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Features a straightforward and concise resource for introductory statistical concepts, methods, and techniques using R Understanding and Applying Basic Statistical Methods Using R uniquely bridges the gap between advances in the statistical literature and methods routinely used by non-statisticians. Providing a conceptual basis for understanding the relative merits and applications of these methods, the book features modern insights and advances relevant to basic techniques in terms of dealing with non-normality, outliers, heteroscedasticity (unequal variances), and curvature. Featuring a…mehr

Produktbeschreibung
Features a straightforward and concise resource for introductory statistical concepts, methods, and techniques using R Understanding and Applying Basic Statistical Methods Using R uniquely bridges the gap between advances in the statistical literature and methods routinely used by non-statisticians. Providing a conceptual basis for understanding the relative merits and applications of these methods, the book features modern insights and advances relevant to basic techniques in terms of dealing with non-normality, outliers, heteroscedasticity (unequal variances), and curvature. Featuring a guide to R, the book uses R programming to explore introductory statistical concepts and standard methods for dealing with known problems associated with classic techniques. Thoroughly class-room tested, the book includes sections that focus on either R programming or computational details to help the reader become acquainted with basic concepts and principles essential in terms of understanding and applying the many methods currently available. Covering relevant material from a wide range of disciplines, Understanding and Applying Basic Statistical Methods Using R also includes: Numerous illustrations and exercises that use data to demonstrate the practical importance of multiple perspectives Discussions on common mistakes such as eliminating outliers and applying standard methods based on means using the remaining data Detailed coverage on R programming with descriptions on how to apply both classic and more modern methods using R A companion website with the data and solutions to all of the exercises Understanding and Applying Basic Statistical Methods Using R is an ideal textbook for an undergraduate and graduate-level statistics courses in the science and/or social science departments. The book can also serve as a reference for professional statisticians and other practitioners looking to better understand modern statistical methods as well as R programming. Rand R. Wilcox, PhD, is Professor in the Department of Psychology at the University of Southern California, Fellow of the Association for Psychological Science, and an associate editor for four statistics journals. He is also a member of the International Statistical Institute. The author of more than 320 articles published in a variety of statistical journals, he is also the author eleven other books on statistics. Dr. Wilcox is creator of WRS (Wilcox' Robust Statistics), which is an R package for performing robust statistical methods. His main research interest includes statistical methods, particularly robust methods for comparing groups and studying associations.
  • Produktdetails
  • Verlag: Wiley & Sons
  • Artikelnr. des Verlages: .1W119061390
  • 1. Auflage
  • Seitenzahl: 504
  • Erscheinungstermin: 27. Mai 2016
  • Englisch
  • Abmessung: 234mm x 156mm x 31mm
  • Gewicht: 879g
  • ISBN-13: 9781119061397
  • ISBN-10: 1119061393
  • Artikelnr.: 44148352
Autorenporträt
Rand R. Wilcox, PhD, is Professor in the Department of Psychology at the University of Southern California, Fellow of the Association for Psychological Science, and an associate editor for four statistics journals. He is also a member of the International Statistical Institute. The author of more than 320 articles published in a variety of statistical journals, he is also the author eleven other books on statistics. Dr. Wilcox is creator of WRS (Wilcox' Robust Statistics), which is an R package for performing robust statistical methods. His main research interest includes statistical methods, particularly robust methods for comparing groups and studying associations.
Inhaltsangabe
List of Symbols xv Preface xvii About the Companion Website xix 1 Introduction 1 1.1 Samples Versus Populations 3 1.2 Comments on Software 4 1.3 R Basics 5 1.3.1 Entering Data 6 1.3.2 Arithmetic Operations 10 1.3.3 Storage Types and Modes 12 1.3.4 Identifying and Analyzing Special Cases 17 1.4 R Packages 20 1.5 Access to Data Used in this Book 22 1.6 Accessing More Detailed Answers to the Exercises 23 1.7 Exercises 23 2 Numerical Summaries of Data 25 2.1 Summation Notation 26 2.2 Measures of Location 29 2.2.1 The Sample Mean 29 2.2.2 The Median 30 2.2.3 Sample Mean versus Sample Median 33 2.2.4 Trimmed Mean 34 2.2.5 R function mean, tmean, and median 35 2.3 Quartiles 36 2.3.1 R function idealf and summary 37 2.4 Measures of Variation 37 2.4.1 The Range 38 2.4.2 R function Range 38 2.4.3 Deviation Scores, Variance, and Standard Deviation 38 2.4.4 R Functions var and sd 40 2.4.5 The Interquartile Range 41 2.4.6 MAD and the Winsorized Variance 41 2.4.7 R Functions winvar, winsd, idealfIQR, and mad 44 2.5 Detecting Outliers 44 2.5.1 A Classic Outlier Detection Method 45 2.5.2 The Boxplot Rule 46 2.5.3 The MAD-Median Rule 47 2.5.4 R Functions outms, outbox, and out 47 2.6 Skipped Measures of Location 48 2.6.1 R Function MOM 49 2.7 Summary 49 2.8 Exercises 50 3 Plots Plus More Basics on Summarizing Data 53 3.1 Plotting Relative Frequencies 53 3.1.1 R Functions table, plot, splot, barplot, and cumsum 54 3.1.2 Computing the Mean and Variance Based on the Relative Frequencies 56 3.1.3 Some Features of the Mean and Variance 57 3.2 Histograms and Kernel Density Estimators 57 3.2.1 R Function hist 58 3.2.2 What Do Histograms Tell Us? 59 3.2.3 Populations, Samples, and Potential Concerns about Histograms 61 3.2.4 Kernel Density Estimators 64 3.2.5 R Functions Density and Akerd 64 3.3 Boxplots and Stem-and-Leaf Displays 65 3.3.1 R Function stem 67 3.3.2 Boxplot 67 3.3.3 R Function boxplot 68 3.4 Summary 68 3.5 Exercises 69 4 Probability and Related Concepts 71 4.1 The Meaning of Probability 71 4.2 Probability Functions 72 4.3 Expected Values, Population Mean and Variance 74 4.3.1 Population Variance 76 4.4 Conditional Probability and Independence 77 4.4.1 Independence and Dependence 78 4.5 The Binomial Probability Function 80 4.5.1 R Functions dbinom and pbinom 85 4.6 The Normal Distribution 85 4.6.1 Some Remarks about the Normal Distribution 88 4.6.2 The Standard Normal Distribution 89 4.6.3 Computing Probabilities for Any Normal Distribution 92 4.6.4 R Functions pnorm and qnorm 94 4.7 Nonnormality and The Population Variance 94 4.7.1 Skewed Distributions 97 4.7.2 Comments on Transforming Data 98 4.8 Summary 100 4.9 Exercises 101 5 Sampling Distributions 107 5.1 Sampling Distribution of p, the Proportion of Successes 108 5.2 Sampling Distribution of the Mean Under Normality 111 5.2.1 Determining Probabilities Associated with the Sample Mean 113 5.2.2 But Typically sigma Is Not Known. Now What? 116 5.3 Nonnormality and the Sampling Distribution of the Sample Mean 116 5.3.1 Approximating the Binomial Distribution 117 5.3.2 Approximating the Sampling Distribution of the Sample Mean: The General Case 119 5.4 Sampling Distribution of the Median and 20% Trimmed Mean 123 5.4.1 Estimating the Standard Error of the Median 126 5.4.2 R Function msmedse 127 5.4.3 Approximating the Sampling Distribution of the Sample Median 128 5.4.4 Estimating the Standard Error of a Trimmed Mean 129 5.4.5 R Function trimse 130 5.4.6 Estimating the Standard Error When Outliers Are Discarded: A Technically Unsound Approach 130 5.5 The Mean Versus the Median and 20% Trimmed Mean 131 5.6 Summary 135 5.7 Exercises 136 6 Confidence Intervals 139 6.1 Confidence Interval for the Mean 139