Introduces the philosophy of experimentation and the part that statistics play in experimentation. Emphasizes the need to develop a capability for ``statistical thinking by using examples drawn from actual case studies.
Applied Regression Analysis Second Edition Norman Draper and Harry Smith, Jr. The revised, expanded Second Edition of this popular book by two authorities with more than 50 years of combined experience in the field. Draper and Smith offer a comprehensive introduction to fundamentals, concepts and applications. This Second Edition treats basic multiple linear regression, the examination of residuals, the planning of large regression studies, the theory of nonlinear estimation, the "practical" significance of regression, and much more. With many new exercises, examples, and references, this book is unusually versatile-a useful reference for professionals in many fields. 1981 (0 471-02995-5) 709 pp. Nonlinear Regression G. A. F. Seber and C. J. Wild Nonlinear Regression carefully blends the theory and practice of nonlinear regression and includes numerous computational examples and graphs throughout. Treats the broad aspects of model building and statistical inference, and serves as a thorough introduction to the topic for graduate students and as an accessible reference source for professionals. Most chapters are self-contained, and new and important material relating to the concept of curvature and its growing role in statistical inference is included. Ideal for those training to be statisticians, biometricians, and econometricians, as well as providing research scientists with up-to-date, readable access to their fields. 1989 (0 471-61760-1) 768 pp. Statistical Design for Research Leslie Kish The issues of data collection and selection, vital to statistics, yet often neglected, are described in-depth in Statistical Design For Research, as well as the ramifying effects of statistical design in the social and health sciences, education, and market research. Emphasizing research design development, this important work includes a discussion of key methods-experimental designs, survey sampling, and controlled investigations-plus ample tables, figures and problem sets. 1987 (0 471-08359-3) 296 pp.
The book integrates the lives of those who graduated from the school and what happened to them after leaving Chestnut and through the years. This is done through stories in the former students own words accompanied by photos on then, now, and their families. It lists all the students who graduated from Chestnut and the year of graduation.
Preface to the Second Edition.
Chapter 1. Catalizing the Generation of Knowledge.
1.1. The Learning Process.
1.2. Important Considerations.
1.3. The Experimenter’s Problem and Statistical Methods.
1.4. A Typical Investigation.
1.5. How to Use Statistical Techniques.
References and Further Reading.
Chapter 2. Basics: Probability, Parameters and Statistics.
2.1. Experimental Error.
2.2. Distributions.
2.3. Statistics and Parameters.
2.4. Measures of Location and Spread.
2.5. The Normal Distribution.
2.6. Normal Probability Plots.
2.7. Randomness and Random Variables.
2.8. Covariance and Correlation as Measures of Linear Dependence.
2.9. Student’s tDistribution.
2.10. Estimates of Parameters.
2.11. Random Sampling from a Normal Population.
2.12. The Chi-Square and FDistributions.
2.13. The Binomial Distribution.
2.14. The Poisson Distribution.
Appendix 2A. Mean and Variance of Linear Combinations of Observations.
References and Further Reading.
Chapter 3. Comparing Two Entities: Relevant Reference Distributions, Tests and Confidence Intervals.
3.1. Relevant Reference Sets and Distributions.
3.2. Randomized Paired Comparison Design: Boys’ Shoes Example.
3.3. Blocking and Randomization.
3.4. Reprise: Comparison, Replication, Randomization, and Blocking in Simple Experiments.
3.5. More on Significance Tests.
3.6. Inferences About Data that are Discrete: Binomial Distribution.
3.7. Inferences about Frequencies (Counts Per Unit): The Poisson Distribution.
3.8. Contingency Tables and Tests of Association.
Appendix 3A. Comparison of the Robustness of Tests to Compare Two Entities.
Appendix 3B. Calculation of reference distribution from past data.
References and Further Reading.
Chapter 4. Comparing a Number of Entities: Randomized Blocks and Latin Squares.
4.1. Comparing kTreatments in a Fully Randomized Design.
4.2. Randomized Block Designs.
4.3. A Preliminary Note on Split-Plot Experiments and their Relationship to Randomized Blocks.
4.4. More than one blocking component: Latin Squares.
4.5. Balanced Incomplete Block Designs.
Appendix 4A. The Rationale for the Graphical ANOVA.
Appendix 4B. Some Useful Latin Square, Graeco–Latin Square, and Hyper-Graeco–Latin Square Designs.
References and Further Reading.
Chapter 5. Factorial Designs at Two Levels: Advantages of Experimental Design.
5.1. Introduction.
5.2. Example 1: The Effects of Three Factors (Variables) on Clarity of Film.
5.3. Example 2: The Effects of Three Factors on Three Physical Properties of a Polymer Solution.
5.4. A 23 Factorial Design: Pilot Plant Investigation.
5.5. Calculation of Main Effects.
5.6. Interaction Effects.
5.7. Genuine Replicate Runs.
5.8. Interpretation of Results.
5.9. The Table of Contrasts.
5.10. Misuse of the ANOVA for 2kFactorial Experiments.
5.11. Eyeing the Data.
5.12. Dealing with More Than One Response: A Pet Food Experiment.
5.13. A 24 Factorial Design: Process Development Study.
5.14. Analysis Using Normal and Lenth Plots.
5.15. Other Models for Factorial Data.
5.16. Blocking the 2kFactorial Designs.
5.17. Learning by Doing.
5.18. Summary.
Appendix 5A. Blocking Larger Factorial Designs.
Appendix 5B. Partial Confounding.
References and Further Reading.
Chapter 6. Fraction Factorial Designs: Economy in Experimentation.
6.1. Effects of Five Factors on Six Properties of Films in Eight Runs.
6.2. Stability of New Product, Four Factors in Eight Runs, a 24−1 Design.
6.3. A Half-Fraction Example: The Modification of a Bearing.
6.4. The Anatomy of the Half Fraction.
6.5. The 27−4III Design: A Bicycle Example.
6.6. Eight-Run Designs.
6.7. Using Table 6.6: An Illustration.
6.8. Sign Switching, Foldover, and Sequential Assembly.
6.9. An Investigation Using Multiple-Column Foldover.
6.10. Increasing Design Resolution from III to IV by Foldover.
6.11. Sixteen-Run Designs.
6.12. The 25−1 Nodal Half Replicate of the 25 Factorial: Reactor Example.
6.13. The 28−4 IV Nodal Sixteenth Fraction of a 28 Factorial.
6.14. The 215−11 III Nodal Design: The Sixty-Fourth Fraction of the 215 Factorial.
6.15. Constructing Other Two-Level Fractions.
6.16. Elimination of Block Effects.
References and Further Reading.
Chapter 7. Other Fractionals, Analysis and Choosing Follow-up Runs.
7.1. Plackett and Burman Designs.
7.2. Choosing Follow-Up Runs.
7.3. Justifications for the Use of Fractionals.
Appendix 7A. Technical Details.
Appendix 7B. An Approximate Partial Analysis for PB Designs.
Appendix 7C. Hall’s Orthogonal Designs.
References and Further Reading.
Chapter 8. Factorial Designs and Data Transformation.
8.1. A Two-Way (Factorial) Design.
8.2. Simplification and Increased Sensitivity from Transformation.
Appendix 8A. Rationale for Data Transformation.
Appendix 8B. Bartlett’s χ2νfor Testing Inhomogeneity of Variance.
References and Further Reading.
Chapter 9. Multiple Sources of Variation: Split Plot Designs, Variance Components and Error Transmission.
9.1. Split-Plot Designs, Variance Components, and Error Transmission.
9.2. Split-Plot Designs.
9.3. EstimatingVariance Components.
9.4. Transmission of Error.
References and Further Reading.
Chapter 10. Least Squares and Why You Need to Design Experiments.
10.1. Estimation With Least Squares.
10.2. The Versatility of Least Squares.
10.3. The Origins of Experimental Design.
10.4. Nonlinear Models.
Appendix 10A. Vector Representation of Statistical Concepts.
Appendix 10B. Matrix Version of Least Squares.
Appendix 10C. Analysis of Factorials, Botched and Otherwise.
Appendix 10D. Unweighted and Weighted Least Squares.
References and Further Reading.
Chapter 11. Modelling Relationships, Sequential Assembly: Basics for Response Surface Methods.
11.1. Some Empirical Models.
11.2. Some Experimental Designs and the Design Information Function.
11.3. Is the Surface Sufficiently Well Estimated?
11.4. Sequential Design Strategy.
11.5. Canonical Analysis.
11.6. Box–Behnken Designs.
References and Further Reading.
Chapter 12. Some Applications of Response Surface Methods.
12.1. Iterative Experimentation To Improve a Product Design.
12.2. Simplification of a Response Function by Data Transformation.
12.3. Detecting and Exploiting Active and Inactive Factor Spaces for Multiple-Response Data.
12.4. Exploring Canonical Factor Spaces.
12.5. From Empiricism to Mechanism.
12.6. Uses of RSM.
Appendix 12A. Average Variance of ˆy.
Appendix 12B.
References and Further Reading.
Chapter 13. Designing Robust Products: An Introduction.
13.1. Environmental Robustness.
13.2. Robustness To Component Variation.
Appendix 13A. A Mathematical Formulation for Environmental Robustness.
Appendix 13B. Choice of Criteria.
References and Further Reading.
Chapter 14. Process Control, Forecasting and Times Series: An Introduction.
14.1. Process Monitoring.
14.2. The Exponentially Weighted Moving Average.
14.3. The CuSum Chart.
14.4. Process Adjustment.
14.5. A Brief Look At Some Time Series Models and Applications.
14.6. Using a Model to Make a Forecast.
14.7. Intervention Analysis: A Los Angeles Air Pollution Example.
References and Further Reading.
Chapter 15. Evolutionary Process Operation.
15.1. More than One Factor.
15.2. Multiple Responses.
15.3. The Evolutionary Process Operation Committee.
References and Further Reading.
Appendix Tables.
Author Index.
Subject Index.