Description - An R and S-Plus Companion to Applied Regression by John Fox
An R and S-Plus Companion to Applied Regression: introduces various facilities of S as they are needed in the context of detailed, worked through examples; marks more demanding material (for example, requiring a knowledge of matrix algebra) with an asterisk so that it may be skipped without loss of continuity; uses boxes to explain and highlight the many small differences between S-Plus and R and between S3 and S4 so that the reader can quickly locate the material that is relevant to their version of S or skip over the information posted in the box
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(254mm x 177mm x mm)
SAGE Publications Inc
Publisher: SAGE Publications Inc
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Author Biography - John Fox
John Fox is Professor of Sociology at McMaster University in Hamilton, Ontario, Canada. He was previously Professor of Sociology and of Mathematics and Statistics at York University in Toronto, where he also directed the Statistical Consulting Service at the Institute for Social Research. Professor Fox earned a Ph.D. in Sociology from the University of Michigan in 1972. He has delivered numerous lectures and workshops on statistical topics, at such places as the summer program of the Inter-University Consortium for Political and Social Research and the annual meetings of the American Sociological Association. His recent and current work includes research on statistical methods (for example, work on three-dimensional statistical graphs) and on Canadian society (for example, a study of political polls in the 1995 Quebec sovereignty referendum). He is author of many articles, in such journals as Sociological Methodology, The Journal of Computational and Graphical Statistics, The Journal of the American Statistical Association, The Canadian Review of Sociology and Anthropology, and The Canadian Journal of Sociology. He has written several other books, including Applied Regression Analysis, Linear Models, and Related Methods (Sage, 1997), Nonparametric Simple Regression (Sage, 2000), and Multiple and Generalized Nonparametric Regression (Sage, 2000).