Renowned statistician R. G. Miller set the pace for statistics students with Beyond ANOVA: Basics of Applied Statistics. Designed to show students how to work with a set of "real world data," Miller's text goes beyond any specific discipline, and considers a whole variety of techniques from ANOVA to empirical Bayes methods; the jackknife, bootstrap methods; and the James-Stein estimator.
This reissue of Miller's classic book has been revised by professors at Stanford University, California. As before, one of the main strengths of Beyond ANOVA is its promotion of the use of the most straightforward data analysis methods-giving students a viable option, instead of resorting to complicated and unnecessary tests.
Assuming a basic background in statistics, Beyond ANOVA is written for undergraduates and graduate statistics students. Its approach will also be valued by biologists, social scientists, engineers, and anyone who may wish to handle their own data analysis.
Inhaltsverzeichnis
One Sample Normal Theory Nonnormality Effect Dependence Exercises Two Samples Normal Theory Nonnormality Unequal Variances Dependence Exercises One-Way Classification Fixed Effects Normal Theory Nonnormality Unequal Variances Dependence Random Effects Normal Theory Nonnormality Unequal Variances Dependence Exercises Two-Way Classification Fixed Effects Normal Theory Nonnormality Unequal Variances Dependence Mixed Effects Normal Theory Departures from assumptions Random Effects Normal Theory Departures from Assumptions Exercises Regression Regression Model Normal Linear Model Nonlinearity Nonnormality Unequal Variances Dependence Errors-in-Variables Model Normal Theory Departures from Assumptions Exercises Ratios Normal Theory Departures from Assumptions Exercises Variances Normal Theory Nonnormality Dependence Exercises
