Statistics with R

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. 

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 Bachelors, Masters, and Ph.D. degrees, all from Columbia University.

He and his wife, Jyoti, live in New York City.

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 Chapter 9: Hypothesis Tests: Introduction, Basic Concepts, and an Example Chapter 10: Hypothesis Tests about Means and Proportions: Applications Chapter 11: Comparisons of Means and Proportions Chapter 12: Simple Linear Regression 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