# Statistics for People Who (Think They) Hate Statistics Using R - International Student Edition

- Neil J. Salkind
- Leslie A. Shaw - Cornell University, USA

Neil J. Salkind’s best-selling **Statistics for People Who (Think They) Hate Statistics** has been helping ease student anxiety around an often intimidating subject since it first published in 2000. Now the bestselling SPSS and Excel versions are joined by a first edition of the text for use with the R software. New co-author Leslie A. Shaw carries forward Neil’s signature humorous, personable, and informative approach. The text guides students through various statistical procedures, beginning with descriptive statistics, correlation, and graphical representation of data, and ending with inferential techniques and analysis of variance.

Features and benefits:

**Lots of support for getting started with R**: Included are two introductory chapters on R and on R Studio, plus an appendix on other R packages and resource sites.**Step-by-step demonstrations of each statistical procedure in R**: The authors show how to import the dataset, enter the syntax to run the test, and understand the output.**Additional resources make it easy to transition to this text, and to R:**Code and datasets are available on an accompanying website, which also includes screencast R tutorial videos for students, and PowerPoint slides and additional test questions for instructors.

What You Will Learn in This Chapter |

Why Statistics? |

A 5-Minute History of Statistics |

Statistics: What it is and Isn’t |

What am I doing in a Statistics Class? |

Ten Ways to Use this Book (and Learn Statistics at the Same Time) |

Key to Difficulty Icons |

Glossary |

Real-World Stats |

Summary |

Time to Practice |

What You Will Learn in This Chapter |

A Very Short History of R |

The Plusses of Using R |

Where to Find and Download R |

The Opening R Screen |

A Note About Formatting |

Bunches of Data – Free! |

Getting R Help |

Some Important Lingo |

RStudio |

Where to Find RStudio and How to Install It |

Ordering from RStudio |

Summary |

Time to Practice |

What You Will Learn in This Chapter |

Why RStudio (and Why Not Just R?) |

The Grand Tour and All About Those Four Panes |

RStudio Pane Goodies |

Showing Your Stuff – Working With Menus and Tabs and A Sample Data Analysis Using RStudio |

Working with Data |

Next Step: Using and Importing Datasets |

Reading in Established Datasets |

Computing Some Statistics |

Summary |

Time to Practice |

What You Will Learn in This Chapter |

What You Will Learn in This Chapter Computing the Mean |

Computing the Median |

Computing the Mode |

When to Use What Measure of Central Tendency (and All You Need to Know About Scales of Measurement for Now) |

Using the Computer to Compute Descriptive Statistics |

Real World Stats |

Summary |

Time to Practice |

What You Will Learn in This Chapter |

Why Understanding Variability is Important |

Computing the Range |

Computing the Standard Deviation |

Computing the Variance |

Using R to Compute Measures of Variability |

Real World Stats |

Summary |

Time to Practice |

What You Will Learn in This Chapter |

Why Illustrate Data? |

Ten Ways to a Great Graphic |

First Things First: Creating a Frequency Distribution |

The Plot Thickens: Creating a Histogram |

The Next Step: A Frequency Polygon |

Other Cool Ways to Chart Data |

Using the Computer (R, That Is) to Illustrate Data |

Real World Stats |

Summary |

Time to Practice |

What You Will Learn in This Chapter |

What are Correlations All About? |

Computing a Simple Correlation Coefficient |

Understanding What the Correlation Coefficient Means |

A Determined Effort: Squaring the Correlation Coefficient |

Other Cool Correlations |

Parting Ways: A Bit About Partial Correlations |

Summary |

Time to Practice |

What You Will Learn in This Chapter |

An Introduction to Reliability and Validity |

Reliability: Doing it Again Until You Get it Right |

Different Types of Reliability |

How Big is Big? Finally: Interpreting Reliability Coefficients |

Validity: Whoa! What is the Truth? |

A Last Friendly Word |

Validity and Reliability: Really Close Cousins |

Real World Stats |

Summary |

Time to Practice |

What You Will Learn in This Chapter |

So You Want to Be a Scientist |

Samples and Populations |

The Null Hypothesis |

The Research Hypothesis |

What Makes a Good Hypothesis? |

Real-World Stats |

Summary |

Time to Practice |

What You’ll Learn About in this Chapter |

Why Probability? |

The Normal Curve (A.K.A The Bell-Shaped Curve) |

Our Favorite Standard Score |

Fat and Skinny Frequency Distributions |

Real World Stats |

Summary |

Time to Practice |

What You’ll Learn About in this Chapter |

The Concept of Significance |

Significance Versus Meaningfulness |

An Introduction to Inferential Statistics |

An Introduction to Tests of Significance |

Be Even More Confident |

Real World Stats |

Summary |

Time to Practice |

What You’ll Learn About in this Chapter |

Introduction to the One-Sample Z-Test |

The Path to Wisdom and Knowledge |

Computing the Z-Test Statistic |

Using R to Perform a Z-Test |

Special Effects: Are Those Differences for Real? |

Real World Stats |

Summary |

Time to Practice |

What You’ll Learn About in This Chapter |

Introduction to the t-test for Independent Samples |

The Path to Wisdom and Knowledge |

Computing the t-Test Statistic |

Using R to Perform a t-Test |

Real-World Stats |

Summary |

Time to Practice |

What You’ll Learn About in This Chapter |

Introduction of the t-Test for Dependent Samples |

The Path to Wisdom and Knowledge |

Computing the t-Test Statistic |

Using R to Perform a t-Test |

The Effect Size for t(ea) for Two (Again) |

Real World Stats |

Summary |

Time to Practice |

Introduction to Analysis of Variance |

The Path to Wisdom and Knowledge |

Different Flavors of ANOVA |

Computing the F-test Statistic |

Using R to Compute the F Ratio |

The Effect Size for One-Way ANOVA |

But Where is the Difference? |

Real World Stats |

Summary |

Time to Practice |

What You’ll Learn About in This Chapter |

Introduction to Factorial Analysis of Variance |

The Path to Wisdom and Knowledge |

A New Flavor of ANOVA |

All of These Effects |

Even More Interesting Interaction Effects |

Using R to Compute the F Ratio |

Computing the Effect Size for Factorial ANOVA |

Real World Stats |

Summary |

Time to Practice |

What You’ll Learn About in This Chapter |

Introduction to Testing the Correlation Coefficient |

The Path to Wisdom and Knowledge |

Computing the Test Statistic |

Using R to Compute a Correlation Coefficient (Again) |

Real World Stats |

Summary |

Time to Practice |

What You’ll Learn About in this Chapter |

Introduction to Linear Regression |

What is Prediction All About? |

The Logic of Prediction |

Drawing the World’s Best Line (for Your Data) |

How Good is Your Prediction? |

Using R to Compute the Regression Line |

The More Predictors the Better? Maybe |

Real World Stats |

Summary |

Time to Practice |

What You’ll Learn About in this Chapter |

Introduction toe Nonparametric Statistics |

Introduction to the Goodness of Fit (One-Sample) Chi-Square |

Computing the Goodness of Fit Chi-Square Test Statistic |

Introduction to the Test of Independence Chi-Square |

Computing the Test of Independence Chi-Square Test Statistic |

Using R to Perform Chi-Square Tests |

Summary |

Time to Practice |

What You’ll Learn About in this Chapter |

Multivariate Analysis of Variance |

Repeated Measures Analysis of Variance |

Analysis of Covariance |

Multiple Regression |

Multilevel Models |

Meta-Analysis |

Logistic Regression |

Factor Analysis |

Path Analysis |

Structural Equation Modeling |

Summary |