Statistics II for Dummies
Published on 1. September 2009
Book
Paperback/Softback
408 pages
978-0-470-46646-9 (ISBN)
Description
Statistics II for Dummies
Learn to:
* Increase your skills in data analysis
* Sort through and test models
* Make predictions
* Apply statistics to real-world situations
Deborah Rumsey, PhD
Author of Statistics For Dummies and Statistics Workbook For Dummies
The fun and easy way(r) 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
More details
Other editions
New editions
Additional editions
Previous edition
Deborah Rumsey
Intermediate Statistics For Dummies
Book
03/2007
1st Edition
Wiley
€17.90
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Person
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.
Content
Introduction. 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.
Index.
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.
Index.