Understanding and Applying Basic Statistical Methods Using R

Understanding and Applying Basic Statistical Methods Using R

by Rand R. Wilcox
Released June 2016
Publisher(s): Wiley
ISBN: 9781119061397

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Book description

Features a straightforward and concise resource forintroductory 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.

Table of contents

  1. Title Page
  2. Copyright
  3. List of Symbols
  4. Preface
  5. About The Companion Website
  6. Chapter 1: Introduction
    1. 1.4 R Packages
    2. 1.5 Access to Data Used in This Book
    3. 1.6 Accessing More Detailed Answers to The Exercises
    4. 1.7 Exercises
  7. Chapter 2: Numerical Summaries of Data
    1. 2.1 Summation Notation
    2. 2.2 Measures of Location
    3. 2.3 Quartiles
    4. 2.4 Measures of Variation
    5. 2.5 Detecting Outliers
    6. 2.6 Skipped Measures of Location
    7. 2.7 Summary
    8. 2.8 Exercises
  8. Chapter 3: Plots Plus More Basics on Summarizing Data
    1. 3.1 Plotting Relative Frequencies
    2. 3.2 Histograms and Kernel Density Estimators
    3. 3.3 Boxplots and Stem-and-Leaf Displays
    4. 3.4 Summary
    5. 3.5 Exercises
  9. Chapter 4: Probability and Related Concepts
    1. 4.1 The Meaning of Probability
    2. 4.2 Probability Functions
    3. 4.3 Expected Values, Population Mean and Variance
    4. 4.4 Conditional Probability and Independence
    5. 4.5 The Binomial Probability Function
    6. 4.6 The Normal Distribution
    7. 4.7 Nonnormality and The Population Variance
    8. 4.8 Summary
    9. 4.9 Exercises
  10. Chapter 5: Sampling Distributions
    1. 5.1 Sampling Distribution of , The Proportion of Successes
    2. 5.2 Sampling Distribution of the Mean Under Normality
    3. 5.3 Nonnormality and the Sampling Distribution of the Sample Mean
    4. 5.4 Sampling Distribution of the Median and 20% Trimmed Mean
    5. 5.5 The Mean Versus the Median and 20% Trimmed Mean
    6. 5.6 Summary
    7. 5.7 Exercises
  11. Chapter 6: Confidence Intervals
    1. 6.1 Confidence Interval for The Mean
    2. 6.2 Confidence Intervals for The Mean Using s ( Not Known)
    3. 6.3 A Confidence Interval for The Population Trimmed Mean
    4. 6.4 Confidence Intervals for The Population Median
    5. 6.5 The Impact of Nonnormality on Confidence Intervals
    6. 6.6 Some Basic Bootstrap Methods
    7. 6.7 Confidence Interval for The Probability of Success
    8. 6.8 Summary
    9. 6.9 Exercises
  12. Chapter 7: Hypothesis Testing
    1. 7.1 Testing Hypotheses about the Mean, Known
    2. 7.2 Power and Type II Errors
    3. 7.3 Testing Hypotheses about the mean, Not Known
    4. 7.4 Student's T and Nonnormality
    5. 7.5 Testing Hypotheses about Medians
    6. 7.6 Testing Hypotheses Based on a Trimmed Mean
    7. 7.7 Skipped Estimators
    8. 7.8 Summary
    9. 7.9 Exercises
  13. Chapter 8: Correlation and Regression
    1. 8.1 Regression Basics
    2. 8.2 Least Squares Regression
    3. 8.3 Dealing with Outliers
    4. 8.4 Hypothesis Testing
    5. 8.5 Correlation
    6. 8.6 Detecting Outliers When Dealing with Two or More Variables
    7. 8.7 Measures of Association: Dealing with Outliers
    8. 8.8 Multiple Regression
    9. 8.9 Dealing with Curvature
    10. 8.10 Summary
    11. 8.11 Exercises
  14. Chapter 9: Comparing Two Independent Groups
    1. 9.1 Comparing Means
    2. 9.2 Comparing Medians
    3. 9.3 Comparing Trimmed Means
    4. 9.4 Tukey's Three-Decision Rule
    5. 9.5 Comparing Variances
    6. 9.6 Rank-Based (Nonparametric) Methods
    7. 9.7 Measuring Effect Size
    8. 9.8 Plotting Data
    9. 9.9 Comparing Quantiles
    10. 9.10 Comparing Two Binomial Distributions
    11. 9.11 A Method for Discrete or Categorical Data
    12. 9.12 Comparing Regression Lines
    13. 9.13 Summary
    14. 9.14 Exercises
  15. Chapter 10: Comparing More than Two Independent Groups
    1. 10.1 The Anova Test
    2. 10.2 Dealing With Unequal Variances: Welch's Test
    3. 10.3 Comparing Groups Based on Medians
    4. 10.4 Comparing Trimmed Means
    5. 10.5 Two-Way Anova
    6. 10.6 Rank-Based Methods
    7. 10.7 R Functions and
    8. 10.8 Summary
    9. 10.9 Exercises
  16. Chapter 11: Comparing Dependent Groups
    1. 11.1 The Paired Test
    2. 11.2 Comparing Trimmed Means and Medians
    3. 11.3 The Sign Test
    4. 11.4 Wilcoxon Signed Rank Test
    5. 11.5 Comparing Variances
    6. 11.6 Dealing With More Than Two Dependent Groups
    7. 11.7 Between-by-Within Designs
    8. 11.8 Summary
    9. 11.9 Exercises
  17. Chapter 12: Multiple Comparisons
    1. 12.1 Classic Methods For Independent Groups
    2. 12.2 The Tukey–Kramer Method
    3. 12.3 Scheffé's Method
    4. 12.4 Methods That Allow Unequal Population Variances
    5. 12.5 Anova Versus Multiple Comparison Procedures
    6. 12.6 Comparing Medians
    7. 12.7 Two-Way Anova Designs
    8. 12.8 Methods For Dependent Groups
    9. 12.9 Summary
    10. 12.10 Exercises
  18. Chapter 13: Categorical Data
    1. 13.1 One-Way Contingency Tables
    2. 13.2 Two-Way Contingency Tables
    3. 13.3 Logistic Regression
    4. 13.4 Summary
    5. 13.5 Exercises
  19. Appendix A: Solutions to Selected Exercises
  20. Appendix B: Tables
  21. References
  22. Index
  23. End User License Agreement

Product information

  • Title: Understanding and Applying Basic Statistical Methods Using R
  • Author(s): Rand R. Wilcox
  • Release date: June 2016
  • Publisher(s): Wiley
  • ISBN: 9781119061397