Applied multiple regression/correlation analysis for the behavioral sciences
著者
書誌事項
Applied multiple regression/correlation analysis for the behavioral sciences
L. Erlbaum Associates, 1983
2nd ed
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Multiple regression/correlation analysis for the behavioral sciences
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注記
Bibliography: p. 531-535
Includes indexes
内容説明・目次
内容説明
This classic text on multiple regression is noted for its nonmathematical, applied, and data-analytic approach. Readers profit from its verbal-conceptual exposition and frequent use of examples. The applied emphasis provides clear illustrations of the principles and provides worked examples of the types of applications that are possible. Researchers learn how to specify regression models that directly address their research questions. An overview of the fundamental ideas of multiple regression and a review of bivariate correlation and regression and other elementary statistical concepts provide a strong foundation for understanding the rest of the text. The third edition features an increased emphasis on graphics and the use of confidence intervals and effect size measures, and an accompanying CD with data for most of the numerical examples along with the computer code for SPSS, SAS, and SYSTAT. Applied Multiple Regression serves as both a textbook for graduate students and as a reference tool for researchers in psychology, education, health sciences, communications, business, sociology, political science, anthropology, and economics. An introductory knowledge of statistics is required. Self-standing chapters minimize the need for researchers to refer to previous chapters.
目次
Contents: Preface to Second Edition. Preface to First Edition. Part I: Basics. Introduction. Bivariate Correlation and Regression. Multiple Regression/Correlation: Two or More Independent Variables. Sets of Independent Variables. Part II: The Representation of Information in Independent Variables. Nominal or Qualitative Scales. Quantitative Scales. Missing Data. Interactions. Part III: Applications. Causal Models. The Analysis of Covariance and Its Multiple Regression/Correlation Generalization. Repeated Measurement and Matched Subjects Designs. Multiple Regression/Correlation and Multivariate Methods. Appendices: The Mathematical Basis for Multiple Regression/Correlation and Identification of the Inverse Matrix Elements. Desk Calculator Solution of the Multiple Regression/Correlation Problem: Determination of the Inverse Matrix and Applications Thereof. Computer Analysis of Multiple Regression/Correlation. Set Correlation as a General Multivariate Data-Analytic Method. Appendix Tables.
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