# Applied Statistics: From Bivariate Through Multivariate Techniques: From Bivariate Through Multivariate Techniques

SAGE, Apr 10, 2012 - Mathematics - 1172 pages
Rebecca M. Warner’s Applied Statistics: From Bivariate Through Multivariate Techniques, Second Edition provides a clear introduction to widely used topics in bivariate and multivariate statistics, including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. The approach is applied and does not require formal mathematics; equations are accompanied by verbal explanations. Students are asked to think about the meaning of equations. Each chapter presents a complete empirical research example to illustrate the application of a specific method. Although SPSS examples are used throughout the book, the conceptual material will be helpful for users of different programs. Each chapter has a glossary and comprehension questions.

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This book is one tough read. Be sure to have plenty of time to go through the verbose speaking nature of the author. There are good explanations and examples. However, the logic of thought teeters on the edge of too much presented.

### Contents

 Preface About the Author 1 Review of Basic Concepts 2 Basic Statistics Sampling Error and Confidence Intervals 3 Statistical Significance Testing 4 Preliminary Data Screening 5 Comparing Group Means Using the Independent Samples t Test 6 OneWay BetweenSubjects Analysis of Variance
 17 Analysis of Covariance 18 Discriminant Analysis 19 Multivariate Analysis of Variance 20 Principal Components and Factor Analysis 21 Reliability Validity and MultipleItem Scales 22 Analysis of Repeated Measures 23 Binary Logistic Regression Proportions of Area Under a Standard Normal Curve

 7 Bivariate Pearson Correlation 8 Alternative Correlation Coefficients 9 Bivariate Regression Preliminary Exploratory Analyses 11 Multiple Regression with Two Predictor Variables 12 Dummy Predictor Variables in Multiple Regression 13 Factorial Analysis of Variance 14 Multiple Regression with More than Two Predictors Tests for Interaction in Multiple Regression 16 Mediation
 Critical Values for t Distribution Critical Values of F Critical Values of ChiSquare Critical Values of the Pearson Correlation Coefficient Critical Values of the Studentized Range Statistic Appendix G Transformation of r Pearson Correlation to Fisher Z Glossary References Index Copyright