{"product_id":"fundamentals-of-statistical-experimental-design-and-analysis-isbn-9781118954638","title":"Fundamentals of Statistical Experimental Design and Analysis","description":"\u003cp\u003eProfessionals 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.\u003cbr\u003e \u003cbr\u003e 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 \u003ci\u003ewhat-do-we-do-next?\u003c\/i\u003e 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.  \u003cbr\u003e \u003cbr\u003e \u003ci\u003eFundamentals of Statistical Experimental Design and Analysis\u003c\/i\u003e introduces the basic elements of an experimental design, and the basic concepts underlying statistical analyses. Subsequent chapters address the following families of experimental designs:\u003cbr\u003e \u003cbr\u003e \u003c\/p\u003e \u003cul\u003e \u003cli\u003eCompletely Randomized designs, with single or multiple treatment factors, quantitative or qualitative\u003c\/li\u003e \u003cli\u003eRandomized Block designs\u003c\/li\u003e \u003cli\u003eLatin Square designs\u003c\/li\u003e \u003cli\u003eSplit-Unit designs\u003c\/li\u003e \u003cli\u003eRepeated Measures designs\u003c\/li\u003e \u003cli\u003eRobust designs\u003c\/li\u003e \u003cli\u003eOptimal designs\u003c\/li\u003e \u003c\/ul\u003e \u003cbr\u003e 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. \u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003eAcknowledgments xix\u003c\/p\u003e \u003cp\u003eCredits xxi\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMotivation: Why Experiment? 1\u003c\/p\u003e \u003cp\u003eSteps in an Experimental Program 2\u003c\/p\u003e \u003cp\u003ePlanning and analysis 2\u003c\/p\u003e \u003cp\u003eCommunication 3\u003c\/p\u003e \u003cp\u003eSubject‐Matter Passion 4\u003c\/p\u003e \u003cp\u003eCase Study 5\u003c\/p\u003e \u003cp\u003eOverview of Text 9\u003c\/p\u003e \u003cp\u003eAssignment 10\u003c\/p\u003e \u003cp\u003eReferences 10\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Fundamentals of Experimental Design 11\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 11\u003c\/p\u003e \u003cp\u003eExperimental Structure 13\u003c\/p\u003e \u003cp\u003eExperimental units 13\u003c\/p\u003e \u003cp\u003eBlocks and block structures 15\u003c\/p\u003e \u003cp\u003eTreatments and treatment structures 17\u003c\/p\u003e \u003cp\u003eResponse measurement 19\u003c\/p\u003e \u003cp\u003ePrinciples of Experimental Design 20\u003c\/p\u003e \u003cp\u003eReplication 21\u003c\/p\u003e \u003cp\u003eRandomization 22\u003c\/p\u003e \u003cp\u003eBlocking 24\u003c\/p\u003e \u003cp\u003eControl 26\u003c\/p\u003e \u003cp\u003eAssignment 27\u003c\/p\u003e \u003cp\u003eReferences 27\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Fundamentals of Statistical Data Analysis 29\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 29\u003c\/p\u003e \u003cp\u003eBoys’ Shoes Experiment 30\u003c\/p\u003e \u003cp\u003eExperimental design 30\u003c\/p\u003e \u003cp\u003eGraphical displays 31\u003c\/p\u003e \u003cp\u003eSignificance testing 34\u003c\/p\u003e \u003cp\u003eProbability and probability distributions 34\u003c\/p\u003e \u003cp\u003eSign test 36\u003c\/p\u003e \u003cp\u003eMisinterpretation of P‐values 38\u003c\/p\u003e \u003cp\u003eRandomization test 39\u003c\/p\u003e \u003cp\u003eNormal distribution theory t‐test 40\u003c\/p\u003e \u003cp\u003eSummary and discussion: Significance tests 46\u003c\/p\u003e \u003cp\u003eEconomic analysis: The bigger picture 48\u003c\/p\u003e \u003cp\u003eStatistical confidence intervals 50\u003c\/p\u003e \u003cp\u003eDiscussion 53\u003c\/p\u003e \u003cp\u003eWhy calculate statistical confidence limits? 54\u003c\/p\u003e \u003cp\u003eSample size determination 54\u003c\/p\u003e \u003cp\u003eTomato Fertilizer Experiment 56\u003c\/p\u003e \u003cp\u003eExperimental design 56\u003c\/p\u003e \u003cp\u003eAnalysis 1: Plot the data 56\u003c\/p\u003e \u003cp\u003eThe value of randomization 58\u003c\/p\u003e \u003cp\u003eThe importance of ancillary data 59\u003c\/p\u003e \u003cp\u003eA New Tomato Experiment 59\u003c\/p\u003e \u003cp\u003eAnalysis 1: Plot the data 59\u003c\/p\u003e \u003cp\u003eSignificance tests 62\u003c\/p\u003e \u003cp\u003eRank sum test 63\u003c\/p\u003e \u003cp\u003eRandomization test 64\u003c\/p\u003e \u003cp\u003eNormal theory t‐test 66\u003c\/p\u003e \u003cp\u003eConfidence intervals 69\u003c\/p\u003e \u003cp\u003eDetermining the size of an experiment 71\u003c\/p\u003e \u003cp\u003eComparing Standard Deviations 77\u003c\/p\u003e \u003cp\u003eDiscussion 79\u003c\/p\u003e \u003cp\u003eAppendix 3.A The Binomial Distribution 79\u003c\/p\u003e \u003cp\u003eAppendix 3.B Sampling from a Normal Distribution 81\u003c\/p\u003e \u003cp\u003eAppendix 3.C Statistical Underpinnings 85\u003c\/p\u003e \u003cp\u003eSingle sample 86\u003c\/p\u003e \u003cp\u003eTwo samples 87\u003c\/p\u003e \u003cp\u003eAssignment 89\u003c\/p\u003e \u003cp\u003eReferences 89\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Completely Randomized Design 91\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 91\u003c\/p\u003e \u003cp\u003eDesign Issues 92\u003c\/p\u003e \u003cp\u003eCRD: Single Qualitative Factor 92\u003c\/p\u003e \u003cp\u003eExample: Market research 92\u003c\/p\u003e \u003cp\u003eAnalysis of Variance 95\u003c\/p\u003e \u003cp\u003eWithin‐group variation 96\u003c\/p\u003e \u003cp\u003eAmong‐groups variation 97\u003c\/p\u003e \u003cp\u003eThe F‐test 98\u003c\/p\u003e \u003cp\u003eAnalysis of variance 99\u003c\/p\u003e \u003cp\u003eDiscussion 100\u003c\/p\u003e \u003cp\u003eResults 101\u003c\/p\u003e \u003cp\u003eTesting the Assumptions of Equal Variances and Normality 103\u003c\/p\u003e \u003cp\u003eConfidence Intervals 103\u003c\/p\u003e \u003cp\u003eInference 105\u003c\/p\u003e \u003cp\u003eStatistical Prediction Interval 105\u003c\/p\u003e \u003cp\u003eExample: Tomato Fertilizer Experiment Revisited 106\u003c\/p\u003e \u003cp\u003eSizing a Completely Randomized Experiment 107\u003c\/p\u003e \u003cp\u003eCRD: Single Quantitative Factor 107\u003c\/p\u003e \u003cp\u003eExample: Growth rate of rats 108\u003c\/p\u003e \u003cp\u003eGraphical display 109\u003c\/p\u003e \u003cp\u003eCurve fit 109\u003c\/p\u003e \u003cp\u003eAnalysis of variance 111\u003c\/p\u003e \u003cp\u003eDesign Issues 113\u003c\/p\u003e \u003cp\u003eEnhanced Case Study: Power Window Gear Teeth 114\u003c\/p\u003e \u003cp\u003eGraphical display 117\u003c\/p\u003e \u003cp\u003eAnova 119\u003c\/p\u003e \u003cp\u003eDiscussion 120\u003c\/p\u003e \u003cp\u003eAssignment 120\u003c\/p\u003e \u003cp\u003eReferences 121\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Completely Randomized Design with Multiple Treatment Factors 123\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 123\u003c\/p\u003e \u003cp\u003eDesign Issues 124\u003c\/p\u003e \u003cp\u003eExample 1 (Two qualitative factors): Poisons and antidotes 124\u003c\/p\u003e \u003cp\u003eAnalysis 1: Plot the data 126\u003c\/p\u003e \u003cp\u003eEyeball analysis 126\u003c\/p\u003e \u003cp\u003eInteraction 128\u003c\/p\u003e \u003cp\u003eAnova 130\u003c\/p\u003e \u003cp\u003eGeneralizing the ANOVA for a CRD with two factors 131\u003c\/p\u003e \u003cp\u003eAntidote B versus Antidote D 132\u003c\/p\u003e \u003cp\u003eEstimation of effects 133\u003c\/p\u003e \u003cp\u003ePrediction intervals 135\u003c\/p\u003e \u003cp\u003eProbability estimation and tolerance intervals 136\u003c\/p\u003e \u003cp\u003eFurther experiments 138\u003c\/p\u003e \u003cp\u003eExample 2 (Two quantitative factors): Ethanol blends and CO emissions 139\u003c\/p\u003e \u003cp\u003eData displays 142\u003c\/p\u003e \u003cp\u003eDiscussion 144\u003c\/p\u003e \u003cp\u003eRegression analysis and ANOVA 145\u003c\/p\u003e \u003cp\u003eDiscussion 148\u003c\/p\u003e \u003cp\u003eResponse Surface Designs 149\u003c\/p\u003e \u003cp\u003eExtensions: More than two treatment factors 150\u003c\/p\u003e \u003cp\u003eExample 3: Poison\/antidote experiment extended 151\u003c\/p\u003e \u003cp\u003eExample 4: Ethanol experiment extended 154\u003c\/p\u003e \u003cp\u003eSpecial Case: Two‐Level Factorial Experiments 155\u003c\/p\u003e \u003cp\u003eExample 5: Pot production 156\u003c\/p\u003e \u003cp\u003eAnalysis 1: Look at the data 158\u003c\/p\u003e \u003cp\u003eAnalysis 2: Regression analysis 159\u003c\/p\u003e \u003cp\u003eAnalysis 2: Stepwise regression 162\u003c\/p\u003e \u003cp\u003eAnalysis 3: “Effect sparsity” and graphical analysis 162\u003c\/p\u003e \u003cp\u003eFractional Two‐Level Factorials 167\u003c\/p\u003e \u003cp\u003eExample 6: E‐mail marketing 167\u003c\/p\u003e \u003cp\u003eOne‐factor‐at‐a‐time designs 168\u003c\/p\u003e \u003cp\u003eResults: E‐mail experiment 170\u003c\/p\u003e \u003cp\u003eExample 7: Flower pot experiment revisited 171\u003c\/p\u003e \u003cp\u003eExtensions 175\u003c\/p\u003e \u003cp\u003eAssignment 175\u003c\/p\u003e \u003cp\u003eReferences 175\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Randomized Complete Block Design 177\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 177\u003c\/p\u003e \u003cp\u003eDesign Issues 178\u003c\/p\u003e \u003cp\u003eRBD with replication: Example 1—battery experiment 179\u003c\/p\u003e \u003cp\u003eAnalysis 1: Plot the data 180\u003c\/p\u003e \u003cp\u003eAnalysis of variance 181\u003c\/p\u003e \u003cp\u003eReliability analysis 183\u003c\/p\u003e \u003cp\u003eFurther analysis 184\u003c\/p\u003e \u003cp\u003eBringing subject‐matter knowledge to bear 185\u003c\/p\u003e \u003cp\u003eExample 2: More tomato fertilizer experiments 187\u003c\/p\u003e \u003cp\u003eExample 3: More gear teeth experiments 188\u003c\/p\u003e \u003cp\u003eRBD with Single Replication 188\u003c\/p\u003e \u003cp\u003eExample 4: Penicillin production 189\u003c\/p\u003e \u003cp\u003eComponents of variation 191\u003c\/p\u003e \u003cp\u003eSizing a Randomized Block Experiment 194\u003c\/p\u003e \u003cp\u003eTrue Replication 195\u003c\/p\u003e \u003cp\u003eExample 5: Cookies 195\u003c\/p\u003e \u003cp\u003eExample 6: Battery experiment revisited 196\u003c\/p\u003e \u003cp\u003eExample 7: Boys’ shoes revisited 197\u003c\/p\u003e \u003cp\u003eExtensions of the RBD 199\u003c\/p\u003e \u003cp\u003eMultifactor treatments and blocks—example: Penicillin experiment extended 199\u003c\/p\u003e \u003cp\u003eExample 8: A blocks‐only “experiment”—textile production 201\u003c\/p\u003e \u003cp\u003eAnalysis 1: Plot the data 201\u003c\/p\u003e \u003cp\u003eDiscussion 202\u003c\/p\u003e \u003cp\u003eBalanced Incomplete Block Designs 203\u003c\/p\u003e \u003cp\u003eExample: Boys’ shoes revisited again 203\u003c\/p\u003e \u003cp\u003eSummary 205\u003c\/p\u003e \u003cp\u003eAssignment 205\u003c\/p\u003e \u003cp\u003eReferences 205\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Other Experimental Designs 207\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 207\u003c\/p\u003e \u003cp\u003eLatin Square Design 208\u003c\/p\u003e \u003cp\u003eExample: Gasoline additives and car emissions 208\u003c\/p\u003e \u003cp\u003eAnalysis 1: Plot the data 212\u003c\/p\u003e \u003cp\u003eAnova 214\u003c\/p\u003e \u003cp\u003eDiscussion 215\u003c\/p\u003e \u003cp\u003eFollow‐on experiments 216\u003c\/p\u003e \u003cp\u003eExercise 216\u003c\/p\u003e \u003cp\u003eExtensions 217\u003c\/p\u003e \u003cp\u003eSplit‐Unit Designs 218\u003c\/p\u003e \u003cp\u003eExample: Corrosion Resistance 220\u003c\/p\u003e \u003cp\u003eAnalysis 1: Plot the data 222\u003c\/p\u003e \u003cp\u003eAnova 225\u003c\/p\u003e \u003cp\u003eDiscussion 228\u003c\/p\u003e \u003cp\u003eRepeated Measures Designs 230\u003c\/p\u003e \u003cp\u003eExample: Effects of drugs on heart rate 231\u003c\/p\u003e \u003cp\u003eAnalysis 1: Plot the data 232\u003c\/p\u003e \u003cp\u003eDiscussion 234\u003c\/p\u003e \u003cp\u003eExtensions 235\u003c\/p\u003e \u003cp\u003eRobust Designs 235\u003c\/p\u003e \u003cp\u003eIntroduction 235\u003c\/p\u003e \u003cp\u003eVariance transmission 235\u003c\/p\u003e \u003cp\u003eMathematical model: Robustness 238\u003c\/p\u003e \u003cp\u003eConcluding comments 239\u003c\/p\u003e \u003cp\u003eOptimal Designs 240\u003c\/p\u003e \u003cp\u003eIntroduction 240\u003c\/p\u003e \u003cp\u003eFinding “optimal experimental designs” 240\u003c\/p\u003e \u003cp\u003eDesign augmentation 242\u003c\/p\u003e \u003cp\u003eAssignment 243\u003c\/p\u003e \u003cp\u003eReferences 243\u003c\/p\u003e \u003cp\u003eIndex 245\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e “This is an interesting and very useful book that explains the basic concepts and fundamentals of statistical experimental design and analysis to its readers in an easy-to-understand and accessible style”……” Rather than presenting the statistical design of experiments as a cut-and-dried subject, the author spices up this book with a sense of humour and fun”…..” Students, professionals, and researchers will find it interesting. It is a welcome addition to the statistics market” \u003cb\u003eD. V. Chopra, MathSciNet, Aug 2017\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e \u003cp\u003e“Easterling sets out to provide a textbook for an undergraduate course in applied experimental design for a mixed group of students. He succeeds admirably. Although many excellent texts on experimental design exist for statistics students, most are too technical for mixed disciplines. This book covers only basic designs but with extensive discussion of matters other textbooks elide or ignore. Examples from respected textbooks are elaborated to show the reasoning underpinning experimentation and the need to combine statistical and subject-area knowledge … this is a book that can be enjoyed by students being taught how and why to work with a statistician, and by statisticians who want to work more productively in teams with other disciplines.” \u003cb\u003eSignificance, 14:6 (2017)\u003c\/b\u003e\u003c\/p\u003e \u003cb\u003e\u003cbr\u003e\u003c\/b\u003e  \u003cb\u003eRobert G. Easterling.\u003c\/b\u003e Dr. Easterling is retired from Sandia National Laboratories where he was a statistical consultant, manager, and senior scientist. He is a Fellow of the American Statistical Association, a former Editor of \u003ci\u003eTechnometrics\u003c\/i\u003e, and a recipient of the American Society for Quality’s Brumbaugh Award. He holds a Ph.D. in statistics from Oklahoma State University.  \u003cp\u003eProfessionals 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.\u003cbr\u003e \u003cbr\u003e 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 \u003ci\u003ewhat-do-we-do-next?\u003c\/i\u003e 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.  \u003cbr\u003e \u003cbr\u003e \u003ci\u003eFundamentals of Statistical Experimental Design and Analysis\u003c\/i\u003e introduces the basic elements of an experimental design, and the basic concepts underlying statistical analyses. Subsequent chapters address the following families of experimental designs:\u003cbr\u003e \u003cbr\u003e \u003c\/p\u003e \u003cul\u003e \u003cli\u003eCompletely Randomized designs, with single or multiple treatment factors, quantitative or qualitative\u003c\/li\u003e \u003cli\u003eRandomized Block designs\u003c\/li\u003e \u003cli\u003eLatin Square designs\u003c\/li\u003e \u003cli\u003eSplit-Unit designs\u003c\/li\u003e \u003cli\u003eRepeated Measures designs\u003c\/li\u003e \u003cli\u003eRobust designs\u003c\/li\u003e \u003cli\u003eOptimal designs\u003c\/li\u003e \u003c\/ul\u003e \u003cbr\u003e 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.","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989264580837,"sku":"NP9781118954638","price":78.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118954638.jpg?v=1761783435","url":"https:\/\/k12savings.com\/es\/products\/fundamentals-of-statistical-experimental-design-and-analysis-isbn-9781118954638","provider":"K12savings","version":"1.0","type":"link"}