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 nonstatisticians. 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 nonnormality, 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 classroom 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 graduatelevel 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
 Title Page
 Copyright
 List of Symbols
 Preface
 About The Companion Website
 Chapter 1: Introduction
 Chapter 2: Numerical Summaries of Data
 Chapter 3: Plots Plus More Basics on Summarizing Data
 Chapter 4: Probability and Related Concepts

Chapter 5: Sampling Distributions
 5.1 Sampling Distribution of , The Proportion of Successes
 5.2 Sampling Distribution of the Mean Under Normality
 5.3 Nonnormality and the Sampling Distribution of the Sample Mean
 5.4 Sampling Distribution of the Median and 20% Trimmed Mean
 5.5 The Mean Versus the Median and 20% Trimmed Mean
 5.6 Summary
 5.7 Exercises

Chapter 6: Confidence Intervals
 6.1 Confidence Interval for The Mean
 6.2 Confidence Intervals for The Mean Using s ( Not Known)
 6.3 A Confidence Interval for The Population Trimmed Mean
 6.4 Confidence Intervals for The Population Median
 6.5 The Impact of Nonnormality on Confidence Intervals
 6.6 Some Basic Bootstrap Methods
 6.7 Confidence Interval for The Probability of Success
 6.8 Summary
 6.9 Exercises
 Chapter 7: Hypothesis Testing

Chapter 8: Correlation and Regression
 8.1 Regression Basics
 8.2 Least Squares Regression
 8.3 Dealing with Outliers
 8.4 Hypothesis Testing
 8.5 Correlation
 8.6 Detecting Outliers When Dealing with Two or More Variables
 8.7 Measures of Association: Dealing with Outliers
 8.8 Multiple Regression
 8.9 Dealing with Curvature
 8.10 Summary
 8.11 Exercises

Chapter 9: Comparing Two Independent Groups
 9.1 Comparing Means
 9.2 Comparing Medians
 9.3 Comparing Trimmed Means
 9.4 Tukey's ThreeDecision Rule
 9.5 Comparing Variances
 9.6 RankBased (Nonparametric) Methods
 9.7 Measuring Effect Size
 9.8 Plotting Data
 9.9 Comparing Quantiles
 9.10 Comparing Two Binomial Distributions
 9.11 A Method for Discrete or Categorical Data
 9.12 Comparing Regression Lines
 9.13 Summary
 9.14 Exercises
 Chapter 10: Comparing More than Two Independent Groups
 Chapter 11: Comparing Dependent Groups

Chapter 12: Multiple Comparisons
 12.1 Classic Methods For Independent Groups
 12.2 The Tukey–Kramer Method
 12.3 Scheffé's Method
 12.4 Methods That Allow Unequal Population Variances
 12.5 Anova Versus Multiple Comparison Procedures
 12.6 Comparing Medians
 12.7 TwoWay Anova Designs
 12.8 Methods For Dependent Groups
 12.9 Summary
 12.10 Exercises
 Chapter 13: Categorical Data
 Appendix A: Solutions to Selected Exercises
 Appendix B: Tables
 References
 Index
 End User License Agreement
Product information
 Title: Understanding and Applying Basic Statistical Methods Using R
 Author(s):
 Release date: June 2016
 Publisher(s): Wiley
 ISBN: 9781119061397
You might also like
book
Applied Statistics
Instructs readers on how to use methods of statistics and experimental design with R software Applied …
book
Storytelling with Data: A Data Visualization Guide for Business Professionals
Don't simply show your data—tell a story with it! Storytelling with Data teaches you the fundamentals …
book
Introduction to Probability
Developed from celebrated Harvard statistics lectures, Introduction to Probability provides essential language and tools for understanding …
book
40 Algorithms Every Programmer Should Know
Learn algorithms for solving classic computer science problems with this concise guide covering everything from fundamental …