Applied linear statistical models : regression, analysis of variance, and experimental designs

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.

1 Linear Regression with One Independent Variable

2 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

22 Two Factor StudiesßUnequal Sample Sizes and Unequal Treatment Importance

23 Multi-Factor Studies

24 Random and Mixed-Effect Models

25 Analysis of Covariance

26 Design of Experiments, Randomization, and Sample Size Planning

27 Randomized Block Designs

28 Nested Designs, Subsampling, and Partially Nested Designs

29 Repeated Measure Designs

30 Latin Square and Related Designs

31 Explanatory Experiments--Two-level Factorial and Fractional Factorial Designs

32 Response Surface Methodology

Appendixes

Available:*

On Order

Summary

Summary

Table of Contents