Synopses & Reviews
The fun and easy way to enhance your grasp of statistics
Need to expand your statistics knowledge and move on to Statistics II? This friendly, hands-on guide gives you the skills you need to take on multiple regression, analysis of variance (ANOVA), Chi-square tests, nonparametric procedures, and other key topics. Statistics II For Dummies also provides plenty of test-taking strategies as well as real-world applications that make data analysis a snap, whether you're in the classroom or at work.
Begin with the basics — review the highlights of Stats I and expand on simple linear regression, confidence intervals, and hypothesis tests
Start making predictions — master multiple, nonlinear, and logistic regression; check conditions; and interpret results
Analyze variance with ANOVA — break down the ANOVA table, one-way and two-way ANOVA, the F-test, and multiple comparisons
Connect with Chi-square tests — examine two-way tables and test categorical data for independence and goodness-of-fit
Leap ahead with nonparametrics — grasp techniques used when you can't assume your data has a normal distribution
Open the book and find:
Up-to-date methods for analyzing data
Full explanations of Statistics II concepts
Clear and concise step-by-step procedures
Dissection of computer output
Lots of tips, strategies, and warnings
Ten common errors in statistical conclusions
Everyday statistics applications
Tables for completing calculations used in the book
Featuring new and updated examples, real-world applications, and test-taking strategies for success, this easy-to-understand guide covers such key topics as sorting and testing models, using regression to make predictions, performing variance analysis (ANOVA), and more.
The ideal supplement and study guide for students preparing for advanced statistics
Packed with fresh and practical examples appropriate for a range of degree-seeking students, Statistics II For Dummies helps any reader succeed in an upper-level statistics course. It picks up with data analysis where Statistics For Dummies left off, featuring new and updated examples, real-world applications, and test-taking strategies for success. This easy-to-understand guide covers such key topics as sorting and testing models, using regression to make predictions, performing variance analysis (ANOVA), drawing test conclusions with chi-squares, and making comparisons with the Rank Sum Test.
About the Author
Deborah Rumsey, PhD, is a Statistics Education Specialist and Auxiliary Faculty Member in the Department of Statistics at Ohio State University. She is also a Fellow of the American Statistical Association and has received the Presidential Teaching Award from Kansas State University. Dr. Rumsey has published numerous papers and given many professional presentations on the subject of statistics education.
Table of Contents
Part I: Tackling Data Analysis and Model-Building Basics.
Chapter 1: Beyond Number Crunching: The Art and Science of Data Analysis.
Chapter 2: Finding the Right Analysis for the Job.
Chapter 3: Reviewing Confi dence Intervals and Hypothesis Tests.
Part II: Using Different Types of Regression to Make Predictions.
Chapter 4: Getting in Line with Simple Linear Regression.
Chapter 5: Multiple Regression with Two X Variables.
Chapter 6: How Can I Miss You If You Won’t Leave? Regression Model Selection.
Chapter 7: Getting Ahead of the Learning Curve with Nonlinear Regressio.
Chapter 8: Yes, No, Maybe So: Making Predictions by Using Logistic Regression.
Part III: Analyzing Variance with ANOVA.
Chapter 9: Testing Lots of Means? Come On Over to ANOVA!
Chapter 10: Sorting Out the Means with Multiple Comparisons.
Chapter 11: Finding Your Way through Two-Way ANOVA.
Chapter 12: Regression and ANOVA: Surprise Relatives!
Part IV: Building Strong Connections with Chi-Square Tests.
Chapter 13: Forming Associations with Two-Way Tables.
Chapter 14: Being Independent Enough for the Chi-Square Test.
Chapter 15: Using Chi-Square Tests for Goodness-of-Fit (Your Data, Not Your Jeans).
Part V: Nonparametric Statistics: Rebels without a Distribution.
Chapter 16: Going Nonparametric.
Chapter 17: All Signs Point to the Sign Test and Signed Rank Test.
Chapter 18: Pulling Rank with the Rank Sum Test.
Chapter 19: Do the Kruskal-Wallis and Rank the Sums with the Wilcoxon.
Chapter 20: Pointing Out Correlations with Spearman's Rank.
Part VI: The Part of Tens.
Chapter 21: Ten Common Errors in Statistical Conclusions.
Chapter 22: Ten Ways to Get Ahead by Knowing Statistics.
Chapter 23: Ten Cool Jobs That Use Statistics.
Appendix: Reference Tables.