Applied regression analysis. /
Norman R. Draper
- 3rd ed.
- New York: John Wiley and sons INC, 1998.
- xvii,706p.: ill.; 24cm.
Includes index and bibliography.
Contents: About the software -- Basic prerequisite knowledge -- Fitting a straight line by least squares -- Checking the straight line fit -- Fitting straight lines: Special topics -- Regression in matrix terms: Straight line case -- The general regression situation -- Extra sums of squares and tests for several parameters being zero -- Serial correlation in the residuals and the durbin-watson test -- More on checking fitted models -- Multiple regression: Special topics -- Bias in regression estimates, and expected values of mean squares and sums of squares -- On worthwhile regressions, big F's and R2 -- Models containing functions of the predictors including polynomial models -- Transformation of the response variable -- Dummy variables -- Selecting the best regression equation -- III-Conditioning in regression data -- Ridge regression -- Generalized linear models (GLIM) -- Mixture ingredients as predator variables -- The geometry of least sqaures -- More geometry of least squares -- Orthogonal polynomials and summary data -- Mutiple regression applied to analysis of variance problems -- An introduction to nonlinear estimation -- Robust regression -- Resampling procedures (bootstrapping).