Linear Regression Analysis ─ Theory and Computing

Yan (U. of Missouri-Kansas City) and Su (U. of Central Florida) present this work as a one-semester textbook for a course in regression analysis for graduate students majoring in statistics and biostatistics in particular. Their treatment is contained in nine chapters that introduce the basic concept of regression and define the linear regression model and then discuss simple linear regression and multiple linear regression (focusing on the concept of vector space, linear projection, and linear hypothesis test); the topics of outlier detection, influential observations identification, collinearity, confounding, regression on dummy variables, checking for equal variance assumption, graphical display of residual diagnosis, and variable transformation technique in linear regression analysis; the generalizations of the ordinary least squares estimation in linear regression; the Poisson Regression for count data and the logistic regression for binary data; and Bayesian linear regression and the Bayes averaging method. Annotation c2009 Book News, Inc., Portland, OR (booknews.com)