Richard Boddy, Gordon Smith

Effective Experimentation

For Scientists and Technologists

• Gebundenes Buch

Produktbeschreibung

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.

Produktdetails

• Verlag: John Wiley & Sons / Wiley
• Artikelnr. des Verlages: 14568460000
• Seitenzahl: 270
• Erscheinungstermin: 9. August 2010
• Englisch
• Abmessung: 250mm x 176mm x 23mm
• Gewicht: 612g
• ISBN-13: 9780470684603
• ISBN-10: 0470684607
• Artikelnr.: 29931935

Autorenporträt

Richard Boddy, Director of S4i Limited, Co-founder of Statistics for Industry and Director for 30 years and has lectured on more than 300 courses to scientists and technologists from industry. Gordon Laird Smith, Consultant Statistician, Director of Statistics for Industry, developing and presenting training courses to scientists and technologists of all disciplines at all levels.

Inhaltsangabe

Preface. 1 Why Bother to Design an Experiment? 1.1 Indroduction. 1.2
Examples and Benefits. 1.2.1 Develop a Better Product. 1.2.2 Which
Antiperspirant is Best? 1.2.3 A Complex Project. 1.3 Good Design and Good
Analysis. 2 A Change for the Better - Significance Testing. 2.1
Introduction. 2.2 Towards a Darker Stout. 2.3 Summary Statistics. 2.4 The
Normal Distribution. 2.5 How Accurate is My Mean? 2.6 Is the New Additive
an Improvement? 2.7 How Many Trials are Needed for an Experiment? 2.8 Were
the Aims of the Investigation Achieved? 2.9 Problems. 3 Improving
Effectiveness Using a Paired Design. 3.1 Introduction. 3.2 An Example: Who
Wears the Trousers? 3.3 How Do We Rate the Wear? 3.4 How Often Do You Carry
Out an Assessment? 3.5 Choosing the Participants. 3.6 Controlling the
Participants. 3.7 The Paired Design. 3.8 Was the Experiment Successful? 3.9
Problems. 4 A Simple but Effective Design for Two Variables. 4.1
Introduction. 4.2 An Investigation. 4.3 Limitations of a
One-Variable-at-a-Time Experiment. 4.4 A Factorial Experiment. 4.5
Confidence Intervals for Effect Estimates. 4.6 What Conditions Should be
Recommended? 4.7 Were the Aims of the Investigation Achieved? 4.8 Problems.
5 Investigating 3 and 4 Variables in an Experiment. 5.1 Introduction. 5.2
An Experiment with Three Variables. 5.3 The Design Matrix Method. 5.4
Computation of Predicted Values. 5.5 Computation of Confidence Interval.
5.6 95% Confidence Interval for an Effect. 5.7 95% Confidence Interval for
a Predicted Value. 5.8 Sequencing of the Trials. 5.9 Were the Aims of the
Experiment Achieved? 5.10 A Four-Variable Experiment. 5.11 Half-Normal
Plots. 5.12 Were the Aims of the Experiment Achieved? 5.13 Problems. 6 More
for Even Less: Using a Fraction of a Full Design. 6.1 Introduction. 6.2
Obtaining Half-Fractional Designs. 6.2.1 With Defining Contrast ABC. 6.2.2
With Defining Contrast AC. 6.3 Design of 1/2(24) Experiment. 6.4 Analysing
a Fractional Experiment. 6.5 Summary 59 6.6 Did Wheelwright Achieve the
Aims of His Experiment? 6.7 When and Where to Choose a Fractional Design.
6.7.1 Three Variables. 6.7.2 Four Variables. 6.7.3 Five Variables or More.
6.8 Problems. 7 Saturated Designs. 7.1 Introduction. 7.2 Towards a Better
Oil? 7.3 The Experiment. 7.4 An Alternative Procedure for Estimating the
Residual SD. 7.5 Did Doug Achieve the Aims of His Experiment? 7.6 How
Rugged is My Method? 7.7 Analysis of the Design. 7.8 Conclusions from the
Experiment. 7.9 Did Serena Achieve Her Aims? 7.10 Which Order Should I Use
for the Trials? 7.11 How to Obtain the Designs. 7.12 Other Uses of
Saturated Designs. 7.13 Problems. 8 Regression Analysis. 8.1 Introduction.
8.2 Example: Keeping Quality of Sprouts. 8.3 How Good a Fit Has the Line to
the Data? 8.4 Residuals. 8.5 Percentage Fit. 8.6 Correlation Coefficient.
8.7 Percentage Fit - An Easier Method. 8.8 Is There a Significant
Relationship between the Variables? 8.9 Confidence Intervals for the
Regression Statistics. 8.10 Assumptions. 8.11 Problem. 9 Multiple
Regression: The First Essentials. 9.1 Introduction. 9.2 An Experiment to
Improve the Yield. 9.3 Building a Regression Model. 9.4 Selecting the First
Independent Variable. 9.5 Relationship between Yield and Weight. 9.6 Model
Building. 9.7 Selecting the Second Independent Variable. 9.8 An Alternative
Model. 9.9 Limitations to the Analysis. 9.10 Was the Experiment Successful?
9.11 Problems. 10 Designs to Generate Response Surfaces. 10.1 Introduction.
10.2 An Example: Easing the Digestion. 10.3 Analysis of Crushing Strength.
10.4 Analysis of Dissolution Time. 10.5 How Many Levels of a Variable
Should We Use in a Design? 10.6 Was the Experiment Successful? 10.7
Problem. 11 Outliers and Influential Observations. 11.1 Introduction. 11.2
An Outlier in One Variable. 11.3 Other Outlier Tests. 11.4 Outliers in
Regression. 11.5 Influential Observations. 11.6 Outliers and Influence in
Multiple Regression. 11.7 What to Do After Detection? 12 Central Composite
Designs. 12.1 Introduction. 12.2 An Example: Design the Crunchiness. 12.3
Estimating the Variability. 12.4 Estimating the Effects. 12.5 Using
Multiple Regression. 12.6 Second Stage of the Design. 12.7 Has the
Experiment Been Successful? 12.8 Choosing a Central Composite Design. 12.9
Critique of Central Composite Designs. 13 Designs For Mixtures. 13.1
Introduction. 13.2 Mixtures of Two Components. 13.3 A Concrete Case Study.
13.4 Design and Analysis for a 3-Component Mixture. 13.5 Designs with
Mixture Variables and Process Variables. 13.6 Fractional Experiments. 13.7
Was the Experiment Successful? 14 Computer-Aided Experimental Design
(CAED). 14.1 Introduction. 14.2 How It Works. 14.3 An Example. 14.4
Selecting the Repertoire. 14.5 Selecting the Model and Number of Trials.
14.6 How the Program Chooses the Design Set. 14.7 Summary. 14.8 Problems.
15 Optimization Designs. 15.1 Introduction. 15.2 The Principles behind
EVOP. 15.3 EVOP: The Experimental Design. 15.4 Running EVOP Programmes.
15.5 The Principles of Simplex Optimization. 15.6 Simplex Optimization: An
Experiment. 15.7 Comparison of EVOP, Simplex and Response Surface Methods.
15.7.1 Number of Trials (Batches) Required. 15.7.2 Production of
In-Specification Product. 15.7.3 Obtaining Optimum. 15.7.4 Understanding
Variables. 15.7.5 Amount of Judgement Required. 15.7.6 Coping with Nuisance
Variables. 16 Improving a Bad Experiment. 16.1 Introduction. 16.2 Was the
Experiment Successful? 17 How to Compare Several Treatments. 17.1
Introduction. 17.2 An Example: Which is the Best Treatment? 17.3 Analysis
of Variance. 17.4 Multiple Comparison Test. 17.5 Are the Standard
Deviations Significantly Different? 17.6 Cochran's Test for Standard
Deviations. 17.7 When Should the Above Method Not be Used? 17.8 Was
Golightly's Experiment Successful? 17.9 Problems. 18 Experiments in Blocks.
18.1 Introduction. 18.2 An Example: Kill the Sweat. 18.3 Analysis of the
Data. 18.4 Benefits of a Randomized Block Experiment. 18.5 Was the
Experiment Successful? 18.6 Double and Treble Blocking. 18.7 Example: A
Dog's Life. 18.8 The Latin Square Design. 18.9 Latin Square Analysis of
Variance. 18.10 Properties and Assumptions of the Latin Square Design.
18.11 Examples of Latin Squares. 18.12 Was the Experiment Successful? 18.13
An Extra Blocking Factor - Graeco-Latin Square. 18.14 Problem. 19 Two-Way
Designs. 19.1 Introduction. 19.2 An Example: Improving the Taste of Coffee.
19.3 Two-Way Analysis of Variance. 19.4 Multiple Comparison Test. 19.5 Was
the Experiment Successful? 19.6 Problem. 20 Too Much at Once: Incomplete
Block Experiments. 20.1 Introduction. 20.2 Example: An Incomplete Block
Experiment. 20.3 Adjusted Means. 20.4 Analysis of Variance for Balanced
Incomplete Block Design. 20.5 Alternative Designs. 20.6 A Design with a
Control. 20.7 Was Jeremy's Experiment Successful? 20.8 Problem. 21 23 Ways
of Messing Up an Experiment. 21.1 Introduction. 21.2 23 Ways of Messing Up
an Experiment. 21.3 Initial Thoughts When Planning an Experiment. 21.4
Developing the Ideas. 21.5 Designing the Experiment. 21.6 Conducting the
Experiment. 21.7 Analysing the Data. 21.8 Summary. Solutions to Problems.
Statistical Tables. Index.

Rezensionen

"There is much explanatory verbiage to mathematical content that will be appreciated by the beginner. The writing style is clear and concise, as one would expect from a text derived from years of teaching industrial scientists . . . this text is recommended to the beginner and other excellent texts." (Scientific Computing, 1 August 2010)