Applied Linear Statistical Models 5th Edition
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After the introductory chapter, the authors gave just the right amount of theory to explain the topic at hand and give extensive footnotes for further information. Lots of graphs and example software output are included, all very helpful. I found the text to be well-organized, with coverage given to explanation and examples of each topic.
My one complaint with the book is that it included no instruction on how to work with software programs to get the desired results, so if you are entirely new to the area and do not know how to use Statistix (which has a thorough and self-explanatory help system), R, Minitab, and SAS (which do not), going will be rough. One of the other reviewers mentioned a SAS guide. You may need it if your professor does not demonstrate software use in class.
This book is not the be all and end all of statistics books, but it gives a basic overview of many topics. It is easy for someone with a background knowledge in basic statistics to read. If you want to know more about a topic, there is a bibliography. I like to read first from this book, to get a general idea about the concepts and then go to a more difficult text for all the details.
My book came with a CD, which I have never used because all the data and the student solution guide (odd answers) are online through mcgraw-hill.
The only pitfall is it uses MINITAB. I wish it used R instead.
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Table of Contents
Part 1 Simple Linear Regression
1 Linear Regression with One Predictor Variable
2 Inferences in Regression and Correlation Analysis
3 Diagnostic and Remedial Measures
4 Simultaneous Inferences and Other Topics in Regression Analysis
5 Matrix Approach to Simple Linear Regression Analysis
Part 2 Multiple Linear Regression
6 Multiple Regression I
7 Multiple Regression II
8 Regression Models for Quantitative and Qualitative Predictors
9 Building the Regression Model I: Model Selection and Validation
10 Building the Regression Model II: Diagnostics
11 Building the Regression Model III: Remedial Measures
12 Autocorrelation in Time Series Data
Part 3 Nonlinear Regression
13 Introduction to Nonlinear Regression and Neural Networks
14 Logistic Regression, Poisson Regression, and Generalized Linear Models
Part 4 Design and Analysis of Single-Factor Studies
15 Introduction to the Design of Experimental and Observational Studies
16 Single Factor Studies
17 Analysis of Factor-Level Means
18 ANOVA Diagnostics and Remedial Measures
Part 5 Multi-Factor Studies
19 Two Factor Studies with Equal Sample Sizes
20 Two Factor Studies-One Case per Treatment
21 Randomized Complete Block Designs
22 Analysis of Covariance
23 Two Factor Studies with Unequal Sample Sizes
24 MultiFactor Studies
25 Random and Mixed Effects Models
Part 6 Specialized Study Designs
26 Nested Designs, Subsampling, and Partially Nested Designs
27 Repeated Measures and Related Designs
28 Balanced Incomplete Block, Latin Square, and Related Designs
29 Exploratory Experiments: Two-Level Factorial and Fractional Factorial Designs
30 Response Surface Methodology
Appendix A: Some Basic Results in Probability and Statistics
Appendix B: Tables
Appendix C: Data Sets
Appendix D: Rules for Develping ANOVA Models and Tables for Balanced Designs
Appendix E: Selected Bibliography