Now! Reprinted in convenient softcover format and reduced in price!
Developed over years of classroom use, this text can be used as an introduction to statistics emphasizing experimental
design or as an elementary graduate survey course. This text explains how to choose sound and suitable design structures
and to engage students in understanding the interpretive and constructive natures of data analysis and experimental
design.
Students will build a deep understanding of statistical concepts over time as they analyze and design experiments.
The field of statistics is presented as a matrix, rather than a hierarchy, of related concepts. The text has been
widely praised for its exceptional range of intelligent and creative exercises, and for its large number of examples
and data sets.
The new printing, with minor errata corrections, creates a more convenient and portable format for your students,
at about 60% the cost of most competitive titles. Your students will love George Cobb's thorough, careful introduction
to a topic often shrouded in complexity. You will appreciate his integrative approach, which weaves introductory
material with experimental design considerations.
This new printing replaces the previous Springer-Verlag hardcover version: ISBN 0-387-94607-1.
Table of Contents
1. Introduction to Experimental Design
2. Informal Analysis and Checking Assumptions
3. Formal ANOVA: Decomposing the Data and Measuring Variability, Testing Hypotheses and Estimating True Differences
4. Decisions About the Content of an Experiment
5. Randomization and the Basic Factorial Design
6. Interaction and the Principle of Factorial Crossing
7. The Principle of Blocking
8. Working with the Four Basic Designs
9. Extending the Basic Designs by Factorial Crossing
10. Decomposing a Data Set
11. Comparisons, Contrasts, and Confidence Intervals
12. The Fisher Assumptions and How to Check Them
13. Other Experimental Designs and Models
14. Continuous Carriers: A Visual Approach to Regression, Correlation and Analysis of Covariance
15. Sampling Distributions and the Role of the Assumptions
