{"product_id":"illuminating-statistical-analysis-using-scenarios-and-simulations-isbn-9781119296331","title":"Illuminating Statistical Analysis Using Scenarios and Simulations","description":"\u003cp\u003e \u003cb\u003eFeatures an integrated approach of statistical scenarios and simulations to aid readers in developing key intuitions needed to understand the wide ranging concepts and methods of statistics and inference\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eIlluminating Statistical Analysis Using Scenarios and Simulations \u003c\/i\u003epresents the basic concepts of statistics and statistical inference using the dual mechanisms of scenarios and simulations. This approach helps readers develop key intuitions and deep understandings of statistical analysis. Scenario-specific sampling simulations depict the results that would be obtained by a very large number of individuals investigating the same scenario, each with their own evidence, while graphical depictions of the simulation results present clear and direct pathways to intuitive methods for statistical inference. These intuitive methods can then be easily linked to traditional formulaic methods, and the author does not simply explain the linkages, but rather provides demonstrations throughout for a broad range of statistical phenomena. In addition, induction and deduction are repeatedly interwoven, which fosters a natural \"need to know basis\" for ordering the topic coverage.\u003c\/p\u003e \u003cp\u003eExamining computer simulation results is central to the discussion and provides an illustrative way to (re)discover the properties of sample statistics, the role of chance, and to (re)invent corresponding principles of statistical inference. In addition, the simulation results foreshadow the various mathematical formulas that underlie statistical analysis.\u003c\/p\u003e \u003cp\u003eIn addition, this book:\u003c\/p\u003e \u003cp\u003e• Features both an intuitive and analytical perspective and includes a broad introduction to the use of Monte Carlo simulation and formulaic methods for statistical analysis\u003c\/p\u003e \u003cp\u003e• Presents straight-forward coverage of the essentials of basic statistics and ensures proper understanding of key concepts such as sampling distributions, the effects of sample size and variance on uncertainty, analysis of proportion, mean and rank differences, covariance, correlation, and regression\u003c\/p\u003e \u003cp\u003e• Introduces advanced topics such as Bayesian statistics, data mining, model cross-validation, robust regression, and resampling\u003c\/p\u003e \u003cp\u003e• Contains numerous example problems in each chapter with detailed solutions as well as an appendix that serves as a manual for constructing simulations quickly and easily using Microsoft® Office Excel®\u003c\/p\u003e \u003cp\u003e\u003ci\u003eIlluminating Statistical Analysis Using Scenarios and Simulations \u003c\/i\u003eis an ideal textbook for courses, seminars, and workshops in statistics and statistical inference and is appropriate for self-study as well. The book also serves as a thought-provoking treatise for researchers, scientists, managers, technicians, and others with a keen interest in statistical analysis.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eJeffrey E. Kottemann, Ph.D., \u003c\/b\u003eis Professor in the Perdue School at Salisbury University. Dr. Kottemann has published articles in a wide variety of academic research journals in the fields of business administration, computer science, decision sciences, economics, engineering, information systems, psychology, and public administration. He received his Ph.D. in Systems and Quantitative Methods from the University of Arizona.\u003c\/p\u003e \u003cp\u003ePreface ix\u003c\/p\u003e \u003cp\u003eAcknowledgements xi\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Sample Proportions and the Normal Distribution 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1 Evidence and Verdicts 3\u003c\/p\u003e \u003cp\u003e2 Judging Coins I 5\u003c\/p\u003e \u003cp\u003e3 Brief on Bell Shapes 9\u003c\/p\u003e \u003cp\u003e4 Judging Coins II 11\u003c\/p\u003e \u003cp\u003e5 Amount of Evidence I 19\u003c\/p\u003e \u003cp\u003e6 Variance of Evidence I 23\u003c\/p\u003e \u003cp\u003e7 Judging Opinion Splits I 27\u003c\/p\u003e \u003cp\u003e8 Amount of Evidence II 31\u003c\/p\u003e \u003cp\u003e9 Variance of Evidence II 35\u003c\/p\u003e \u003cp\u003e10 Judging Opinion Splits II 39\u003c\/p\u003e \u003cp\u003e11 It Has Been the Normal Distribution All Along 45\u003c\/p\u003e \u003cp\u003e12 Judging Opinion Split Differences 49\u003c\/p\u003e \u003cp\u003e13 Rescaling to Standard Errors 53\u003c\/p\u003e \u003cp\u003e14 The Standardized Normal Distribution Histogram 55\u003c\/p\u003e \u003cp\u003e15 The z-Distribution 59\u003c\/p\u003e \u003cp\u003e16 Brief on Two-Tail Versus One-Tail 65\u003c\/p\u003e \u003cp\u003e17 Brief on Type I Versus Type II Errors 69\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Sample Means and the Normal Distribution 75\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18 Scaled Data and Sample Means 77\u003c\/p\u003e \u003cp\u003e19 Distribution of Random Sample Means 79\u003c\/p\u003e \u003cp\u003e20 Amount of Evidence 81\u003c\/p\u003e \u003cp\u003e21 Variance of Evidence 83\u003c\/p\u003e \u003cp\u003e22 Homing in on the Population Mean I 87\u003c\/p\u003e \u003cp\u003e23 Homing in on the Population Mean II 91\u003c\/p\u003e \u003cp\u003e24 Homing in on the Population Mean III 93\u003c\/p\u003e \u003cp\u003e25 Judging Mean Differences 95\u003c\/p\u003e \u003cp\u003e26 Sample Size, Variance, and Uncertainty 99\u003c\/p\u003e \u003cp\u003e27 The t-Distribution 105\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Multiple Proportions and Means: The X2- and \u003c\/b\u003e\u003cb\u003eF-Distributions 111\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e28 Multiple Proportions and the X2-Distribution 113\u003c\/p\u003e \u003cp\u003e29 Facing Degrees of Freedom 119\u003c\/p\u003e \u003cp\u003e30 Multiple Proportions: Goodness of Fit 121\u003c\/p\u003e \u003cp\u003e31 Two-Way Proportions: Homogeneity 125\u003c\/p\u003e \u003cp\u003e32 Two-Way Proportions: Independence 127\u003c\/p\u003e \u003cp\u003e33 Variance Ratios and the F-Distribution 131\u003c\/p\u003e \u003cp\u003e34 Multiple Means and Variance Ratios: ANOVA 137\u003c\/p\u003e \u003cp\u003e35 Two-Way Means and Variance Ratios: ANOVA 143\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV Linear Associations: Covariance, Correlation, and \u003c\/b\u003e\u003cb\u003eRegression 147\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e36 Covariance 149\u003c\/p\u003e \u003cp\u003e37 Correlation 153\u003c\/p\u003e \u003cp\u003e38 What Correlations Happen Just by Chance? 155\u003c\/p\u003e \u003cp\u003e39 Judging Correlation Differences 161\u003c\/p\u003e \u003cp\u003e40 Correlation with Mixed Data Types 165\u003c\/p\u003e \u003cp\u003e41 A Simple Regression Prediction Model 167\u003c\/p\u003e \u003cp\u003e42 Using Binomials Too 171\u003c\/p\u003e \u003cp\u003e43 A Multiple Regression Prediction Model 175\u003c\/p\u003e \u003cp\u003e44 Loose End I (Collinearity) 179\u003c\/p\u003e \u003cp\u003e45 Loose End II (Squaring R) 183\u003c\/p\u003e \u003cp\u003e46 Loose End III (Adjusting R-Squared) 185\u003c\/p\u003e \u003cp\u003e47 Reality Strikes 187\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart V Dealing with Unruly Scaled Data 193\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e48 Obstacles and Maneuvers 195\u003c\/p\u003e \u003cp\u003e49 Ordered Ranking Maneuver 199\u003c\/p\u003e \u003cp\u003e50 What Rank Sums Happen Just by Chance? 201\u003c\/p\u003e \u003cp\u003e51 Judging Rank Sum Differences 203\u003c\/p\u003e \u003cp\u003e52 Other Methods Using Ranks 205\u003c\/p\u003e \u003cp\u003e53 Transforming the Scale of Scaled Data 207\u003c\/p\u003e \u003cp\u003e54 Brief on Robust Regression 209\u003c\/p\u003e \u003cp\u003e55 Brief on Simulation and Resampling 211\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart VI Review and Additional Concepts 213\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e56 For Part I 215\u003c\/p\u003e \u003cp\u003e57 For Part II 221\u003c\/p\u003e \u003cp\u003e58 For Part III 227\u003c\/p\u003e \u003cp\u003e59 For Part IV 233\u003c\/p\u003e \u003cp\u003e60 For Part V 243\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendices 247\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA Data Types and Some Basic Statistics 249\u003c\/p\u003e \u003cp\u003eB Simulating Statistical Scenarios 253\u003c\/p\u003e \u003cp\u003eC Standard Error as Standard Deviation 271\u003c\/p\u003e \u003cp\u003eD Data Excerpt 273\u003c\/p\u003e \u003cp\u003eE Repeated Measures 277\u003c\/p\u003e \u003cp\u003eF Bayesian Statistics 281\u003c\/p\u003e \u003cp\u003eG Data Mining 287\u003c\/p\u003e \u003cp\u003eIndex 295\u003c\/p\u003e \u003cp\u003e\u003cb\u003eJeffrey E. Kottemann, Ph.D., \u003c\/b\u003eis Professor in the Perdue School at Salisbury University. Dr. Kottemann has published articles in a wide variety of academic research journals in the fields of business administration, computer science, decision sciences, economics, engineering, information systems, psychology, and public administration. He received his Ph.D. in Systems and Quantitative Methods from the University of Arizona.\u003c\/p\u003e \u003cp\u003e  \u003c\/p\u003e \u003cp\u003e \u003cb\u003eFeatures an integrated approach of statistical scenarios and simulations to aid readers in developing key intuitions needed to understand the wide ranging concepts and methods of statistics and inference\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eIlluminating Statistical Analysis Using Scenarios and Simulations \u003c\/i\u003epresents the basic concepts of statistics and statistical inference using the dual mechanisms of scenarios and simulations. This approach helps readers develop key intuitions and deep understandings of statistical analysis. Scenario-specific sampling simulations depict the results that would be obtained by a very large number of individuals investigating the same scenario, each with their own evidence, while graphical depictions of the simulation results present clear and direct pathways to intuitive methods for statistical inference. These intuitive methods can then be easily linked to traditional formulaic methods, and the author does not simply explain the linkages, but rather provides demonstrations throughout for a broad range of statistical phenomena. In addition, induction and deduction are repeatedly interwoven, which fosters a natural \"need to know basis\" for ordering the topic coverage.\u003c\/p\u003e \u003cp\u003eExamining computer simulation results is central to the discussion and provides an illustrative way to (re)discover the properties of sample statistics, the role of chance, and to (re)invent corresponding principles of statistical inference. In addition, the simulation results foreshadow the various mathematical formulas that underlie statistical analysis.\u003c\/p\u003e \u003cp\u003eIn addition, this book:\u003c\/p\u003e \u003cp\u003e• Features both an intuitive and analytical perspective and includes a broad introduction to the use of Monte Carlo simulation and formulaic methods for statistical analysis\u003c\/p\u003e \u003cp\u003e• Presents straight-forward coverage of the essentials of basic statistics and ensures proper understanding of key concepts such as sampling distributions, the effects of sample size and variance on uncertainty, analysis of proportion, mean and rank differences, covariance, correlation, and regression\u003c\/p\u003e \u003cp\u003e• Introduces advanced topics such as Bayesian statistics, data mining, model cross-validation, robust regression, and resampling\u003c\/p\u003e \u003cp\u003e• Contains numerous example problems in each chapter with detailed solutions as well as an appendix that serves as a manual for constructing simulations quickly and easily using Microsoft® Office Excel®\u003c\/p\u003e \u003cp\u003e\u003ci\u003eIlluminating Statistical Analysis Using Scenarios and Simulations \u003c\/i\u003eis an ideal textbook for courses, seminars, and workshops in statistics and statistical inference and is appropriate for self-study as well. The book also serves as a thought-provoking treatise for researchers, scientists, managers, technicians, and others with a keen interest in statistical analysis.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eJeffrey E. Kottemann, Ph.D., \u003c\/b\u003eis Professor in the Perdue School at Salisbury University. Dr. Kottemann has published articles in a wide variety of academic research journals in the fields of business administration, computer science, decision sciences, economics, engineering, information systems, psychology, and public administration. He received his Ph.D. in Systems and Quantitative Methods from the University of Arizona.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989400699109,"sku":"NP9781119296331","price":128.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119296331.jpg?v=1761783966","url":"https:\/\/k12savings.com\/es\/products\/illuminating-statistical-analysis-using-scenarios-and-simulations-isbn-9781119296331","provider":"K12savings","version":"1.0","type":"link"}