
Basic Experimental Strategies and Data Analysis for Science and Engineering
- 434 pages
- English
- PDF
- Available on iOS & Android
Basic Experimental Strategies and Data Analysis for Science and Engineering
About this book
Every technical investigation involving trial-and-error experimentation embodies a strategy for deciding what experiments to perform, when to quit, and how to interpret the data. This handbook presents several statistically derived strategies which are more efficient than any intuitive approach and will get the investigator to their goal with the fewest experiments, give the greatest degree of reliability to their conclusions, and keep the risk of overlooking something of practical importance to a minimum.
Features:
- Provides a comprehensive desk reference on experimental design that will be useful to practitioners without extensive statistical knowledge
- Features a review of the necessary statistical prerequisites
- Presents a set of tables that allow readers to quickly access various experimental designs
- Includes a roadmap for where and when to use various experimental design strategies
- Shows compelling examples of each method discussed
- Illustrates how to reproduce results using several popular software packages on a companion web site
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Following the outlines and examples in this book should quickly allow a working professional or student to select the appropriate experimental design for a research problem at hand, follow the design to conduct the experiments, and analyze and interpret the resulting data.
John Lawson and John Erjavec have a combined 25 years of industrial experience and over 40 years of academic experience. They have taught this material to numerous practicing engineers and scientists as well as undergraduate and graduate students.
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Information
Table of contents
- Cover
- Half Title
- Title Page
- Copyright Page
- Dedication
- Table of Contents
- List of Figures
- List of Tables
- Preface
- Author Bios
- 1: Strategies for Experimentation with Multiple Factors
- 2: Statistics and Probability
- 3: Basic Two-Level Factorial Experiments
- 4: Additional Tools for Two-Level Factorials
- 5: General Factorial Experiments and ANOVA
- 6: Variance Component Studies
- 7: Screening Designs
- 8: Regression Analysis
- 9: Response Surface Designs
- 10: Response Surface Model Fitting
- 11: Sequential Experimentation
- 12: Mixture Experiments
- 13: Practical Suggestions for Successful Experimentation
- Appendix A
- Appendix B
- Check Figures for Selected Exercises
- Bibliography
- Index