Professionals in all areas – business; government; the physical, life, and social sciences; engineering; medicine, etc. – benefit from using statistical experimental design to better understand their worlds and then use that understanding to improve the products, processes, and programs they are responsible for. This book aims to provide the practitioners of tomorrow with a memorable, easy to read, engaging guide to statistics and experimental design. This book uses examples, drawn from a variety of established texts, and embeds them in a business or scientific context, seasoned with a dash of humor, to emphasize the issues and ideas that led to the experiment and the what-do-we-do-next? steps after the experiment. Graphical data displays are emphasized as means of discovery and communication and formulas are minimized, with a focus on interpreting the results that software produce. The role of subject-matter knowledge, and passion, is also illustrated. The examples do not require specialized knowledge, and the lessons they contain are transferrable to other contexts.   Fundamentals of Statistical Experimental Design and Analysis introduces the basic elements of an experimental design, and the basic concepts underlying statistical analyses. Subsequent chapters address the following families of experimental designs: Completely Randomized designs, with single or multiple treatment factors, quantitative or qualitative Randomized Block designs Latin Square designs Split-Unit designs Repeated Measures designs Robust designs Optimal designs Written in an accessible, student-friendly style, this book is suitable for a general audience and particularly for those professionals seeking to improve and apply their understanding of experimental design.

Table of Contents:
Preface xiii Acknowledgments xix Credits xxi 1 Introduction 1 Motivation: Why Experiment? 1 Steps in an Experimental Program 2 Planning and analysis 2 Communication 3 Subject‐Matter Passion 4 Case Study 5 Overview of Text 9 Assignment 10 References 10 2 Fundamentals of Experimental Design 11 Introduction 11 Experimental Structure 13 Experimental units 13 Blocks and block structures 15 Treatments and treatment structures 17 Response measurement 19 Principles of Experimental Design 20 Replication 21 Randomization 22 Blocking 24 Control 26 Assignment 27 References 27 3 Fundamentals of Statistical Data Analysis 29 Introduction 29 Boys’ Shoes Experiment 30 Experimental design 30 Graphical displays 31 Significance testing 34 Probability and probability distributions 34 Sign test 36 Misinterpretation of P‐values 38 Randomization test 39 Normal distribution theory t‐test 40 Summary and discussion: Significance tests 46 Economic analysis: The bigger picture 48 Statistical confidence intervals 50 Discussion 53 Why calculate statistical confidence limits? 54 Sample size determination 54 Tomato Fertilizer Experiment 56 Experimental design 56 Analysis 1: Plot the data 56 The value of randomization 58 The importance of ancillary data 59 A New Tomato Experiment 59 Analysis 1: Plot the data 59 Significance tests 62 Rank sum test 63 Randomization test 64 Normal theory t‐test 66 Confidence intervals 69 Determining the size of an experiment 71 Comparing Standard Deviations 77 Discussion 79 Appendix 3.A The Binomial Distribution 79 Appendix 3.B Sampling from a Normal Distribution 81 Appendix 3.C Statistical Underpinnings 85 Single sample 86 Two samples 87 Assignment 89 References 89 4 Completely Randomized Design 91 Introduction 91 Design Issues 92 CRD: Single Qualitative Factor 92 Example: Market research 92 Analysis of Variance 95 Within‐group variation 96 Among‐groups variation 97 The F‐test 98 Analysis of variance 99 Discussion 100 Results 101 Testing the Assumptions of Equal Variances and Normality 103 Confidence Intervals 103 Inference 105 Statistical Prediction Interval 105 Example: Tomato Fertilizer Experiment Revisited 106 Sizing a Completely Randomized Experiment 107 CRD: Single Quantitative Factor 107 Example: Growth rate of rats 108 Graphical display 109 Curve fit 109 Analysis of variance 111 Design Issues 113 Enhanced Case Study: Power Window Gear Teeth 114 Graphical display 117 Anova 119 Discussion 120 Assignment 120 References 121 5 Completely Randomized Design with Multiple Treatment Factors 123 Introduction 123 Design Issues 124 Example 1 (Two qualitative factors): Poisons and antidotes 124 Analysis 1: Plot the data 126 Eyeball analysis 126 Interaction 128 Anova 130 Generalizing the ANOVA for a CRD with two factors 131 Antidote B versus Antidote D 132 Estimation of effects 133 Prediction intervals 135 Probability estimation and tolerance intervals 136 Further experiments 138 Example 2 (Two quantitative factors): Ethanol blends and CO emissions 139 Data displays 142 Discussion 144 Regression analysis and ANOVA 145 Discussion 148 Response Surface Designs 149 Extensions: More than two treatment factors 150 Example 3: Poison/antidote experiment extended 151 Example 4: Ethanol experiment extended 154 Special Case: Two‐Level Factorial Experiments 155 Example 5: Pot production 156 Analysis 1: Look at the data 158 Analysis 2: Regression analysis 159 Analysis 2: Stepwise regression 162 Analysis 3: “Effect sparsity” and graphical analysis 162 Fractional Two‐Level Factorials 167 Example 6: E‐mail marketing 167 One‐factor‐at‐a‐time designs 168 Results: E‐mail experiment 170 Example 7: Flower pot experiment revisited 171 Extensions 175 Assignment 175 References 175 6 Randomized Complete Block Design 177 Introduction 177 Design Issues 178 RBD with replication: Example 1—battery experiment 179 Analysis 1: Plot the data 180 Analysis of variance 181 Reliability analysis 183 Further analysis 184 Bringing subject‐matter knowledge to bear 185 Example 2: More tomato fertilizer experiments 187 Example 3: More gear teeth experiments 188 RBD with Single Replication 188 Example 4: Penicillin production 189 Components of variation 191 Sizing a Randomized Block Experiment 194 True Replication 195 Example 5: Cookies 195 Example 6: Battery experiment revisited 196 Example 7: Boys’ shoes revisited 197 Extensions of the RBD 199 Multifactor treatments and blocks—example: Penicillin experiment extended 199 Example 8: A blocks‐only “experiment”—textile production 201 Analysis 1: Plot the data 201 Discussion 202 Balanced Incomplete Block Designs 203 Example: Boys’ shoes revisited again 203 Summary 205 Assignment 205 References 205 7 Other Experimental Designs 207 Introduction 207 Latin Square Design 208 Example: Gasoline additives and car emissions 208 Analysis 1: Plot the data 212 Anova 214 Discussion 215 Follow‐on experiments 216 Exercise 216 Extensions 217 Split‐Unit Designs 218 Example: Corrosion Resistance 220 Analysis 1: Plot the data 222 Anova 225 Discussion 228 Repeated Measures Designs 230 Example: Effects of drugs on heart rate 231 Analysis 1: Plot the data 232 Discussion 234 Extensions 235 Robust Designs 235 Introduction 235 Variance transmission 235 Mathematical model: Robustness 238 Concluding comments 239 Optimal Designs 240 Introduction 240 Finding “optimal experimental designs” 240 Design augmentation 242 Assignment 243 References 243 Index 245