Data Science for Business and Decision Making covers both statistics and operations research while most competing textbooks focus on one or the other. As a result, the book more clearly defines the principles of business analytics for those who want to apply quantitative methods in their work. Its emphasis reflects the importance of regression, optimization and simulation for practitioners of business analytics. Each chapter uses a didactic format that is followed by exercises and answers. Freely-accessible datasets enable students and professionals to work with Excel, Stata Statistical Software®, and IBM SPSS Statistics Software®.
1. Chapter 1: Introduction to Data Analysis and Decision Making - p. vii 2. Chapter 2: Types of Variables and Measurement and Accuracy Scales - p. 5 3. Part II: Descriptive Statistics - p. 18 4. Chapter 3: Univariate Descriptive Statistics - p. 18 5. Abstract - p. 21 6. 3.1 Introduction - p. 21 7. 3.2 Frequency Distribution Table - p. 22 8. 3.3 Graphical Representation of the Results - p. 26 9. 3.4 The Most Common Summary-Measures in Univariate Descriptive Statistics - p. 38 10. 3.5 A Practical Example in Excel - p. 68 11. 3.6 A Practical Example on SPSS - p. 69 12. 3.7 A Practical Example on Stata - p. 83 13. 3.8 Final Remarks - p. 87 14. 3.9 Exercises - p. 87 15. Chapter 4: Bivariate Descriptive Statistics - p. 91 16. Part III: Probabilistic Statistics - p. 123 17. Chapter 5: Introduction to Probability - p. 123 18. Chapter 6: Random Variables and Probability Distributions - p. 136 19. Part IV: Statistical Inference - p. 165 20. Chapter 7: Sampling - p. 165 21. Chapter 8: Estimation - p. 188 22. Chapter 9: Hypotheses Tests - p. 197 23. Abstract - p. 199 24. 9.1 Introduction - p. 199 25. 9.2 Parametric Tests - p. 201 26. 9.3 Univariate Tests for Normality - p. 201 27. 9.4 Tests for the Homogeneity of Variances - p. 210 28. 9.5 Hypotheses Tests Regarding a Population Mean (μ) From One Random Sample - p. 219 29. 9.6 Student’s t-Test to Compare Two Population Means From Two Independent Random Samples - p. 223 30. 9.7 Student’s t-Test to Compare Two Population Means From Two Paired Random Samples - p. 227 31. 9.8 ANOVA to Compare the Means of More Than Two Populations - p. 232 32. 9.9 Final Remarks - p. 246 33. 9.10 Exercises - p. 247 34. Chapter 10: Nonparametric Tests - p. 248 35. Abstract - p. 249 36. 10.1 Introduction - p. 249 37. 10.2 Tests for One Sample - p. 250 38. 10.3 Tests for Two Paired Samples - p. 262 39. 10.4 Tests for Two Independent Samples - p. 276 40. 10.5 Tests for k Paired Samples - p. 286 41. 10.6 Tests for k Independent Samples - p. 295 42. 10.7 Final Remarks - p. 304 43. 10.8 Exercises - p. 304 44. Part V: Multivariate Exploratory Data Analysis - p. 305 45. Introduction - p. 305 46. Chapter 11: Cluster Analysis - p. 310 47. Abstract - p. 311 48. 11.1 Introduction - p. 311 49. 11.2 Cluster Analysis - p. 315 50. 11.3 Cluster Analysis with Hierarchical and Nonhierarchical Agglomeration Schedules in SPSS - p. 350 51. 11.4 Cluster Analysis With Hierarchical and Nonhierarchical Agglomeration Schedules in Stata - p. 368 52. 11.5 Final Remarks - p. 377 53. 11.6 Exercises - p. 377 54. Appendix - p. 379 55. Chapter 12: Principal Component Factor Analysis - p. 382 56. Abstract - p. 383 57. 12.1 Introduction - p. 383 58. 12.2 Principal Component Factor Analysis - p. 384 59. 12.3 Principal Component Factor Analysis in SPSS - p. 410 60. 12.4 Principal Component Factor Analysis in Stata - p. 421 61. 12.5 Final Remarks - p. 431 62. 12.6 Exercises - p. 431 63. Appendix: Cronbach’s Alpha - p. 434 64. Part VI: Generalized Linear Models - p. 438 65. Introduction - p. 439 66. Chapter 13: Simple and Multiple Regression Models - p. 441 67. Abstract - p. 443 68. 13.1 Introduction - p. 443 69. 13.2 Linear Regression Models - p. 443 70. 13.3 Presuppositions of Regression Models Estimated by OLS - p. 479 71. 13.4 Nonlinear Regression Models - p. 495 72. 13.5 Estimation of Regression Models in Stata - p. 498 73. 13.6 Estimation of Regression Models in SPSS - p. 516 74. 13.7 Final Remarks - p. 529 75. 13.8 Exercises - p. 529 76. Appendix: Quantile Regression Models - p. 532 77. Chapter 14: Binary and Multinomial Logistic Regression Models - p. 538 78. Abstract - p. 539 79. 14.1 Introduction - p. 539 80. 14.2 The Binary Logistic Regression Model - p. 540 81. 14.3 The Multinomial Logistic Regression Model - p. 563 82. 14.4 Estimation of Binary and Multinomial Logistic Regression Models in Stata - p. 575 83. 14.5 Estimation of Binary and Multinomial Logistic Regression Models in SPSS - p. 591 84. 14.6 Final Remarks - p. 606 85. 14.7 Exercises - p. 606 86. Appendix: Probit Regression Models - p. 608 87. Chapter 15: Regression Models for Count Data: Poisson and Negative Binomial - p. 615 88. Abstract - p. 617 89. 15.1 Introduction - p. 617 90. 15.2 The Poisson Regression Model - p. 618 91. 15.3 The Negative Binomial Regression Model - p. 633 92. 15.4 Estimating Regression Models for Count Data in Stata - p. 644 93. 15.5 Regression Model Estimation for Count Data in SPSS - p. 664 94. 15.6 Final Remarks - p. 685 95. 15.7 Exercises - p. 686 96. Appendix: Zero-Inflated Regression Models - p. 690 97. Part VII: Optimization Models and Simulation - p. 703 98. Chapter 16: Introduction to Optimization Models: General Formulations and Business Modeling - p. 703 99. Abstract - p. 707 100. 16.1 Introduction to Optimization Models - p. 707 101. 16.2 Introduction to Linear Programming Models - p. 709 102. 16.3 Mathematical Formulation of a General Linear Programming Model - p. 709 103. 16.4 Linear Programming Model in the Standard and Canonical Forms - p. 709 104. 16.5 Assumptions of the Linear Programming Model - p. 712 105. 16.6 Modeling Business Problems Using Linear Programming - p. 713 106. 16.7 Final Remarks - p. 736 107. 16.8 Exercises - p. 736 108. Chapter 17: Solution of Linear Programming Problems - p. 746 109. Abstract - p. 747 110. 17.1 Introduction - p. 747 111. 17.2 Graphical Solution of a Linear Programming Problem - p. 747 112. 17.3 Analytical Solution of a Linear Programming Problem in Which m < n - p. 755 113. 17.4 The Simplex Method - p. 757 114. 17.5 Solution by Using a Computer - p. 773 115. 17.6 Sensitivity Analysis - p. 807 116. 17.7 Exercises - p. 823 117. Chapter 18: Network Programming - p. 833 118. Chapter 19: Integer Programming - p. 886 119. Chapter 20: Simulation and Risk Analysis - p. 918 120. Part VIII: Other Topics - p. 931 121. Chapter 21: Design and Analysis of Experiments - p. 931 122. Chapter 22: Statistical Process Control - p. 939 123. Chapter 23: Data Mining and Multilevel Modeling - p. 981 124. Abstract - p. 983 125. 23.1 Introduction to Data Mining - p. 983 126. 23.2 Multilevel Modeling - p. 987 127. 23.3 Nested Data Structures - p. 988 128. 23.4 Hierarchical Linear Models - p. 991 129. 23.5 Estimation of Hierarchical Linear Models in Stata - p. 998 130. 23.6 Estimation of Hierarchical Linear Models in SPSS - p. 1033 131. 23.7 Final Remarks - p. 1045 132. 23.8 Exercises - p. 1047 133. Appendix - p. 1052 134. Answers - p. 1065 135. Answer Keys: Exercises: Chapter 2 - p. 1065 136. Answer Keys: Exercises: Chapter 3 - p. 1065 137. Answer Keys: Exercises: Chapter 4 - p. 1067 138. Answer Keys: Exercises: Chapter 5 - p. 1075 139. Answer Keys: Exercises: Chapter 6 - p. 1075 140. Answer Keys: Exercises: Chapter 7 - p. 1077 141. Answer Keys: Exercises: Chapter 8 - p. 1077 142. Answer Keys: Exercises: Chapter 9 - p. 1077 143. Answer Keys: Exercises: Chapter 10 - p. 1077 144. Answer Keys: Exercises: Chapter 11 - p. 1078 145. Answer Keys: Exercises: Chapter 12 - p. 1093 146. Answer Keys: Exercises: Chapter 13 - p. 1102 147. Answer Keys: Exercises: Chapter 14 - p. 1106 148. Answer Keys: Exercises: Chapter 15 - p. 1114 149. Answer Keys: Exercises: Chapter 16 - p. 1122 150. Answer Keys: Exercises: Chapter 17 - p. 1127 151. Answer Keys: Exercises: Chapter 18 - p. 1130 152. Answer Keys: Exercises: Chapter 19 - p. 1133 153. Answer Keys: Exercises: Chapter 20 - p. 1137 154. Answer Keys: Exercises: Chapter 21 - p. 1137 155. Answer Keys: Exercises: Chapter 22 - p. 1138 156. Answer Keys: Exercises: Chapter 23 - p. 1140 157. Appendices - p. 1157 158. References - p. 1195 159. Index - p. 1215
Data Science for Business and Decision Making covers both statistics and operations research while most competing textbooks focus on one or the other. As a result, the book more clearly defines the principles of business analytics for those who want to apply quantitative methods in their work. Its emphasis reflects the importance of regression, optimization and simulation for practitioners of business analytics. Each chapter uses a didactic format that is followed by exercises and answers. Freely-accessible datasets enable students and professionals to work with Excel, Stata Statistical Software®, and IBM SPSS Statistics Software®.
1. Chapter 1: Introduction to Data Analysis and Decision Making - p. vii 2. Chapter 2: Types of Variables and Measurement and Accuracy Scales - p. 5 3. Part II: Descriptive Statistics - p. 18 4. Chapter 3: Univariate Descriptive Statistics - p. 18 5. Abstract - p. 21 6. 3.1 Introduction - p. 21 7. 3.2 Frequency Distribution Table - p. 22 8. 3.3 Graphical Representation of the Results - p. 26 9. 3.4 The Most Common Summary-Measures in Univariate Descriptive Statistics - p. 38 10. 3.5 A Practical Example in Excel - p. 68 11. 3.6 A Practical Example on SPSS - p. 69 12. 3.7 A Practical Example on Stata - p. 83 13. 3.8 Final Remarks - p. 87 14. 3.9 Exercises - p. 87 15. Chapter 4: Bivariate Descriptive Statistics - p. 91 16. Part III: Probabilistic Statistics - p. 123 17. Chapter 5: Introduction to Probability - p. 123 18. Chapter 6: Random Variables and Probability Distributions - p. 136 19. Part IV: Statistical Inference - p. 165 20. Chapter 7: Sampling - p. 165 21. Chapter 8: Estimation - p. 188 22. Chapter 9: Hypotheses Tests - p. 197 23. Abstract - p. 199 24. 9.1 Introduction - p. 199 25. 9.2 Parametric Tests - p. 201 26. 9.3 Univariate Tests for Normality - p. 201 27. 9.4 Tests for the Homogeneity of Variances - p. 210 28. 9.5 Hypotheses Tests Regarding a Population Mean (μ) From One Random Sample - p. 219 29. 9.6 Student’s t-Test to Compare Two Population Means From Two Independent Random Samples - p. 223 30. 9.7 Student’s t-Test to Compare Two Population Means From Two Paired Random Samples - p. 227 31. 9.8 ANOVA to Compare the Means of More Than Two Populations - p. 232 32. 9.9 Final Remarks - p. 246 33. 9.10 Exercises - p. 247 34. Chapter 10: Nonparametric Tests - p. 248 35. Abstract - p. 249 36. 10.1 Introduction - p. 249 37. 10.2 Tests for One Sample - p. 250 38. 10.3 Tests for Two Paired Samples - p. 262 39. 10.4 Tests for Two Independent Samples - p. 276 40. 10.5 Tests for k Paired Samples - p. 286 41. 10.6 Tests for k Independent Samples - p. 295 42. 10.7 Final Remarks - p. 304 43. 10.8 Exercises - p. 304 44. Part V: Multivariate Exploratory Data Analysis - p. 305 45. Introduction - p. 305 46. Chapter 11: Cluster Analysis - p. 310 47. Abstract - p. 311 48. 11.1 Introduction - p. 311 49. 11.2 Cluster Analysis - p. 315 50. 11.3 Cluster Analysis with Hierarchical and Nonhierarchical Agglomeration Schedules in SPSS - p. 350 51. 11.4 Cluster Analysis With Hierarchical and Nonhierarchical Agglomeration Schedules in Stata - p. 368 52. 11.5 Final Remarks - p. 377 53. 11.6 Exercises - p. 377 54. Appendix - p. 379 55. Chapter 12: Principal Component Factor Analysis - p. 382 56. Abstract - p. 383 57. 12.1 Introduction - p. 383 58. 12.2 Principal Component Factor Analysis - p. 384 59. 12.3 Principal Component Factor Analysis in SPSS - p. 410 60. 12.4 Principal Component Factor Analysis in Stata - p. 421 61. 12.5 Final Remarks - p. 431 62. 12.6 Exercises - p. 431 63. Appendix: Cronbach’s Alpha - p. 434 64. Part VI: Generalized Linear Models - p. 438 65. Introduction - p. 439 66. Chapter 13: Simple and Multiple Regression Models - p. 441 67. Abstract - p. 443 68. 13.1 Introduction - p. 443 69. 13.2 Linear Regression Models - p. 443 70. 13.3 Presuppositions of Regression Models Estimated by OLS - p. 479 71. 13.4 Nonlinear Regression Models - p. 495 72. 13.5 Estimation of Regression Models in Stata - p. 498 73. 13.6 Estimation of Regression Models in SPSS - p. 516 74. 13.7 Final Remarks - p. 529 75. 13.8 Exercises - p. 529 76. Appendix: Quantile Regression Models - p. 532 77. Chapter 14: Binary and Multinomial Logistic Regression Models - p. 538 78. Abstract - p. 539 79. 14.1 Introduction - p. 539 80. 14.2 The Binary Logistic Regression Model - p. 540 81. 14.3 The Multinomial Logistic Regression Model - p. 563 82. 14.4 Estimation of Binary and Multinomial Logistic Regression Models in Stata - p. 575 83. 14.5 Estimation of Binary and Multinomial Logistic Regression Models in SPSS - p. 591 84. 14.6 Final Remarks - p. 606 85. 14.7 Exercises - p. 606 86. Appendix: Probit Regression Models - p. 608 87. Chapter 15: Regression Models for Count Data: Poisson and Negative Binomial - p. 615 88. Abstract - p. 617 89. 15.1 Introduction - p. 617 90. 15.2 The Poisson Regression Model - p. 618 91. 15.3 The Negative Binomial Regression Model - p. 633 92. 15.4 Estimating Regression Models for Count Data in Stata - p. 644 93. 15.5 Regression Model Estimation for Count Data in SPSS - p. 664 94. 15.6 Final Remarks - p. 685 95. 15.7 Exercises - p. 686 96. Appendix: Zero-Inflated Regression Models - p. 690 97. Part VII: Optimization Models and Simulation - p. 703 98. Chapter 16: Introduction to Optimization Models: General Formulations and Business Modeling - p. 703 99. Abstract - p. 707 100. 16.1 Introduction to Optimization Models - p. 707 101. 16.2 Introduction to Linear Programming Models - p. 709 102. 16.3 Mathematical Formulation of a General Linear Programming Model - p. 709 103. 16.4 Linear Programming Model in the Standard and Canonical Forms - p. 709 104. 16.5 Assumptions of the Linear Programming Model - p. 712 105. 16.6 Modeling Business Problems Using Linear Programming - p. 713 106. 16.7 Final Remarks - p. 736 107. 16.8 Exercises - p. 736 108. Chapter 17: Solution of Linear Programming Problems - p. 746 109. Abstract - p. 747 110. 17.1 Introduction - p. 747 111. 17.2 Graphical Solution of a Linear Programming Problem - p. 747 112. 17.3 Analytical Solution of a Linear Programming Problem in Which m < n - p. 755 113. 17.4 The Simplex Method - p. 757 114. 17.5 Solution by Using a Computer - p. 773 115. 17.6 Sensitivity Analysis - p. 807 116. 17.7 Exercises - p. 823 117. Chapter 18: Network Programming - p. 833 118. Chapter 19: Integer Programming - p. 886 119. Chapter 20: Simulation and Risk Analysis - p. 918 120. Part VIII: Other Topics - p. 931 121. Chapter 21: Design and Analysis of Experiments - p. 931 122. Chapter 22: Statistical Process Control - p. 939 123. Chapter 23: Data Mining and Multilevel Modeling - p. 981 124. Abstract - p. 983 125. 23.1 Introduction to Data Mining - p. 983 126. 23.2 Multilevel Modeling - p. 987 127. 23.3 Nested Data Structures - p. 988 128. 23.4 Hierarchical Linear Models - p. 991 129. 23.5 Estimation of Hierarchical Linear Models in Stata - p. 998 130. 23.6 Estimation of Hierarchical Linear Models in SPSS - p. 1033 131. 23.7 Final Remarks - p. 1045 132. 23.8 Exercises - p. 1047 133. Appendix - p. 1052 134. Answers - p. 1065 135. Answer Keys: Exercises: Chapter 2 - p. 1065 136. Answer Keys: Exercises: Chapter 3 - p. 1065 137. Answer Keys: Exercises: Chapter 4 - p. 1067 138. Answer Keys: Exercises: Chapter 5 - p. 1075 139. Answer Keys: Exercises: Chapter 6 - p. 1075 140. Answer Keys: Exercises: Chapter 7 - p. 1077 141. Answer Keys: Exercises: Chapter 8 - p. 1077 142. Answer Keys: Exercises: Chapter 9 - p. 1077 143. Answer Keys: Exercises: Chapter 10 - p. 1077 144. Answer Keys: Exercises: Chapter 11 - p. 1078 145. Answer Keys: Exercises: Chapter 12 - p. 1093 146. Answer Keys: Exercises: Chapter 13 - p. 1102 147. Answer Keys: Exercises: Chapter 14 - p. 1106 148. Answer Keys: Exercises: Chapter 15 - p. 1114 149. Answer Keys: Exercises: Chapter 16 - p. 1122 150. Answer Keys: Exercises: Chapter 17 - p. 1127 151. Answer Keys: Exercises: Chapter 18 - p. 1130 152. Answer Keys: Exercises: Chapter 19 - p. 1133 153. Answer Keys: Exercises: Chapter 20 - p. 1137 154. Answer Keys: Exercises: Chapter 21 - p. 1137 155. Answer Keys: Exercises: Chapter 22 - p. 1138 156. Answer Keys: Exercises: Chapter 23 - p. 1140 157. Appendices - p. 1157 158. References - p. 1195 159. Index - p. 1215
