Designed for a two-semester, introductory course for graduate students in the social sciences, this text illustrates how to use R to apply both standard and modern methods to correct known problems with classic techniques. Numerous illustrations provide a conceptual basis for understanding why practical problems with classic methods were missed for so many years, and why modern techniques have practical value. The second edition will add coverage of advances in regression, such as new insights regarding smoothers. New results on comparing medians would be included and quite a few improvements…mehr
Designed for a two-semester, introductory course for graduate students in the social sciences, this text illustrates how to use R to apply both standard and modern methods to correct known problems with classic techniques. Numerous illustrations provide a conceptual basis for understanding why practical problems with classic methods were missed for so many years, and why modern techniques have practical value. The second edition will add coverage of advances in regression, such as new insights regarding smoothers. New results on comparing medians would be included and quite a few improvements relevant to ANCOVA will be added. In addition, new R functions will be described.
Rand Wilcox has been a Professor of Psychology at the University of Southern California since 1987. He received his Ph.D. from the University of California, Santa Barbara in 1976. His research interests are statistical methods, particularly robust methods for comparing groups and studying associations. He also collaborates with researchers in occupational therapy, gerontology, biology and psychology. He is the author of four books.
Inhaltsangabe
Introduction. Numerical and Graphical Summaries of Data. Probability and Related Concepts. Sampling Distributions and Confidence Intervals. Hypothesis Testing. Regression and Correlation. Bootstrap Methods. Comparing Two Independent Groups. Comparing Two Dependent Groups. One-Way ANOVA. Two-Way and Three-Way Designs. Comparing More than Two Dependent Groups. Multiple Comparisons. Some Multivariate Methods. Robust Regression and Measures of Association. Basic Methods for Analyzing Categorical Data. Answers to Selected Exercises. Tables. Basic Matrix Algebra. References. Index.
Introduction. Numerical and Graphical Summaries of Data. Probability and Related Concepts. Sampling Distributions and Confidence Intervals. Hypothesis Testing. Regression and Correlation. Bootstrap Methods. Comparing Two Independent Groups. Comparing Two Dependent Groups. One-Way ANOVA. Two-Way and Three-Way Designs. Comparing More than Two Dependent Groups. Multiple Comparisons. Some Multivariate Methods. Robust Regression and Measures of Association. Basic Methods for Analyzing Categorical Data. Answers to Selected Exercises. Tables. Basic Matrix Algebra. References. Index.
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