Effective Experimentation: For Scientists and Technologists

Effective Experimentation: For Scientists and Technologists.
By Richard Boddy, Gordon Laird Smith

• Publisher: Wiley

• Number Of Pages: 270
• Publication Date: 2010-08-30
• ISBN-10 / ASIN: 0470684607
• ISBN-13 / EAN: 9780470684603

Product Description:

This book has been developed from courses run by Statistics for Industry Limited for the past 30 years and demonstrates the ways in which statistical methods can be applied successfully, each method is introduced and used in a real situation from industry or research, each chapter includes examples obtained from the authors experience within the field. Features examples from many industries, such as chemicals, plastics, oils, nuclear, food, drink, lighting, water and pharmaceuticals.

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Table of Contents:

Front Matter

Chapter 1:
Why Bother to Design an Experiment? (p 1-3)
1.1 Introduction
1.2 Examples and Benefits
1.2.1 Develop a Better Product
1.2.2 Which Antiperspirant is Best
1.2.3 A Complex Protocol
1.3 Good Design and Good Analysis

Chapter 2:
A Change for the Better - Significance Testing (p 5-14)
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

Chapter 3:
Improving Effectiveness using a Paired Design (p 15-19)
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

Chapter 4:
A Simple but Effective Design for Two Variables (p 21-33)
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

Chapter 5:
Investigating 3 and 4 Variables in an Experiment (p 35-49)
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

Chapter 6:
More for Even Less: Using a Fraction of a Full Design (p 51-64)
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
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

Chapter 7:
Saturated Designs (p 65-75)
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

Chapter 8:
Regression Analysis (p 77-86)
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

Chapter 9:
Multiple Regression: The First Essentials (p 87-100)
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

Chapter 10:
Designs to Generate Response Surfaces (p 101-111)
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

Chapter 11:
Outliers and Influential Observations (p 113-120)
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?

Chapter 12:
Central Composite Designs (p 121-131)
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

Chapter 13:
Designs for Mixtures (p 133-143)
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?

Chapter 14:
Computer-Aided Experimental Design (CAED) (p 145-153)
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

Chapter 15:
Optimization Designs (p 155-165)
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

Chapter 16:
Improving a Bad Experiment (p 167-171)
16.1 Introduction
16.2 Was the Experiment Successful?

Chapter 17:
How to Compare Several Treatments (p 173-181)
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

Chapter 18:
Experiments in Blocks (p 183-193)
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

Chapter 19:
Two-Way Designs (p 195-201)
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

Chapter 20:
Too Much at Once: Incomplete Block Experiments (p 203-211)
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

Chapter 21:
23 Ways of Messing Up an Experiment (p 213-217)
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 (p 219-243)

Statistical Tables (p 245-256)

Index (p 257)