Applied Linear Statistical Models / Edition 4by John Neter, Michael H. Kutner, Christopher J. Nachtsheim
Pub. Date: 08/28/1996
Publisher: McGraw-Hill Companies, The
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.
- McGraw-Hill Companies, The
- Publication date:
- Edition description:
- Student Solutions Manual
- Product dimensions:
- 8.40(w) x 10.68(h) x 0.28(d)
Table of Contents1 Linear Regression with One Independent Variable2 Inferences in Regression Analysis 3 Diagnostic and Remedial Measures 4 Simultaneous Inferences and Other Topics in Regression Analysis 5 Matrix Approach to Simple Linear Regression Analysis 6 Multiple Regression I 7 Multiple Regression II 8 Building the Regression Model I: Selection of Predictor Variables 9 Building the Regression Model II: Diagnostics 10 Building the Regression Model III: Remedial Measures and Validation 11 Qualitative Predictor Variables 12 Autocorrelation in Time Series Data 13 Introduction to Nonlinear Regression 14 Logistic Regression, Poisson Regression, and Generalized Linear Models 15 Normal Correlation Models 16 Analysis of Variance 17 Analysis of Factor-Level Effects 18 ANOVA Diagnostics and Remedial Measures 19 Two-Factor Analysis of VarianceßEqual Sample Sizes 20 Analysis of Factor Effects in Two-Factor StudiesßEqual Sample Sizes 21 Two-Factor StudiesßOne Case per Treatment (and more...)
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This is by far the best empirical modeling book I have come across. I pick this book up and reference it at least every other day. The descriptions don't assume you have a PhD in mathematics like some other modeling books I have read. They even cover introductory linear algebra for those who have not had it. If you need some place to start for figuring out how to model data, start here.