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Statistics with R
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Statistics with R
A Beginner's Guide

Second Edition
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November 2022 | 488 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, practice your skills using the data sets and R scripts from the book with handy screencasts to accompany you. 

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.  

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
 
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

 

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