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An R Companion for Applied Statistics I
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An R Companion for Applied Statistics I
Basic Bivariate Techniques



256 pages | SAGE Publications, Inc

An R Companion for Applied Statistics I: Basic Bivariate Techniques breaks the language of the R software down into manageable chunks in order to help students learn how to use it. R is a powerful, flexible, and free tool. However, the flexibility—which eventually becomes a great asset—can make the initial learning curve appear steep. This book introduces a few key aspects of the R tool. As readers become comfortable with these aspects, they develop a foundation from which to more thoroughly explore R and the packages available for it. This introduction does not explain every possible way to analyze data or perform a specific type of analysis. Rather, it focuses on the analyses that are traditionally included in an undergraduate statistics course and provides one or two ways to run these analyses in R. Datasets and scripts to run the examples are provided on an accompanying website. The book has been designed to be an R companion to Warner's Applied Statistics I, Third Edition, and includes end-of-chapter instructions for replicating the examples from that book in R. However, this text can also be used as a stand-alone R guide, without reference to the Warner text.

 
Chapter 1: Introduction: What is R?
Downloading R and RStudio

 
Creating a Project Folder

 
Getting Acquainted with the RStudio Environment

 
Summary of Key Functions

 
 
Chapter 2: Basic Tasks in R
Coding in R: Object Oriented Programming

 
Creating Data

 
Exporting Data

 
Importing Data

 
Converting Variables

 
Summary of Key Functions

 
 
Chapter 3: Frequency Tables
Frequency Tables with Quantitative Variables

 
Summary of Key Functions

 
 
Chapter 4: Descriptive Statistics
Describing Central Tendency

 
Describing Variability

 
Summary of Key Functions

 
 
Chapter 5: Visualizing Data: Bar Charts, Histograms, and Boxplots
Visualizing Categorical Variables

 
Visualizing Quantitative Variables

 
Visualizing and Accounting for a Second Variable

 
Summary of Key Functions

 
 
Chapter 6: Evaluating Score Locations: Introducing the Normal Distribution and Z Scores
Getting Familiar with New Data Frame and Variables

 
Cumulative Percentage

 
z Scores

 
Addressing Normality

 
Summary of Key Functions

 
 
Chapter 7: Sampling Error and Confidence Intervals
Monte Carlo Simulations

 
Confidence Intervals

 
Summary of Key Functions

 
 
Chapter 8: One Sample t Test: Introduction to Statistical Significance Tests
Checking Assumptions

 
Performing One-Sample t Tests

 
Presenting Results

 
Considering Alternatives

 
Summary of Key Functions

 
 
Chapter 9: Significance Tests Continued: Effect Size and Power
Estimating the Needed Sample Size

 
Estimating the Obtained Power

 
Summary of Key Functions

 
 
Chapter 10: Bivariate Pearson Correlation
Checking Assumptions

 
Performing Pearson's Bivariate Correlation

 
Considering Alternatives

 
Summary of Key Functions

 
 
Chapter 11: Bivariate Regression
Checking Assumptions

 
Performing Bivariate Regression

 
 
Chapter 12: Independent-Samples t Test
Checking Assumptions

 
Performing Independent-Samples t Tests

 
Presenting Results

 
Considering Alternatives

 
Summary of Key Functions

 
 
Chapter 13: One-Way Between-Subjects Analysis of Variance
Checking Assumptions

 
Performing One-Way Between-Subjects ANOVA Tests

 
Presenting Results

 
Considering Alternatives

 
Summary of Key Functions

 
 
Chapter 14: Paired-Samples t Test
Checking Assumptions

 
Performing Paired-Samples t Tests

 
Presenting Results

 
Considering Alternatives

 
Summary of Key Functions

 
 
Chapter 15: One-Way Repeated-Measures ANOVA Tests
Checking Assumptions

 
Performing One-Way Repeated-Measures ANOVA Tests

 
Presenting Results

 
Considering Alternatives

 
Summary of Key Functions

 
 
Chapter 16: Factorial Analysis of Variance
Checking Assumptions

 
Performing Two-Way Between-Subjects ANOVA Tests

 
Presenting Results

 
Considering Alternatives

 
Summary of Key Functions

 
 
Chapter 17: Chi Square Test of Independence
Checking Assumptions

 
Performing Chi Square Tests of Independence

 
Presenting Results

 
Considering Alternatives

 
 
Chapter 18: Parting THoughts About R
Moving Forward

 
Continuing to Learn R

 

Rasco's An R Companion to Applied Statistics I is an excellent companion to Warner's seminal statistics text. If you've ever wanted to use R in place of commercial statistics, this is the book that will help you achieve that goal.

Jeffrey Savage
Cornerstone University

Rasco's text has taken the complexity out of using R for students who are learning the system. His engaging text gives step by step instructions with visuals. He thoroughly covers the relevance and assumptions of each statistical analysis.

Lina Racicot
American International College

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Danney Rasco

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