Statistics with R

A Beginner's Guide
Second Edition
Robert Stinerock - Baruch College, City University of New York, USA
Statistics with R
November 2022 | 448 pages | Sage UK
Create Flyer

If you’re in the Americas, please visit our Sage College Publishing website to purchase or sample this book:

Go to College Publishing Website

Description

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. 
 

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 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

Description

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. 
 

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 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
SAGE Publishing Logo

Statistics with R

A Beginner's Guide

Robert Stinerock
Cancel Print

November 2022 | 448 pages | Sage UK

Format Published Date ISBN Price

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

Recent Product Reviews:

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.
Saroja Subrahmanyan, Professor of Marketing, School of Economics and Business Administration, Saint Mary's College of California
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.
Eric Novik, Founder and CEO of Generable Inc. and Research Scientist at NYU Steinhardt
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.
Swati Mukerjee, Professor of Economics at Bentley University

Recommendations