Statistics with R: A Beginner's Guide

Statistics is made simple with this award-winning guide to using R and applied statistical methods.  With a clear step-by-step approach explained using real world examples, learn the practical skills you need to use statistical methods in your research from an expert with over 30 years of teaching experience. With a wealth of hands-on exercises and online resources created by the author, practice your skills using the data sets and R scripts from the book with detailed screencasts that accompany each script.   This book is ideal for anyone looking to: • Complete an introductory course in statistics • Prepare for more advanced statistical courses • Gain the transferable analytical skills needed to interpret research from across the social sciences • Learn the technical skills needed to present data visually • Acquire a basic competence in the use of R and RStudio.  This edition also includes a gentle introduction to Bayesian methods integrated throughout. The author has created a wide range of online resources, including: over 90 R scripts, 36 datasets, 37 screen casts, complete solutions for all exercises, and 130 multiple-choice questions to test your knowledge. 

Table of Contents:
Chapter 1: Introduction and R Instructions Basic Terminology Data: Qualitative or Quantitative Data: Cross-Sectional or Longitudinal Descriptive Statistics Probability Statistics: Estimation and Inference Chapter 2: Descriptive Statistics: Tabular and Graphical Methods Methods of Summarizing and Displaying Qualitative Data Methods of Summarizing and Displaying Quantitative Data Cross Tabulations and Scatter Plots Chapter 3: Descriptive Statistics: Numerical Methods Measures of Central Tendency Measures of Location Exploratory Data Analysis: The Box Plot Display Measures of Variability The z-Score: A Measure of Relative Location Measures of Association: The Bivariate Case The Geometric Mean Chapter 4: Introduction to Probability Some Important Definitions Counting Rules Assigning Probabilities Events and Probabilities Probabilities of Unions and Intersections of Events Conditional Probability Bayes′ Theorem and Events Chapter 5: Discrete Probability Distributions The Discrete Uniform Probability Distribution The Expected Value and Standard Deviation of a Discrete Random Variable The Binomial Probability Distribution The Poisson Probability Distribution The Hypergeometric Probability Distribution The Hypergeometric Probability Distribution: The General Case Bayes′ Theorem and Discrete Random Variables Chapter 6: Continuous Probability Distributions Continuous Uniform Probability Distribution Normal Probability Distribution Exponential Probability Distribution Optional Material: Derivation of the Cumulative Exponential Probability Func- tion Bayes′ Theorem and Continuous Random Variables Chapter 7: Point Estimation and Sampling Distributions Populations and Samples The Simple Random Sample The Sample Statistic: x, s, and p The Sampling Distribution of x The Sampling Distribution of p Some Other Commonly Used Sampling Methods Bayes′ Theorem: Approximate Bayesian Computation Chapter 8: Confidence Interval Estimation Interval Estimate of µ When σ Is Known Interval Estimate of µ When σ Is Unknown Sample Size Determination in the Case of µ Interval Estimate of p Sample Size Determination in the Case of p Bayes’ Theorem: Confidence Intervals or Credible Intervals Chapter 9: Hypothesis Tests: Introduction, Basic Concepts, and an Example Chapter 10: Hypothesis Tests about Means and Proportions: Applications The Lower-Tail Hypothesis Test about μ: σ Is Known The Two-Tail Hypothesis Test about μ: σ Is Known The Upper-Tail Hypothesis Test about μ: σ Is Unknown The Two-Tail Hypothesis Test about μ: σ is Unknown Hypothesis Tests about p Calculating the Probability of a Type II Error: β Adjusting the Sample Size to Control the Size of β Bayes’ Theorem and an Inferential Approach to p Chapter 11: Comparisons of Means and Proportions The Difference between μ1 and μ2: Independent Samples The Difference between μ1 and μ2: Paired Samples The Difference between p1 and p2: Independent Samples Bayes’ Theorem and the Difference between p1 and p2 Chapter 12: Simple Linear Regression Simple Linear Regression: The Model The Estimated Regression Equation Goodness of Fit: The Coefficient of Determination, r2 The Hypothesis Test about β1 Alternative Approaches to Testing Significance So Far, We Have Tested Only b1. Will We Also Test b0? Assumptions: What Are They? Assumptions: How Are They Validated? Optional Material: Derivation of the Expressions for the Least-Squares Estimates of β0 and β1 Bayes’ Theorem: Using Stan to Estimate the Relationship between Two Variables Chapter 13: Multiple Regression Simple Linear Regression: A Reprise Multiple Regression: The Model Multiple Regression: The Multiple Regression Equation The Estimated Multiple Regression Equation Multiple Regression: The 2 Independent Variable Case Assumptions: What Are They? Can We Validate Them? Tests of Significance: The Overall Regression Model Tests of Signicance: The Independent Variables There Must Be An Easier Way Than This, Right? Using the Estimated Regression Equation for Prediction Independent Variable Selection: The Best-Subsets Method Logistic Regression: The Zero-One Dependent Variable Bayes′ Theorem: Stan and Multiple Regression Analysis

About the Author :
Robert Stinerock has more than 35 years of experience teaching statistics and probability to students at both the undergraduate and graduate level. He currently teaches statistics at two different universities: the Executive MBA Program at Baruch College of The City University of New York and the Quantitative Finance Program at the Stevens Institute of Technology in Hoboken, New Jersey. He has received several awards for excellence in the classroom: the Stevens Howe School Outstanding Undergraduate Teacher Award; the Stevens Alumni Association Outstanding Teacher Award; and the Fairleigh Dickinson Distinguished Faculty Award for Teaching. He has published numerous research articles in academic journals, most recently in the Journal of Macromarketing, the Journal of Business Research, and Geoforum.  The first edition of Statistics with R was the Choice Outstanding Academic Title Award Winner. He earned his Bachelor’s, Master’s, and Ph.D. degrees, all from Columbia University. He and his wife, Jyoti, live in New York City.

Review :
This book is a treasure for both instructors and students. It is written by a master, award-winning teacher with an unparalleled expertise of getting difficult concepts across in a deceptively simple fashion. Written in clear functional English, it both teaches the usual applied statistical methods, as well as provides a gentle introduction to Bayesian methods throughout the book. This is, in essence, more of a new book than just a new edition of an existing one. However, the features that made the first edition so successful have been retained: a student needs only basic algebra to understand the conceptual formulations that are illustrated with hands-on real-life examples that will appeal to students and motivate them to understand the importance of statistics in their daily lives. Introduction to statistics is a busy field, and Stinerock explains the subject in a careful and friendly manner. The inclusion of Bayesian methods in the second edition is an important contribution — when it is encountered at the beginning of the statistical journey, it allows the reader to appreciate the richness of the Bayesian approach without dealing with the analytical and computational complexities of the subject. This book is a wonderful primer for learning both statistics and introductory R programming. It is clearly written, provides straightforward explanations of traditional and Bayesian methods, has a lot of supporting material for instructors and students including numerous practice data sets and solved exercises.