenvalues in Factor Analysis 315<br/><br/>11.6 Rotation in Exploratory Factor Analysis 316<br/><br/>11.7 Estimation in Factor Analysis 318<br/><br/>11.8 Example of Factor Analysis on the Holzinger and Swineford Data 318<br/><br/>11.8.1 Obtaining Initial Eigenvalues 323<br/><br/>11.8.2 Making Sense of the Factor Solution 324<br/><br/>Exercises 325<br/><br/>12 Cluster Analysis 327<br/><br/>12.1 A Simple Example of Cluster Analysis 329<br/><br/>12.2 The Concepts of Proximity and Distance in Cluster Analysis 332<br/><br/>12.3 k-Means Cluster Analysis 332<br/><br/>12.4 Minimizing Criteria 333<br/><br/>12.5 Example of k-Means Clustering in R 334<br/><br/>12.5.1 Plotting the Data 335<br/><br/>12.6 Hierarchical Cluster Analysis 339<br/><br/>12.7 Why Clustering is Inherently Subjective 343<br/><br/>Exercises 344<br/><br/>13 Nonparametric Tests 347<br/><br/>13.1 Mann–Whitney U Test 348<br/><br/>13.2 Kruskal–Wallis Test 349<br/><br/>13.3 Nonparametric Test for Paired Comparisons and Repeated Measures 351<br/><br/>13.3.1 Wilcoxon Signed-Rank Test and Friedman Test 351<br/><br/>13.4 Sign Test 354<br/><br/>Exercises 356<br/><br/>References 359<br/><br/>Index 363 |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
"This book provides a user-friendly and practical guide on R, with emphasis on covering a broader range of statistical methods than previous books on R. This is a "how to" book and will be of use to undergraduates and graduate students along with researchers and professionals who require a quick go-to source to help them perform essential statistical analyses and data management tasks in R. The book only assumes minimal prior knowledge of statistics, providing readers with the tools they need right now to help them understand and interpret their data analyses. This book covers univariate, bivariate, and multivariate statistical methods, as well as some nonparametric tests. It provides students with a hands-on easy-to-read manual on the wealth of applied statistics and essential R computing that they will need for their theses, dissertations, and research publications. A strength of this book is its scope of coverage of univariate through to multivariate procedures, while simultaneously serving as a friendly introduction to R software"-- |
| Assigning source |
Provided by publisher. |
| 545 0# - BIOGRAPHICAL OR HISTORICAL DATA |
|---|
| Biographical or historical note |
About the Author<br/>DANIEL J. DENIS, PHD, is Professor of Quantitative Psychology in the Department of Psychology at the University of Montana. D. Denis is the author of Applied Univariate, Bivariate, and Multivariate Statistics and SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics, both published by Wiley. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Analysis of variance |
| Authority record control number |
http://id.loc.gov/authorities/subjects/sh85004782 |
| Form subdivision |
Textbooks. |
| Authority record control number |
http://id.loc.gov/authorities/subjects/sh99001753. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Multivariate analysis |
| Authority record control number |
http://id.loc.gov/authorities/subjects/sh85088390 |
| Form subdivision |
Textbooks. |
| Authority record control number |
http://id.loc.gov/authorities/subjects/sh99001753. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Mathematical statistics |
| General subdivision |
Data processing |
| Authority record control number |
http://id.loc.gov/authorities/subjects/sh85082137 |
| Form subdivision |
Textbooks. |
| Authority record control number |
http://id.loc.gov/authorities/subjects/sh99001753. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
R (Computer program language) |
| Authority record control number |
http://id.loc.gov/authorities/subjects/sh2002004407 |
| Form subdivision |
Textbooks. |
| Authority record control number |
http://id.loc.gov/authorities/subjects/sh99001753. |
| 655 #4 - INDEX TERM--GENRE/FORM |
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| Genre/form data or focus term |
Electronic books. |
| 655 #7 - INDEX TERM--GENRE/FORM |
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| Genre/form data or focus term |
Textbooks. |
| Source of term |
fast |
| Authority record control number |
http://id.worldcat.org/fast/1423863. |
| 710 2# - ADDED ENTRY--CORPORATE NAME |
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| Corporate name or jurisdiction name as entry element |
Ohio Library and Information Network. |
| Authority record control number |
http://id.loc.gov/authorities/names/no95058981. |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
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| Link text |
Full text is available at Wiley Online Library Click here to view |
| Uniform Resource Identifier |
https://onlinelibrary.wiley.com/doi/book/10.1002/9781119549963 |
| 942 ## - ADDED ENTRY ELEMENTS |
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| Source of classification or shelving scheme |
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| Item type |
EBOOK |