Applied Linear Statistical Models / Edition 3

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Overview

There are two approaches to undergraduate and graduate courses in linear statistical models and experimental design in applied statistics. One is a two-term sequence focusing on regression followed by ANOVA/Experimental design. Applied Linear Statistical Models serves that market. It is offered in business, economics, statistics, industrial engineering, public health, medicine, and psychology departments in four-year colleges and universities, and graduate schools. Applied Linear Statistical Models is the leading text in the market. It is noted for its quality and clarity, and its authorship is first-rate. The approach used in the text is an applied one, with an emphasis on understanding of concepts and exposition by means of examples. Sufficient theoretical foundations are provided so that applications of regression analysis can be carried out comfortably. The fourth edition has been updated to keep it current with important new developments in regression analysis.
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Editorial Reviews

Booknews
A textbook for use in undergraduate or graduate courses in linear statistical models, and in second courses in applied statistics. Blends theory and applications, avoiding the extremes of presenting theory in isolation and of giving applications without theoretical foundation. Assumes an introductory course in statistical inference. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

  • ISBN-13: 9780256083385
  • Publisher: McGraw-Hill Higher Education
  • Publication date: 6/28/1990
  • Edition description: Older Edition
  • Edition number: 3
  • Pages: 1184

Table of Contents

1 Linear Regression with One Independent Variable 3
2 Inferences in Regression Analysis 44
3 Diagnostics and Remedial Measures 95
4 Simultaneous Inferences and Other Topics in Regression Analysis 152
5 Matrix Approach to Simple Linear Regression Analysis 176
6 Multiple Regression - I 217
7 Multiple Regression - II 260
8 Building the Regression Model I: Selection of Predictor Variables 327
9 Building the Regression Model II: Diagnostics 361
10 Building the Regression Model III: Remedial Measures and Validation 400
11 Qualitative Predictor Variables 455
12 Autocorrelation in Time Series Data 497
13 Introduction to Nonlinear Regression 531
14 Logistic Regression, Poisson Regression, and Generalized Linear Models 567
15 Normal Correlation Models 631
Appendix A Some Basic Results in Probability and Statistics 663
Appendix B Tables 685
Appendix C Data Sets 702
Appendix D Selected Bibliography 708
Index 715
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