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My Applied Linear Statistical Models book has a 3.5' floppy disk with data on it. I needed the data the other day, so I scrounged for a USB floppy drive, copied the files, and imaged the disk. In case anyone else needs them, here are the data sets. Book info: Title: Applied Linear Statistical Models; Authors: John Neter, Michael H Kutner.
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Applied Linear Statistical Models (3rd Edition)
J. Opt Res. Soc.B o o k SelectionEdited by RICHARD EGLESE 0MIKE PIDD 0W.D. RAY 00 1. NETER, W. WASSERMAN and M. H. KUTNER: Applied Linear Statistical Models (3rd Edition) OWEN P . HALL JR: Computer Models for Operations Management THOMAS H. CORMEN, CHARLES E. LEISERSON and RONALD L. RIVEST: Introduction to Algorithms INGOLF STAHL: Introduction to Simulation with GPSS on the PC, Macintosh and VAX PETER CHECKLAND and JIM SCHOLES: Soft Systems Methodology in ActionApplied Linear Statistical Models (3rd Edition) 1. NETER, W. WASSERMAN and M.H. KUTNER Irwin, Boston, Mass., 1990. 1181 + xvi pp. ISBN 0 256 08338 X Computer Models for Operations Management OWEN P. HALL JR Addison-Wesley, Reading, Mass. 1989. 195 + v pp. +disk. £24.25 ISBN 0 201 170501 7 This package actually consists of a 195-page text togetherwith a 5.25-inch disk suitable for an IBM PC or close compatible. According to the back cover of the text, the whole offers a collection of production and operations management analytical software models for decision support.-815815This is a vast but beautifully produced book. It is now in its third edition, the first having beenin 1974, the second in 1980. The three main themes are regression, analysis of variance, andexperimental designs, the approach being very much of an applied nature. It is not a theoretical treatise,indeed that is its strength; it could be a useful asset to the working statistician when he wishes toconsult some aspect of one or other of the above three topics in more detail.It seems superfluous to itemize the contents; suffice it to say that regression gets a thoroughairing, in fact this reviewer cannot recall so extensive a coverage in works of this kind, taking asit does almost half the book. However, it is well done, and several interesting research results arereferenced, such as the outlier, autocorrelated error and multicollinearity problems.The analysis of variance is again comprehensively dealt with, although somewhat repetitively inthat simple models give way to slightly more difficult ones and so on without much in the way ofnew principles being involved. Some digestion of the material here would not have impaired thepresentation, though to be fair if someone wished to consult it for a particular analysis, the chancesare they would find it here. The approach is standard with fixed, random, and mixed effects modelsfor multi-factor situations being entertained. Covariance is dealt with briefly, although there is littleor nothing on factorial analyses.The final theme of about a hundred pages is a brief excursion into the statistical design ofexperiments, introducing randomized blocks, nested designs, repeated measures designs, and latinsquares.In summary then, this is a very nice manual for the practising statistician, being particularly goodon regression. Finally, if a statistical computer package coughs out something strange, one coulddo worse than consult this book for its meaning.