{"product_id":"business-experiments-with-r-isbn-9781119689706","title":"Business Experiments with R","description":"BUSINESS EXPERIMENTS with \u003cb\u003eR\u003c\/b\u003e \u003cp\u003eA unique text that simplifies experimental business design and is dedicated to the R language\u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eBusiness Experiments with R\u003c\/i\u003e offers a guide to, and explores the fundamentals of experimental business designs. The book fills a gap in the literature to provide a text on the topic of business statistics that addresses issues such as small samples, lack of normality, and data confounding. The author—a noted expert on the topic—puts the focus on the A\/B tests (and their variants) that are widely used in industry, but not typically covered in business statistics textbooks. \u003c\/p\u003e\u003cp\u003eThe text contains the tools needed to design and analyze two-treatment experiments (i.e., A\/B tests) to answer business questions. The author highlights the strategic and technical issues involved in designing experiments that will truly affect organizations. The book then builds on the foundation in Part I and expands the multivariable testing. Since today’s companies are using experiments to solve a broad range of problems, \u003ci\u003eBusiness Experiments with R\u003c\/i\u003e is an essential resource for any business student. This important text: \u003c\/p\u003e\u003cli\u003e\u003cbl\u003ePresents the key ideas that business students need to know about experiments\u003c\/bl\u003e\u003c\/li\u003e \u003cli\u003e\u003cbl\u003eOffers a series of examples, focusing on a specific business question\u003c\/bl\u003e\u003c\/li\u003e \u003cli\u003e\u003cbl\u003eHelps develop the ability to frame ill-defined problems and determine what data and analysis would provide information about that problem\u003c\/bl\u003e\u003c\/li\u003e \u003cp\u003eWritten for students of general business, marketing, and business analytics, \u003ci\u003eBusiness Experiments with R\u003c\/i\u003e is an important text that helps to answer business questions by highlighting the strategic and technical issues involved in designing experiments that will truly affect organizations. \u003c\/p\u003e\u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003eAcknowledgments xvii\u003c\/p\u003e \u003cp\u003eBruce McCullough xix\u003c\/p\u003e \u003cp\u003eAbout the Companion Website xxi\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Why Experiment? \u003c\/b\u003e\u003cb\u003e1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Case: Life Expectancy and Newspapers 2\u003c\/p\u003e \u003cp\u003eExercises 6\u003c\/p\u003e \u003cp\u003e1.2 Case: Credit Card Defaults 7\u003c\/p\u003e \u003cp\u003e1.2.1 Lurking Variables 9\u003c\/p\u003e \u003cp\u003e1.2.2 Sample Selection Bias 11\u003c\/p\u003e \u003cp\u003eExercises 13\u003c\/p\u003e \u003cp\u003e1.3 Case: Salk Polio Vaccine Trials 14\u003c\/p\u003e \u003cp\u003eExercises 17\u003c\/p\u003e \u003cp\u003e1.4 What Is a Business Experiment? 17\u003c\/p\u003e \u003cp\u003e1.4.1 Four Steps of an Experiment 21\u003c\/p\u003e \u003cp\u003e1.4.2 Big Three of Causality 22\u003c\/p\u003e \u003cp\u003e1.4.3 Most Experiments Fail 23\u003c\/p\u003e \u003cp\u003eExercises 24\u003c\/p\u003e \u003cp\u003e1.5 Improving Website Designs 24\u003c\/p\u003e \u003cp\u003eExercises 30\u003c\/p\u003e \u003cp\u003e1.6 A Brief History of Experiments 31\u003c\/p\u003e \u003cp\u003e1.7 Chapter Exercises 34\u003c\/p\u003e \u003cp\u003e1.8 Learning More 34\u003c\/p\u003e \u003cp\u003e1.9 Statistics Refresher 38\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Analyzing A\/B Tests: Basics \u003c\/b\u003e\u003cb\u003e43\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Case: Improving Response to Sales Calls (Two-Sample Test of Means) 44\u003c\/p\u003e \u003cp\u003e2.1.1 Initial Analysis and Visualization 45\u003c\/p\u003e \u003cp\u003e2.1.2 Confidence Interval for Difference Between Means 47\u003c\/p\u003e \u003cp\u003e2.1.3 Reporting Results 52\u003c\/p\u003e \u003cp\u003e2.1.4 Hypothesis Test for Comparing Means 52\u003c\/p\u003e \u003cp\u003e2.1.5 Power and Sample Size for Tests of Difference of Means 56\u003c\/p\u003e \u003cp\u003e2.1.6 Considering Costs 60\u003c\/p\u003e \u003cp\u003eExercises 62\u003c\/p\u003e \u003cp\u003e2.2 Case: Email Response Test (Two-Sample Test of Proportions) 64\u003c\/p\u003e \u003cp\u003e2.2.1 Confidence Interval and Hypothesis Test for Comparing Two Proportions 66\u003c\/p\u003e \u003cp\u003e2.2.2 Better Confidence Intervals for Comparing Two Proportions 66\u003c\/p\u003e \u003cp\u003e2.2.3 Power and Sample Size for Tests of Difference of Two Proportions 68\u003c\/p\u003e \u003cp\u003eExercises 70\u003c\/p\u003e \u003cp\u003e2.3 Case: Comparing Landing Pages (Two-Sample Test of Means, Again) 71\u003c\/p\u003e \u003cp\u003eExercises 74\u003c\/p\u003e \u003cp\u003e2.4 Case: Display Ad Clickthrough Rate 75\u003c\/p\u003e \u003cp\u003e2.4.1 Beta-Binomial Model 75\u003c\/p\u003e \u003cp\u003e2.4.2 Comparing Two Proportions Using the Beta-Binomial Model 78\u003c\/p\u003e \u003cp\u003eExercises 80\u003c\/p\u003e \u003cp\u003e2.5 Case: Hotel Ad Test 81\u003c\/p\u003e \u003cp\u003e2.5.1 Tips on Presenting Experimental Findings 83\u003c\/p\u003e \u003cp\u003eExercises 84\u003c\/p\u003e \u003cp\u003e2.6 Chapter Exercises 84\u003c\/p\u003e \u003cp\u003e2.7 Learning More 86\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Designing A\/B Tests with Large Samples \u003c\/b\u003e\u003cb\u003e91\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 The Average Treatment Effect 92\u003c\/p\u003e \u003cp\u003eExercises 93\u003c\/p\u003e \u003cp\u003e3.2 Internal and External Validity 93\u003c\/p\u003e \u003cp\u003e3.2.1 Threats to Internal Validity 93\u003c\/p\u003e \u003cp\u003e3.2.2 Threats to External Validity 95\u003c\/p\u003e \u003cp\u003eExercises 96\u003c\/p\u003e \u003cp\u003e3.3 Designing Conclusive Experiments 96\u003c\/p\u003e \u003cp\u003eExercises 102\u003c\/p\u003e \u003cp\u003e3.4 The Lady Tasting Tea 103\u003c\/p\u003e \u003cp\u003eExercises 103\u003c\/p\u003e \u003cp\u003e3.5 Testing a New Checkout Button 104\u003c\/p\u003e \u003cp\u003eExercises 104\u003c\/p\u003e \u003cp\u003e3.6 Chapter Exercises 104\u003c\/p\u003e \u003cp\u003e3.7 Learning More 104\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Analyzing A\/B Tests: Advanced Techniques \u003c\/b\u003e\u003cb\u003e107\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Case: Audio\/Video Test Reprise (One-Sided Tests) 108\u003c\/p\u003e \u003cp\u003e4.1.1 One-Sided Confidence Intervals 109\u003c\/p\u003e \u003cp\u003e4.1.2 One-Sided Power 112\u003c\/p\u003e \u003cp\u003eExercises 113\u003c\/p\u003e \u003cp\u003e4.2 Case: Typing Test (Paired \u003ci\u003et\u003c\/i\u003e-Test) 114\u003c\/p\u003e \u003cp\u003e4.2.1 Matched Pairs 115\u003c\/p\u003e \u003cp\u003eExercises 119\u003c\/p\u003e \u003cp\u003e4.3 A\/B\/n Tests 121\u003c\/p\u003e \u003cp\u003eExercises 126\u003c\/p\u003e \u003cp\u003e4.4 Minimum Detectable Effect 126\u003c\/p\u003e \u003cp\u003eExercises 128\u003c\/p\u003e \u003cp\u003e4.5 Subgroup Analysis 129\u003c\/p\u003e \u003cp\u003e4.5.1 Deficiencies of Subgroup Analysis 132\u003c\/p\u003e \u003cp\u003e4.5.2 Subgroup Analysis of Bank Data 133\u003c\/p\u003e \u003cp\u003eExercises 135\u003c\/p\u003e \u003cp\u003e4.6 Simpson’s Paradox 136\u003c\/p\u003e \u003cp\u003e4.6.1 Sex Discrimination at UC Berkeley 137\u003c\/p\u003e \u003cp\u003e4.6.2 Do You Want Kidney Stone Treatment A or Treatment B? 138\u003c\/p\u003e \u003cp\u003e4.6.3 When the Subgroup Is Misleading 140\u003c\/p\u003e \u003cp\u003eExercises 143\u003c\/p\u003e \u003cp\u003e4.7 Test and Roll 143\u003c\/p\u003e \u003cp\u003eExercises 145\u003c\/p\u003e \u003cp\u003e4.8 Chapter Exercises 146\u003c\/p\u003e \u003cp\u003e4.9 Learning More 146\u003c\/p\u003e \u003cp\u003e4.10 Appendix on One-Sided CIs, Tests, and Sample Sizes 151\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Designing Tests with Small Samples \u003c\/b\u003e\u003cb\u003e159\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Case: Call Center Scripts (ANOVA) 160\u003c\/p\u003e \u003cp\u003e5.1.1 Blocking 161\u003c\/p\u003e \u003cp\u003eExercises 165\u003c\/p\u003e \u003cp\u003e5.2 Case: Facebook Geo-Testing (Latin Square Design) 166\u003c\/p\u003e \u003cp\u003e5.2.1 More on Latin Square Designs 169\u003c\/p\u003e \u003cp\u003e5.2.2 Latin Squares and Degrees of Freedom 172\u003c\/p\u003e \u003cp\u003eExercises 175\u003c\/p\u003e \u003cp\u003e5.3 Dealing with Covariate Imbalance 177\u003c\/p\u003e \u003cp\u003e5.3.1 Matching 179\u003c\/p\u003e \u003cp\u003e5.3.2 Rerandomization 180\u003c\/p\u003e \u003cp\u003e5.3.3 Propensity Score 181\u003c\/p\u003e \u003cp\u003e5.3.4 Optimal Matching 184\u003c\/p\u003e \u003cp\u003e5.3.5 Sophisticated Matching: Selling Slushies 184\u003c\/p\u003e \u003cp\u003eExercises 185\u003c\/p\u003e \u003cp\u003e5.4 Chapter Exercises 187\u003c\/p\u003e \u003cp\u003e5.5 Learning More 188\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Analyzing Designs via Regression \u003c\/b\u003e\u003cb\u003e193\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Experiments and Linear Regression 193\u003c\/p\u003e \u003cp\u003eExercises 198\u003c\/p\u003e \u003cp\u003e6.2 Dummies, Effect Coding, and Orthogonality 198\u003c\/p\u003e \u003cp\u003eExercises 203\u003c\/p\u003e \u003cp\u003e6.3 Case: Loan Experiment Revisited (Interactions) 203\u003c\/p\u003e \u003cp\u003e6.3.1 Interactions 203\u003c\/p\u003e \u003cp\u003e6.3.2 Loan Experiment 208\u003c\/p\u003e \u003cp\u003eExercises 215\u003c\/p\u003e \u003cp\u003e6.4 Case: Direct Mail (Three-Way Interactions) 215\u003c\/p\u003e \u003cp\u003eExercises 224\u003c\/p\u003e \u003cp\u003e6.5 Pretreatment Covariates in Regression 224\u003c\/p\u003e \u003cp\u003eExercises 225\u003c\/p\u003e \u003cp\u003e6.6 Chapter Exercises 226\u003c\/p\u003e \u003cp\u003e6.7 Learning More 228\u003c\/p\u003e \u003cp\u003e6.8 Appendix: The Covariance Matrix of the Regression Coefficients 233\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Two-Level Full Factorial Experiments \u003c\/b\u003e\u003cb\u003e237\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Case: The Postcard Example 238\u003c\/p\u003e \u003cp\u003eExercises 247\u003c\/p\u003e \u003cp\u003e7.2 Case: Email Campaign 247\u003c\/p\u003e \u003cp\u003eExercises 250\u003c\/p\u003e \u003cp\u003e7.3 The Determinant of a Matrix 252\u003c\/p\u003e \u003cp\u003eExercises 257\u003c\/p\u003e \u003cp\u003e7.4 Aliasing 258\u003c\/p\u003e \u003cp\u003eExercises 264\u003c\/p\u003e \u003cp\u003e7.5 Blocking (Again) 265\u003c\/p\u003e \u003cp\u003eExercises 269\u003c\/p\u003e \u003cp\u003e7.6 Mee’s Blunders 269\u003c\/p\u003e \u003cp\u003e7.7 Chapter Exercises 270\u003c\/p\u003e \u003cp\u003e7.8 Learning More 271\u003c\/p\u003e \u003cp\u003e7.9 Appendix on aliasMatrix and colorMap 273\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Two-Level Screening Designs \u003c\/b\u003e\u003cb\u003e279\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Preliminaries 280\u003c\/p\u003e \u003cp\u003eExercises 286\u003c\/p\u003e \u003cp\u003e8.2 Case: Puncture Resistance (Small Screening Experiment) 287\u003c\/p\u003e \u003cp\u003eExercises 288\u003c\/p\u003e \u003cp\u003e8.3 Case: College Giving (Big Screening Experiment) 289\u003c\/p\u003e \u003cp\u003eExercises 292\u003c\/p\u003e \u003cp\u003e8.4 How to Set Up a Screening Experiment 294\u003c\/p\u003e \u003cp\u003eExercises 295\u003c\/p\u003e \u003cp\u003e8.5 Creating a Screening Design 295\u003c\/p\u003e \u003cp\u003eExercises 298\u003c\/p\u003e \u003cp\u003e8.6 Chapter Exercises 299\u003c\/p\u003e \u003cp\u003e8.7 Learning More 300\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Custom Design of Experiments \u003c\/b\u003e\u003cb\u003e305\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Case: Selling Used Cars at Auction I (Small Custom Screening) 306\u003c\/p\u003e \u003cp\u003e9.1.1 Create the Design 307\u003c\/p\u003e \u003cp\u003e9.1.2 Evaluate the Design 312\u003c\/p\u003e \u003cp\u003e9.1.3 Use the Design 316\u003c\/p\u003e \u003cp\u003eExercises 319\u003c\/p\u003e \u003cp\u003e9.2 Case: Selling Used Cars at Auction II (Custom Experiment) 319\u003c\/p\u003e \u003cp\u003eExercises 322\u003c\/p\u003e \u003cp\u003e9.3 Custom Experiment with Blocking 322\u003c\/p\u003e \u003cp\u003eExercises 324\u003c\/p\u003e \u003cp\u003e9.4 Custom Screening Experiments 326\u003c\/p\u003e \u003cp\u003eExercises 331\u003c\/p\u003e \u003cp\u003e9.5 More Than Two Levels 332\u003c\/p\u003e \u003cp\u003eExercises 337\u003c\/p\u003e \u003cp\u003e9.6 Chapter Exercises 338\u003c\/p\u003e \u003cp\u003e9.7 Learning More 338\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Epilogue \u003c\/b\u003e\u003cb\u003e341\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 The Sequential Nature of Experimentation 342\u003c\/p\u003e \u003cp\u003e10.2 Approaches to Sequential Experimentation 345\u003c\/p\u003e \u003cp\u003eReferences 347\u003c\/p\u003e \u003cp\u003eIndex 357 \u003c\/p\u003e \u003cp\u003e\u003cb\u003eB. D. MCCULLOUGH, P\u003csmall\u003eH\u003c\/small\u003eD,\u003c\/b\u003e was a Professor in the Department of Decision Sciences \u0026amp; MIS, LeBow College of Business, Drexel University, Philadelphia, PA.\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e\u003cp\u003eA unique text that simplifies experimental business design and is dedicated to the R language\u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eBusiness Experiments with R\u003c\/i\u003e offers a guide to, and explores the fundamentals of experimental business designs. The book fills a gap in the literature to provide a text on the topic of business statistics that addresses issues such as small samples, lack of normality, and data confounding. The author—a noted expert on the topic—puts the focus on the A\/B tests (and their variants) that are widely used in industry, but not typically covered in business statistics textbooks. \u003c\/p\u003e\u003cp\u003eThe text contains the tools needed to design and analyze two-treatment experiments (i.e., A\/B tests) to answer business questions. The author highlights the strategic and technical issues involved in designing experiments that will truly affect organizations. The book then builds on the foundation in Part I and expands the multivariable testing. Since today’s companies are using experiments to solve a broad range of problems, \u003ci\u003eBusiness Experiments with R\u003c\/i\u003e is an essential resource for any business student. This important text: \u003c\/p\u003e\u003cli\u003e\u003cbl\u003ePresents the key ideas that business students need to know about experiments\u003c\/bl\u003e\u003c\/li\u003e \u003cli\u003e\u003cbl\u003eOffers a series of examples, focusing on a specific business question\u003c\/bl\u003e\u003c\/li\u003e \u003cli\u003e\u003cbl\u003eHelps develop the ability to frame ill-defined problems and determine what data and analysis would provide information about that problem\u003c\/bl\u003e\u003c\/li\u003e \u003cp\u003eWritten for students of general business, marketing, and business analytics, \u003ci\u003eBusiness Experiments with R\u003c\/i\u003e is an important text that helps to answer business questions by highlighting the strategic and technical issues involved in designing experiments that will truly affect organizations.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988871495909,"sku":"NP9781119689706","price":106.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119689706.jpg?v=1761781857","url":"https:\/\/k12savings.com\/es\/products\/business-experiments-with-r-isbn-9781119689706","provider":"K12savings","version":"1.0","type":"link"}