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Statistical Methods for Psychology, International Edition
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Table of Contents

1. Basic Concepts. 2. Describing and Exploring Data. 3. The Normal Distribution. 4. Sampling Distributions and Hypothesis Testing. 5. Basic Concepts of Probability. 6. Categorical Data and Chi-Square. 7. Hypothesis Tests Applied to Means. 8. Power. 9. Correlation and Regression. 10. Alternative Correlational Techniques. 11. Simple Analysis of Variance. 12. Multiple Comparisons Among Treatment Means. 13. Factorial Analysis of Variance. 14. Repeated-Measures Designs. 15. Multiple Regression. 16. Analyses Of Variance and Covariance as General Linear Models. 17. Meta-Analysis and Single-Case Designs. 18. Resampling and Nonparametric Approaches to Data.

About the Author

David C. Howell is a professor emeritus and former chair of the psychology department at the University of Vermont. Professor Howell's primary area of research is in statistics and experimental methods. He is also the author of STATISTICAL METHODS FOR PSYCHOLOGY, currently in an Eighth Edition (Wadsworth Cengage Learning, 2013), and the ENCYCLOPEDIA OF STATISTICS IN BEHAVIOR SCIENCE (2005) with Brian Everitt. Before retiring, he frequently served as consultant with other faculty, both in the psychology department and in departments as disparate as Geology and Animal Sciences, and brings those experiences to this endeavor. Professor Howell's other interests include computing and the World Wide Web, and how technology affects communication, teaching, and research.

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1. Basic Concepts. 2. Describing and Exploring Data. 3. The Normal Distribution. 4. Sampling Distributions and Hypothesis Testing. 5. Basic Concepts of Probability. 6. Categorical Data and Chi-Square. 7. Hypothesis Tests Applied to Means. 8. Power. 9. Correlation and Regression. 10. Alternative Correlational Techniques. 11. Simple Analysis of Variance. 12. Multiple Comparisons Among Treatment Means. 13. Factorial Analysis of Variance. 14. Repeated-Measures Designs. 15. Multiple Regression. 16. Analyses Of Variance and Covariance as General Linear Models. 17. Meta-Analysis and Single-Case Designs. 18. Resampling and Nonparametric Approaches to Data.

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