Statistics for People Who (Think They) Hate Statistics

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Part I: Yippee! I'm in Statistics Chapter 1: Statistics or Sadistics? It's Up to You Why Statistics? A Five-Minute History of Statistics Statistics: What It Is (and Isn't) Tooling Around With the Analysis ToolPak What Am I Doing in a Statistics Class? Ten Ways to Use This Book (and Learn Statistics at the Same Time!) About Those Icons Key to Difficulty Icons Key to "How Much Excel" Icons Little Chapter 1a. All You Need to Know About Formulas and Functions What's a Formula? What's a Function? Little Chapter 1b. All You Need to Know About Using the Amazing Analysis ToolPak A Look at the Analysis ToolPak Don't Have It? Little Chapter 1c. Mac Lovers Rejoice! StatPlus: The Mac Alternative to the Analysis ToolPak Little Chapter 1c. For Mac Lovers Who are Still using Version 2011: Rejoice!! And, for Mac Lovers Who are new to Version 2016, Rejoice More!!! Part II: Sigma Freud and Descriptive Statistics Chapter 2: Computing and Understanding Averages: Means to an End Computing the Mean Computing a Weighted Mean Computing the Median Computing the Mode Using the Amazing Analysis ToolPak to Compute Descriptive Statistics When to Use What Chapter 3: Vive la Difference: Understanding Variability Why Understanding Variability Is Important Computing the Range Computing the Standard Deviation Computing the Variance And Now... Using Excel's VAR.S Function The Standard Deviation Versus the Variance Using the Amazing Analysis ToolPak (Again!) Chapter 4: A Picture Really Is Worth a Thousand Words Why Illustrate Data? Ten Ways to a Great Figure (Eat Less and Exercise More?) First Things First: Creating a Frequency Distribution The Plot Thickens: Creating a Histogram Fat and Skinny Frequency Distributions Excellent Charts Excellent Charts Part Deux: Making Charts Pretty Other Cool Charts Chapter 5: Ice Cream and Crime: Computing Correlation Coefficients What Are Correlations All About? Computing a Simple Correlation Coefficient More Excel-Bunches of Correlations a la Excel Using the Amazing Analysis ToolPak to Compute Correlations Understanding What the Correlation Coefficient Means As More Ice Cream Is Eaten... the Crime Rate Goes Up (or Association Versus Causality) Other Cool Correlations Chapter 6: Just the Truth: An Introduction to Understanding Reliability and Validity An Introduction to Reliability and Validity All About Measurement Scales Reliability-Doing It Again Until You Get It Right Validity-Whoa! What Is the Truth? A Last, Friendly Word Validity and Reliability: Really Close Cousins Part III: Taking Chances for Fun and Profit Chapter 7: Hypotheticals and You: Testing Your Questions So You Want to Be a Scientist... The Null Hypothesis The Research Hypothesis What Makes a Good Hypothesis? Chapter 8: Are Your Curves Normal? Probability and Why It Counts Why Probability? The Normal Curve (aka the Bell-Shaped Curve) Our Favorite Standard Score: The z Score Part IV: Significantly Different: Using Inferential Statistics Chapter 9: Significantly Significant: What It Means for You and Me The Concept of Significance Significance Versus Meaningfulness An Introduction to Inferential Statistics An Introduction to Tests of Significance An Introduction to Tests of Significance Chapter 10: Only the Lonely: The One-Sample Z-Test Introduction to the One-Sample Z-Test Computing the Test Statistic Using the Excel Z.TEST Function to Compute the z Value Chapter 11: t(ea) for Two: Tests Between the Means of Different Groups Introduction to the t-Test for Independent Samples Computing the Test Statistic Using the Amazing Analysis ToolPak to Compute the t Value Special Effects: Are Those Differences for Real? Chapter 12: t(ea) for Two (Again): Tests Between the Means of Related Groups Introduction to the t-Test for Dependent Samples Computing the Test Statistic Using the Amazing Analysis ToolPak to Compute the t Value Chapter 13: Two Groups Too Many? Try Analysis of Variance Introduction to Analysis of Variance Computing the F-Test Statistic Using the Amazing Analysis ToolPak to Compute the F Value Chapter 14: Two Too Many Factors: Factorial Analysis of Variance-A Brief Introduction Introduction to Factorial Analysis of Variance The Main Event: Main Effects in Factorial ANOVA Even More Interesting: Interaction Effects Computing the ANOVA F Statistic Using the Amazing Analysis ToolPak Chapter 15: Cousins or Just Good Friends? Testing Relationships Using the Correlation Coefficient Introduction to Testing the Correlation Coefficient Computing the Test Statistic Chapter 16: Predicting Who'll Win the Super Bowl: Using Linear Regression What Is Prediction All About? The Logic of Prediction Drawing the World's Best Line (for Your Data) Computing the Regression Equation Using the Amazing Analysis ToolPak How Good Is Our Prediction? The More Predictors, the Better? Maybe Chapter 17: What to Do When You're Not Normal: Chi-Square and Some Other Nonparametric Tests Introduction to Nonparametric Statistics Introduction to One-Sample Chi-Square Computing the Chi-Square Test Statistic Other Nonparametric Tests You Should Know About Chapter 18: Some Other (Important) Statistical Procedures You Should Know About Post Hoc Comparisons Multivariate Analysis of Variance Repeated Measures Analysis of Variance Analysis of Covariance Multiple Regression Logistic Regression Factor Analysis Data Mining Path Analysis Structural Equation Modeling Chapter 19: A Statistical Software Sampler Selecting the Perfect Statistics Software What's Out There Chapter 20: (Mini) Data Mining: A Introduction to Getting The Most Out Of Your BIG Data Part V: Ten Things You'll Want to Know and Remember Chapter 21: The Ten (or More) Best (and Most Fun) Internet Sites for Statistics Stuff How About Studying Statistics in Stockholm? Calculators Galore! Who's Who and What's Happened It's All Here HyperStat Data? You Want Data? More and More Resources Plain, But Fun Online Statistical Teaching Materials And, of Course, YouTube... Chapter 22: The Ten Commandments of Data Collection Appendix A: Excel-erate Your Learning: All You Need to Know About Excel Appendix B: Tables Appendix C: Data Sets Appendix D: Answers to Practice Questions Appendix E: Math - Just the Basics Appendix F: The Reward: The Brownie Recipe

Neil J. Salkind received his PhD in human development from the University of Maryland, and after teaching for 35 years at the University of Kansas, he was Professor Emeritus in the Department of Psychology and Research in Education, where he collaborated with colleagues and work with students. His early interests were in the area of children's cognitive development, and after research in the areas of cognitive style and (what was then known as) hyperactivity, he was a postdoctoral fellow at the University of North Carolina's Bush Center for Child and Family Policy. His work then changed direction to focus on child and family policy, specifically the impact of alternative forms of public support on various child and family outcomes. He delivered more than 150 professional papers and presentations; written more than 100 trade and textbooks; and is the author of Statistics for People Who (Think They) Hate Statistics (SAGE), Theories of Human Development (SAGE), and Exploring Research (Prentice Hall). He has edited several encyclopedias, including the Encyclopedia of Human Development, the Encyclopedia of Measurement and Statistics, and the Encyclopedia of Research Design. He was editor of Child Development Abstracts and Bibliography for 13 years. He lived in Lawrence, Kansas, where he liked to read, swim with the River City Sharks, work as the proprietor and sole employee of big boy press, bake brownies (see www.statisticsforpeople.com for the recipe), and poke around old Volvos and old houses.

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