Statistics for People Who (Think They) Hate Statistics Using R 1st edition by Neil Salkind, Leslie Shaw – Ebook PDF Instant Download/Delivery: 154432457X, 978-1544324579
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ISBN 10: 154432457X
ISBN 13: 978-1544324579
Author: Neil Salkind, Leslie Shaw
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.
Statistics for People Who (Think They) Hate Statistics Using R 1st Table of contents:
Part I • Yippee! I’m in Statistics
Chapter 1 • Statistics or Sadistics? It’s Up to You
What You Will Learn in This Chapter
Why Statistics?
And Why R?
A 5-Minute History of Statistics
Statistics: What It Is (and Isn’t)
What Are Descriptive Statistics?
What Are Inferential Statistics?
In Other Words . . .
What Am I Doing in a Statistics Class?
Ten Ways to Use This Book (and Learn Statistics at the Same Time!)
About Those Icons
What Else Does This Book Contain?
Key to Difficulty Icons
Glossary
Real-World Stats
Summary
Time to Practice
Student Study Site
Part II • Welcome to the Interesting, Useful, Flexible, Fun, and (Very) Deep Worlds of R and RStudio
Chapter 2 • Here’s Why We Love R and How to Get Started
What You Will Learn in This Chapter
A Very Short History of R
The Pluses of Using R
The Minuses of Using R
Other Reasons to Use R?
A Short Note to You (and to Your Instructor) About Open Source (Again!)
Where to Find and Download R
The Opening R Screen
Packages and Functions in R
A Note About Formatting
Bunches of Data—Free!
Getting R Help
Getting Help on Help
Some Important Lingo
RStudio
Where to Find RStudio and How to Install It
Take RStudio for a Test Ride
Ordering From RStudio
File
Edit
Code
View
Plots
Session
Build
Debug
Profile
Tools
Help(!)
Summary
Time to Practice
Student Study Site
Chapter 3 • Using RStudio: Much Easier Than You Think
What You Will Learn in This Chapter
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
The Basics: +, –, ?, *, and More: Using Operators
Working With Data
Creating a Data Set From One Vector
More Vectors
Let’s See What’s in the Workspace
Removing an Object From the Workspace
Returning an Object to the Workspace
Seeing the Contents of an Object
Reading in Established Data Sets
Using the Easiest R Command in the Galaxy
Oops! How Do You Correct Console Errors?
Pointing and Clicking to Open a Data Set
Computing Some Statistics
Computing Some Simple Descriptive Statistics
Computing a Correlation Coefficient (Your First Time!)
Creating a Plot
Ten Important Things to Remember About R and RStudio (but Not Necessarily in Order of Importance)
Summary
Time to Practice
Student Study Site
Part III • Σigma Freud and Descriptive Statistics
Chapter 4 • Computing and Understanding Averages: Means to an End
What You Will Learn in This Chapter
Computing the Mean
Computing a Weighted Mean
Computing the Median
Computing the Mode
Apple Pie à la Bimodal
When to Use What Measure of Central Tendency (and All You Need to Know About Scales of Measurement for Now)
A Rose by Any Other Name: The Nominal Level of Measurement
Any Order Is Fine With Me: The Ordinal Level of Measurement
1 + 1 = 2: The Interval Level of Measurement
Can Anyone Have Nothing of Anything? The Ratio Level of Measurement
In Sum . . .
Using the Computer to Compute Descriptive Statistics
Calculating the Mean
Finding the 50th Percentile: The Median
A Rose by Any Other Name: The Nominal Level of Measurement
Real-World Stats
Summary
Time to Practice
Student Study Site
Chapter 5 • Understanding Variability: Vive la Différence
What You Will Learn in This Chapter
Why Understanding Variability Is Important
Computing the Range
Computing the Standard Deviation
Step-by-Step
Why n − 1? What’s Wrong With Just n?
What’s the Big Deal?
Computing the Variance
The Standard Deviation Versus the Variance
Using R to Compute Measures of Variability
Real-World Stats
Summary
Time to Practice
Student Study Site
Chapter 6 • Creating Graphs: A Picture Really Is Worth a Thousand Words
What You Will Learn in This Chapter
Why Illustrate Data?
Ten Ways to a Great Graphic
First Things First: Creating a Frequency Distribution
The Classiest of Intervals
The Plot Thickens: Creating a Histogram
The Tallyho Method
The Next Step: A Frequency Polygon
Cumulating Frequencies
Other Cool Ways to Chart Data
Bar Charts
Column Charts
Line Charts
Using the Computer (R, That Is) to Illustrate Data
Creating a Histogram
Creating a Bar Chart
Creating a Line Graph
Real-World Stats
Summary
Time to Practice
Student Study Site
Chapter 7 • Computing Correlation Coefficients: Ice Cream and Crime
What You Will Learn in This Chapter
What Are Correlations All About?
Types of Correlation Coefficients: Flavor 1 and Flavor 2
Computing a Simple Correlation Coefficient
A Visual Picture of a Correlation: The Scatterplot
Bunches of Correlations: The Correlation Matrix
Understanding What the Correlation Coefficient Means
Using-Your-Thumb (or Eyeball) Method
A Determined Effort: Squaring the Correlation Coefficient
As More Ice Cream Is Eaten . . . the Crime Rate Goes Up (or Association vs. Causality)
Using RStudio to Compute the Correlation Coefficient
Computing the Correlation Coefficient by Entering Data
R Output
Computing the Correlation Coefficient by Importing a File
Creating a Scatterplot (or Scattergram or Whatever You Want to Call It)
Things Don’t Have to Be Linear Part 2
Other Cool Correlations
Parting Ways: A Bit About Partial Correlation
Using R to Compute Partial Correlations
Computing the Correlation Between Three Variables
Understanding the R Output for Partial Correlation
Other Ways to Compute the Correlation Coefficient
Real-World Stats
Summary
Time to Practice
Student Study Site
Chapter 8 • An Introduction to Understanding Reliability and Validity: Just the Truth
What You Will Learn in This Chapter
An Introduction to Reliability and Validity
What’s Up With This Measurement Stuff?
Reliability: Doing It Again Until You Get It Right
Test Scores: Truth or Dare?
Observed Score = True Score + Error Score
Different Types of Reliability
Test–Retest Reliability
Parallel Forms Reliability
Internal Consistency Reliability
Computing Cronbach’s Alpha
Using R to Calculate Cronbach’s Alpha
Understanding the R Output
Interrater Reliability
Computing Interrater Reliability
How Big Is Big? Finally: Interpreting Reliability Coefficients
And If You Can’t Establish Reliability . . . Then What?
Just One More Thing
Validity: Whoa! What Is the Truth?
Different Types of Validity
And If You Can’t Establish Validity . . . Then What?
A Last Friendly Word
Validity and Reliability: Really Close Cousins
Real-World Stats
Summary
Time to Practice
Student Study Site
Part IV • Taking Chances for Fun and Profit
Chapter 9 • Hypotheticals and You: Testing Your Questions
What You Will Learn in This Chapter
So You Want to Be a Scientist
Samples and Populations
The Null Hypothesis
The Purposes of the Null Hypothesis
The Research Hypothesis
The Nondirectional Research Hypothesis
The Directional Research Hypothesis
Some Differences Between the Null Hypothesis and the Research Hypothesis
What Makes a Good Hypothesis?
Real-World Stats
Summary
Time to Practice
Student Study Site
Chapter 10 • Probability and Why It Counts: Fun With a Bell-Shaped Curve1
What You Will Learn in This Chapter
Why Probability?
The Normal Curve (a.k.a. the Bell-Shaped Curve)
Hey, That’s Not Normal!
More Normal Curve 101
Our Favorite Standard Score: The z Score
What z Scores Represent
What z Scores Really Represent
Hypothesis Testing and z Scores: The First Step
Using R to Compute z Scores
Fat and Skinny Frequency Distributions
Average Value
Variability
Skewness
Kurtosis
Real-World Stats
Summary
Time to Practice
Student Study Site
Part V • Significantly Different: Using Inferential Statistics
Chapter 11 • Significantly Significant: What It Means for You and Me
What You Will Learn in This Chapter
The Concept of Significance
If Only We Were Perfect
The World’s Most Important Table (for This Semester Only)
More About Table 11.1
Back to Type I Errors
Significance Versus Meaningfulness
An Introduction to Inferential Statistics
How Inference Works
How to Select What Test to Use
Here’s How to Use the Chart
An Introduction to Tests of Significance
How a Test of Significance Works: The Plan
Here’s the Picture That’s Worth a Thousand Words
Be Even More Confident
Real-World Stats
Summary
Time to Practice
Student Study Site
Chapter 12 • The One-Sample z Test: Only the Lonely
What You Will Learn in This Chapter
Introduction to the One-Sample z Test
The Path to Wisdom and Knowledge
Computing the z Test Statistic
So How Do I Interpret z = 2.40, p < .05?
Using R to Perform a z Test
Special Effects: Are Those Differences for Real?
Understanding Effect Size
Real-World Stats
Summary
Time to Practice
Student Study Site
Chapter 13 • t(ea) for Two: Tests Between the Means of Different Groups
What You Will Learn in This Chapter
Introduction to the t Test for Independent Samples
The Path to Wisdom and Knowledge
Computing the t Test Statistic
Time for an Example
So How Do I Interpret t(58) = −0.14, p > .05?
The Effect Size and t(ea) for Two
Computing and Understanding the Effect Size
Two Very Cool Effect Size Calculators
Using R to Perform a t Test
Real-World Stats
Summary
Time to Practice
Student Study Site
Chapter 14 • t(ea) for Two (Again): Tests Between the Means of Related Groups
What You Will Learn in This Chapter
Introduction to the t Test for Dependent Samples
The Path to Wisdom and Knowledge
Computing the t Test Statistic
So How Do I Interpret t(24) = 2.45, p < .05?
Using R to Perform a t Test
The Effect Size for t(ea) for Two (Again)
Real-World Stats
Summary
Time to Practice
Student Study Site
Chapter 15 • Two Groups Too Many? Try Analysis of Variance
What You Will Learn in This Chapter
Introduction to Analysis of Variance
The Path to Wisdom and Knowledge
Different Flavors of ANOVA
Computing the F Test Statistic
So How Do I Interpret F(2, 27) = 8.80, p < .05?
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
Student Study Site
Chapter 16 • Two Too Many Factors: Factorial Analysis of Variance—A Brief Introduction
What You Will Learn in This Chapter
Introduction to Factorial Analysis of Variance
The Path to Wisdom and Knowledge
A New Flavor of ANOVA
All of Those Effects
The Place to Start: Interaction Effects
The Main Event: Main Effects in Factorial ANOVA
The Other Rows
Plotting the Means by Group
Even More Interesting Interaction Effects
Assumptions About Variances
Using R to Compute the F Ratio
Computing the Effect Size for Factorial ANOVA
Real-World Stats
Summary
Time to Practice
Student Study Site
Chapter 17 • Testing Relationships Using the Correlation Coefficient: Cousins or Just Good Friends?
What You Will Learn in This Chapter
Introduction to Testing the Correlation Coefficient
The Path to Wisdom and Knowledge
Computing the Test Statistic
So How Do I Interpret r(28) = .437, p < .05?
Causes and Associations (Again!)
Significance Versus Meaningfulness (Again, Again!)
Using R to Compute a Correlation Coefficient (Again)
Real-World Stats
Summary
Time to Practice
Student Study Site
Chapter 18 • Using Linear Regression: Predicting the Future
What You Will Learn 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
Understanding the R Output
The More Predictors the Better? Maybe
The Big Rule(s) When It Comes to Using Multiple Predictor Variables
Real-World Stats
Summary
Time to Practice
Student Study Site
Part VI • More Statistics! More Tools! More Fun!
Chapter 19 • Chi-Square and Some Other Nonparametric Tests: What to Do When You’re Not Normal
What You Will Learn in This Chapter
Introduction to Nonparametric Statistics
Introduction to the Goodness-of-Fit (One-Sample) Chi-Square
Computing the Goodness-of-Fit Chi-Square Test Statistic
So How Do I Interpret χ2(2) = 20.6, p < .05?
Introduction to the Test of Independence Chi-Square
Computing the Test of Independence Chi-Square Test Statistic
Using R to Perform Chi-Square Tests
Goodness of Fit and R
Test of Independence and R
Understanding the R Output
Other Nonparametric Tests You Should Know About
Real-World Stats
Summary
Time to Practice
Student Study Site
Chapter 20 • Some Other (Important) Statistical Procedures You Should Know About
What You Will Learn in This Chapter
Multivariate Analysis of Variance
MANOVA in R
Repeated-Measures Analysis of Variance
Repeated-Measures Analysis of Variance in R
Analysis of Covariance
ANCOVA in R
Multiple Regression
Multiple Regression in R
Multilevel Models
Multilevel Models in R
Meta-Analysis
Meta-Analysis in R
Logistic Regression
Logistic Regression in R
Factor Analysis
Factor Analysis in R
Path Analysis
Path Analysis in R
Structural Equation Modeling
Structural Equation Modeling in R
Summary
Student Study Site
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