You are here

SAGE online ordering services will be unavailable due to system maintenance on August 12th at 18:00 pm Pacific / 21:00 pm Eastern time. Thank you for your patience and we apologize for the inconvenience.
Statistics with R
Share

Statistics with R
A Beginner's Guide

Second Edition
Additional resources:


December 2022 | 448 pages | SAGE Publications Ltd
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. 
 

 
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

 
Key features
KEY FEATURES:
  • In-depth R tutorials that start from the act of locating and downloading the software and continue through each statistical method
  • Shows readers how to use R in a gradual way that builds confidence and eliminates fear
  • Grounds each statistical method in practical, real world examples that are both timely and interdisciplinary
  • Provides carefully cultivated, jargon-free pedagogy that appeals to different learning styles through a mix of text, visuals, and off the page learning
  • Provides readers with a step-by-step guide to statistical language that includes a variety of resources for reflection, revision, and practice
 

  • Through in-depth R tutorials that start from the act of locating and downloading the software and continue through each statistical method, this book shows students how to use R in a gradual way that builds confidence and eliminates fear, as the software becomes more prevalent on undergraduate and introductory statistics courses.
  • By grounding each statistical method in practical, real world examples that are both timely and interdisciplinary students can see how to overcome their struggles with conceptualizing how to apply statistical knowledge outside of the exams and classroom activities
  • For students who lack confidence in their statistical literacy and get easily frustrated with challenging concepts, the text provides carefully cultivated, jargon-free pedagogy that appeals to different learning styles through a mix of text, visuals and off the page learning.
  • Provides students with a step-by-step guide to statistical language that includes a variety of resources for reflection, revision and practice to ensure students retain and maintain knowledge of key concepts through learning exercises, data sets, formulae lists, accessible definitions, and software screenshots
  • For instructors

    Please select a format:

    Select a Purchasing Option


    Paperback
    ISBN: 9781529753523
    $54.00

    Hardcover
    ISBN: 9781529753530
    $165.00