# Statistics for People Who (Think They) Hate Statistics Using R

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

Neil J. Salkind’s bestselling *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 text for use with the R software, **Statistics for People Who (Think They) Hate Statistics Using R**. New co-author Leslie A. Shaw carries forward Salkind’s signature humorous, personable, and informative approach as the text guides students in a grounding of statistical basics and R computing, and the application of statistics to research studies. The book covers various basic and advanced statistical procedures, from correlation and graph creation to analysis of variance, regression, non-parametric tests, and more.

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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 |

### Supplements

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**password-protected site**for complete and protected access to all text-specific instructor resources; **R syntax and data files**are available for download and use with exercises in the book;**test banks**that provide a diverse range of ready-to-use options that save you time. You can also easily edit any question and/or insert your own personalized questions;**multimedia content featuring original screencast tutorial videos that demonstrate setting up the data and running selected problems in R**meet the learning needs of today’s media-savvy students and bring concepts to life; and**editable, chapter-specific PowerPoint® slides**that offer complete flexibility for creating a multimedia presentation for your course.

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**open-access site**that makes it easy for students to maximize their study time, anywhere, anytime; **R syntax and data files**are available for download and use with exercises in the book;**video resources**that bring concepts to life, are tied to learning objectives, and are curated and produced exclusively for this text, featuring:- Screencast tutorial videos demonstrate setting up the data and running selected problems in R

**eFlashcards**that strengthen understanding of key terms and concepts; and**eQuizzes**that allow students to practice and assess how much they’ve learned and where they need to focus their attention.

“Salkind is the master of presenting options for data analysis in a logical, straightforward manner so students are able to focus on the meaning rather than the math of statistics.”

**Delta State University**

“The (late) Dr. Salkind's text continues (with Dr. Shaw's R-integration) to be a readable statistical text that provides a gentle yet surprisingly comprehensive introduction to statistics. For anyone teaching a basic level, introductory level, or first class in statistics, I cannot think of a better text. This R update adds an important element to the Excel and SPSS versions of this inimitable text.”

**Cornerstone University**

“There are many textbooks on R, textbooks on Statistics, and textbooks on R and Statistics that are extremely technical, and difficult to read and use. This textbook is the golden mean!”

**Wayne State University**

“The text makes statistics accessible for even the most ‘math-phobic’ student and ‘demystifies’ the world of R. It is the most comprehensive statistics textbook that walks students through both the mathematical and software steps of doing statistics.”

**University of Texas at El Paso**

“The value of this text is that it presents complex ideas in a way that people can relate to—using examples, walking through steps, and providing all the additional tools needed to succeed in an introduction to statistics course.”

**East Tennessee State University**

“This text is a thorough and effective packaging of statistical analysis and computational techniques in the R language, which would be highly useful to students from a variety of backgrounds.”

**University of North Carolina, Charlotte**

“Salkind and Shaw do an excellent job of presenting difficult statistical concepts and tools in a highly accessible manner. One of the best introductory statistics textbooks available.”

**Southern Illinois University**

“Salkind's book has always been the very best text for introducing my undergraduate students to statistics. Now, it introduces R as well. I will recommend this book to everyone.”

**Brigham Young University, Idaho**

“As with previous editions of this book by Dr. Salkind, this textbook captures the essence of Dr. Salkind's style, talent, and expertise in explaining statistics. Including information and instruction on R software for analysis is a benefit since students can now access a free software program.”

**Ursuline College**