
- English
- ePUB (mobile friendly)
- Available on iOS & Android
About this book
Effective Experimentation is a practical book on how to design and analyse experiments. Each of the methods are introduced and illustrated through real world scenario drawn from industry or research. Formulae are kept to a minimum to enable the reader to concentrate on how to apply and understand the different methods presented.
The book has been developed from courses run by Statistics for Industry Limited during which time more than 10, 000 scientists and technologists have gained the knowledge and confidence to plan experiments successfully and to analyse their data. Each chapter starts with an example of a design obtained from the authors' experience. Statistical methods for analysing data are introduced, followed, where appropriate, by a discussion of the assumptions of the method and effectiveness and limitations of the design.
The examples have been chosen from many industries including chemicals, oils, building materials, textiles, food, drink, lighting, water, pharmaceuticals, electronics, paint, toiletries and petfoods.
This book is a valuable resource for researchers and industrial statisticians involved in designing experiments. Postgraduates studying statistics, engineering and mathematics will also find this book of interest.
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Information
Table of contents
- Cover
- Title
- Copyright
- Preface
- 1: Why bother to design an experiment?
- 2: A change for the better – significance testing
- 3: Improving effectiveness using a paired design
- 4: A simple but effective design for two variables
- 5: Investigating 3 and 4 variables in an experiment
- 6: More for even less: using a fraction of a full design
- 7: Saturated designs
- 8: Regression analysis
- 9: Multiple regression: the first essentials
- 10: Designs to generate response surfaces
- 11: Outliers and influential observations
- 12: Central composite designs
- 13: Designs for mixtures
- 14: Computer-aided experimental design (CAED)
- 15: Optimization designs
- 16: Improving a bad experiment
- 17: How to compare several treatments
- 18: Experiments in blocks
- 19: Two-way designs
- 20: Too much at once: incomplete block experiments
- 21: 23 Ways of messing up an experiment
- Solutions to problems
- Statistical tables
- Index