About The Authors |
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xix | |
Preface |
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xxiii | |
Chapter 1 Introduction |
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2 | (16) |
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4 | (1) |
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1.2 Business Analytics Defined |
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5 | (1) |
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1.3 A Categorization of Analytical Methods and Models |
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6 | (1) |
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6 | (1) |
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6 | (1) |
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7 | (1) |
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7 | (4) |
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9 | (1) |
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9 | (1) |
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9 | (1) |
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9 | (2) |
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1.5 Business Analytics in Practice |
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11 | (3) |
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11 | (1) |
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Human Resource (HR) Analytics |
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12 | (1) |
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12 | (1) |
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12 | (1) |
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13 | (1) |
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Analytics for Government and Nonprofits |
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13 | (1) |
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13 | (1) |
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14 | (1) |
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14 | (1) |
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15 | (3) |
Chapter 2 Descriptive Statistics |
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18 | (64) |
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Analytics in Action: U.S. Census Bureau |
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19 | (1) |
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2.1 Overview of Using Data: Definitions and Goals |
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19 | (2) |
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21 | (3) |
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Population and Sample Data |
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21 | (1) |
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Quantitative and Categorical Data |
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21 | (1) |
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Cross-Sectional and Time Series Data |
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21 | (1) |
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21 | (3) |
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2.3 Modifying Data in Excel |
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24 | (5) |
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Sorting and Filtering Data in Excel |
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24 | (3) |
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Conditional Formatting of Data in Excel |
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27 | (2) |
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2.4 Creating Distributions from Data |
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29 | (10) |
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Frequency Distributions for Categorical Data |
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29 | (1) |
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Relative Frequency and Percent Frequency Distributions |
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30 | (1) |
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Frequency Distributions for Quantitative Data |
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31 | (3) |
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34 | (3) |
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37 | (2) |
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39 | (5) |
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39 | (1) |
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40 | (1) |
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41 | (1) |
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41 | (3) |
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2.6 Measures of Variability |
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44 | (3) |
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44 | (1) |
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45 | (1) |
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46 | (1) |
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47 | (1) |
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2.7 Analyzing Distributions |
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47 | (8) |
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48 | (1) |
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49 | (1) |
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49 | (1) |
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50 | (2) |
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52 | (1) |
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52 | (3) |
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2.8 Measures of Association Between Two Variables |
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55 | (6) |
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55 | (2) |
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57 | (3) |
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60 | (1) |
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61 | (7) |
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61 | (2) |
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63 | (2) |
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Identification of Erroneous Outliers and Other Erroneous Values |
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65 | (2) |
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67 | (1) |
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68 | (1) |
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69 | (2) |
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71 | (8) |
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Case Problem: Heavenly Chocolates Web Site Transactions |
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79 | (3) |
Chapter 3 Data Visualization |
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82 | (56) |
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Analytics in Action: Cincinnati Zoo & Botanical Garden |
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83 | (2) |
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3.1 Overview of Data Visualization |
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85 | (3) |
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Effective Design Techniques |
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85 | (3) |
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88 | (11) |
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89 | (1) |
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90 | (3) |
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93 | (4) |
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Recommended PivotTables in Excel |
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97 | (2) |
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99 | (18) |
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99 | (2) |
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Recommended Charts in Excel |
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101 | (1) |
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102 | (4) |
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Bar Charts and Column Charts |
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106 | (1) |
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A Note on Pie Charts and Three-Dimensional Charts |
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107 | (2) |
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109 | (1) |
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110 | (2) |
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Additional Charts for Multiple Variables |
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112 | (3) |
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115 | (2) |
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3.4 Advanced Data Visualization |
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117 | (5) |
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117 | (3) |
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Geographic Information Systems Charts |
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120 | (2) |
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122 | (3) |
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Principles of Effective Data Dashboards |
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123 | (1) |
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Applications of Data Dashboards |
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123 | (2) |
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125 | (1) |
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125 | (1) |
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126 | (10) |
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Case Problem: All-Time Movie Box-Office Data |
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136 | (2) |
Chapter 4 Descriptive Data Mining |
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138 | (28) |
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Analytics in Action: Advice from a Machine |
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139 | (1) |
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140 | (8) |
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Measuring Similarity Between Observations |
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140 | (3) |
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143 | (3) |
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146 | (1) |
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Hierarchical Clustering versus k-Means Clustering |
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147 | (1) |
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148 | (3) |
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Evaluating Association Rules |
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150 | (1) |
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151 | (4) |
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Voice of the Customer at Triad Airline |
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151 | (2) |
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Preprocessing Text Data for Analysis |
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153 | (1) |
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154 | (1) |
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155 | (1) |
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155 | (1) |
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156 | (8) |
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Case Problem: Know Thy Customer |
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164 | (2) |
Chapter 5 Probability: An Introduction to Modeling Uncertainty |
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166 | (54) |
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Analytics in Action: National Aeronautics and Space Administration |
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167 | (1) |
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5.1 Events and Probabilities |
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168 | (1) |
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5.2 Some Basic Relationships of Probability |
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169 | (3) |
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169 | (1) |
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170 | (2) |
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5.3 Conditional Probability |
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172 | (8) |
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177 | (1) |
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177 | (1) |
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178 | (2) |
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180 | (2) |
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Discrete Random Variables |
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180 | (1) |
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Continuous Random Variables |
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181 | (1) |
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5.5 Discrete Probability Distributions |
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182 | (12) |
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Custom Discrete Probability Distribution |
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182 | (2) |
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Expected Value and Variance |
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184 | (3) |
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Discrete Uniform Probability Distribution |
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187 | (1) |
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Binomial Probability Distribution |
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188 | (3) |
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Poisson Probability Distribution |
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191 | (3) |
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5.6 Continuous Probability Distributions |
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194 | (13) |
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Uniform Probability Distribution |
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194 | (2) |
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Triangular Probability Distribution |
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196 | (2) |
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Normal Probability Distribution |
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198 | (5) |
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Exponential Probability Distribution |
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203 | (4) |
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207 | (1) |
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207 | (2) |
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209 | (9) |
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Case Problem: Hamilton County Judges |
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218 | (2) |
Chapter 6 Statistical Inference |
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220 | (74) |
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Analytics in Action: John Morrell & Company |
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221 | (2) |
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223 | (4) |
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Sampling from a Finite Population |
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223 | (1) |
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Sampling from an Infinite Population |
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224 | (3) |
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227 | (2) |
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229 | (1) |
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6.3 Sampling Distributions |
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229 | (11) |
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Sampling Distribution of x |
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232 | (5) |
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Sampling Distribution of To |
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237 | (3) |
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240 | (10) |
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Interval Estimation of the Population Mean |
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240 | (7) |
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Interval Estimation of the Population Proportion |
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247 | (3) |
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250 | (18) |
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Developing Null and Alternative Hypotheses |
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250 | (3) |
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Type I and Type II Errors |
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253 | (1) |
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Hypothesis Test of the Population Mean |
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254 | (11) |
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Hypothesis Test of the Population Proportion |
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265 | (3) |
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6.6 Big Data, Statistical Inference, and Practical Significance |
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268 | (10) |
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268 | (1) |
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269 | (1) |
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270 | (1) |
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Understanding What Big Data Is |
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271 | (1) |
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Big Data and Sampling Error |
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272 | (1) |
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Big Data and the Precision of Confidence Intervals |
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273 | (1) |
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Implications of Big Data for Confidence Intervals |
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274 | (1) |
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Big Data, Hypothesis Testing, and p Values |
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275 | (2) |
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Implications of Big Data in Hypothesis Testing |
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277 | (1) |
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278 | (1) |
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279 | (2) |
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281 | (10) |
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Case Problem 1: Young Professional Magazine |
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291 | (1) |
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Case Problem 2: Quality Associates, Inc |
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292 | (2) |
Chapter 7 Linear Regression |
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294 | (78) |
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Analytics in Action: Alliance Data Systems |
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295 | (1) |
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7.1 Simple Linear Regression Model |
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296 | (2) |
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296 | (1) |
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Estimated Regression Equation |
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296 | (2) |
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298 | (6) |
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Least Squares Estimates of the Regression Parameters |
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300 | (2) |
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Using Excel's Chart Tools to Compute the Estimated Regression Equation |
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302 | (2) |
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7.3 Assessing the Fit of the Simple Linear Regression Model |
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304 | (4) |
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304 | (2) |
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The Coefficient of Determination |
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306 | (1) |
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Using Excel's Chart Tools to Compute the Coefficient of Determination |
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307 | (1) |
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7.4 The Multiple Regression Model |
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308 | (5) |
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308 | (1) |
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Estimated Multiple Regression Equation |
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308 | (1) |
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Least Squares Method and Multiple Regression |
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309 | (1) |
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Butler Trucking Company and Multiple Regression |
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310 | (1) |
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Using Excel's Regression Tool to Develop the Estimated Multiple Regression Equation |
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310 | (3) |
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7.5 Inference and Regression |
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313 | (12) |
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Conditions Necessary for Valid Inference in the Least Squares Regression Model |
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314 | (4) |
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Testing Individual Regression Parameters |
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318 | (3) |
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Addressing Nonsignificant Independent Variables |
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321 | (1) |
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322 | (3) |
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7.6 Categorical Independent Variables |
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325 | (5) |
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Butler Trucking Company and Rush Hour |
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325 | (2) |
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Interpreting the Parameters |
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327 | (1) |
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More Complex Categorical Variables |
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328 | (2) |
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7.7 Modeling Nonlinear Relationships |
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330 | (12) |
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Quadratic Regression Models |
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331 | (4) |
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Piecewise Linear Regression Models |
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335 | (2) |
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Interaction Between Independent Variables |
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337 | (5) |
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342 | (2) |
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Variable Selection Procedures |
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342 | (1) |
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343 | (1) |
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7.9 Big Data and Regression |
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344 | (5) |
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Inference and Very Large Samples |
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344 | (4) |
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348 | (1) |
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7.10 Prediction with Regression |
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349 | (2) |
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351 | (1) |
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352 | (2) |
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354 | (15) |
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Case Problem: Alumni Giving |
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369 | (3) |
Chapter 8 Time Series Analysis and Forecasting |
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372 | (50) |
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Analytics in Action: ACCO Brands |
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373 | (2) |
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375 | (7) |
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375 | (2) |
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377 | (1) |
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378 | (1) |
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Trend and Seasonal Pattern |
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379 | (3) |
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382 | (1) |
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Identifying Time Series Patterns |
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382 | (1) |
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382 | (4) |
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8.3 Moving Averages and Exponential Smoothing |
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386 | (9) |
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387 | (4) |
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391 | (4) |
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8.4 Using Regression Analysis for Forecasting |
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395 | (10) |
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395 | (2) |
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Seasonality Without Trend |
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397 | (1) |
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398 | (3) |
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Using Regression Analysis as a Causal Forecasting Method |
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401 | (3) |
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Combining Causal Variables with Trend and Seasonality Effects |
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404 | (1) |
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Considerations in Using Regression in Forecasting |
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405 | (1) |
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8.5 Determining the Best Forecasting Model to Use |
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405 | (1) |
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406 | (1) |
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406 | (1) |
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407 | (8) |
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Case Problem: Forecasting Food and Beverage Sales |
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415 | (7) |
Chapter 9 Predictive Data Mining |
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422 | (42) |
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Analytics in Action: Orbitz |
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423 | (1) |
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9.1 Data Sampling, Preparation, and Partitioning |
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424 | (1) |
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425 | (7) |
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Evaluating the Classification of Categorical Outcomes |
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425 | (6) |
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Evaluating the Estimation of Continuous Outcomes |
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431 | (1) |
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432 | (4) |
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436 | (3) |
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Classifying Categorical Outcomes with k-Nearest Neighbors |
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436 | (2) |
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Estimating Continuous Outcomes with k-Nearest Neighbors |
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438 | (1) |
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9.5 Classification and Regression Trees |
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439 | (10) |
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Classifying Categorical Outcomes with a Classification Tree |
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439 | (6) |
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Estimating Continuous Outcomes with a Regression Tree |
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445 | (1) |
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446 | (3) |
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449 | (1) |
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450 | (2) |
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452 | (10) |
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Case Problem: Grey Code Corporation |
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462 | (2) |
Chapter 10 Spreadsheet Models |
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464 | (36) |
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Analytics in Action: Procter & Gamble |
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465 | (1) |
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10.1 Building Good Spreadsheet Models |
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466 | (5) |
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466 | (1) |
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Building a Mathematical Model |
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466 | (2) |
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Spreadsheet Design and Implementing the Model in a Spreadsheet |
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468 | (3) |
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471 | (9) |
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471 | (2) |
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473 | (2) |
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475 | (5) |
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10.3 Some Useful Excel Functions for Modeling |
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480 | (7) |
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481 | (2) |
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483 | (2) |
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485 | (2) |
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10.4 Auditing Spreadsheet Models |
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487 | (4) |
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Trace Precedents and Dependents |
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487 | (1) |
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487 | (2) |
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489 | (1) |
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489 | (1) |
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490 | (1) |
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10.5 Predictive and Prescriptive Spreadsheet Models |
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491 | (1) |
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492 | (1) |
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492 | (1) |
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493 | (6) |
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Case Problem: Retirement Plan |
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499 | (1) |
Chapter 11 Monte Carlo Simulation |
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500 | (56) |
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Analytics in Action: Polio Eradication |
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501 | (1) |
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11.1 Risk Analysis for Sanotronics LLC |
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502 | (12) |
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502 | (1) |
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503 | (1) |
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503 | (1) |
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Sanotronics Spreadsheet Model |
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503 | (1) |
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Use of Probability Distributions to Represent Random Variables |
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504 | (2) |
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Generating Values for Random Variables with Excel |
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506 | (4) |
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Executing Simulation Trials with Excel |
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510 | (1) |
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Measuring and Analyzing Simulation Output |
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510 | (4) |
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11.2 Simulation Modeling for Land Shark Inc. |
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514 | (13) |
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Spreadsheet Model for Land Shark |
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515 | (2) |
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Generating Values for Land Shark's Random Variables |
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517 | (2) |
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Executing Simulation Trials and Analyzing Output |
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519 | (3) |
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Generating Bid Amounts with Fitted Distributions |
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522 | (5) |
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11.3 Simulation with Dependent Random Variables |
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527 | (5) |
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Spreadsheet Model for Press Teag Worldwide |
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527 | (5) |
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11.4 Simulation Considerations |
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532 | (1) |
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Verification and Validation |
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532 | (1) |
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Advantages and Disadvantages of Using Simulation |
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532 | (1) |
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533 | (1) |
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534 | (1) |
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534 | (13) |
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Case Problem: Four Corners |
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547 | (2) |
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Appendix 11.1: Common Probability Distributions for Simulation |
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549 | (7) |
Chapter 12 Linear Optimization Models |
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556 | (50) |
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Analytics in Action: General Electric |
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557 | (1) |
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12.1 A Simple Maximization Problem |
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558 | (3) |
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559 | (2) |
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Mathematical Model for the Par, Inc. Problem |
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561 | (1) |
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12.2 Solving the Par, Inc. Problem |
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561 | (7) |
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The Geometry of the Par, Inc. Problem |
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562 | (2) |
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Solving Linear Programs with Excel Solver |
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564 | (4) |
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12.3 A Simple Minimization Problem |
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568 | (2) |
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568 | (1) |
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Solution for the M&D Chemicals Problem |
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568 | (2) |
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12.4 Special Cases of Linear Program Outcomes |
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570 | (5) |
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Alternative Optimal Solutions |
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571 | (1) |
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572 | (1) |
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573 | (2) |
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12.5 Sensitivity Analysis |
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575 | (2) |
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Interpreting Excel Solver Sensitivity Report |
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575 | (2) |
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12.6 General Linear Programming Notation and More Examples |
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577 | (12) |
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Investment Portfolio Selection |
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578 | (2) |
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580 | (4) |
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Advertising Campaign Planning |
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584 | (5) |
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12.7 Generating an Alternative Optimal Solution for a Linear Program |
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589 | (2) |
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591 | (1) |
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592 | (1) |
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593 | (11) |
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Case Problem: Investment Strategy |
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604 | (2) |
Chapter 13 Integer Linear Optimization Models |
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606 | (40) |
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Analytics in Action: Petrobras |
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607 | (1) |
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13.1 Types of Integer Linear Optimization Models |
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607 | (1) |
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13.2 Eastborne Realty, an Example of Integer Optimization |
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608 | (3) |
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The Geometry of Linear All-Integer Optimization |
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609 | (2) |
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13.3 Solving Integer Optimization Problems with Excel Solver |
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611 | (5) |
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A Cautionary Note About Sensitivity Analysis |
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614 | (2) |
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13.4 Applications Involving Binary Variables |
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616 | (10) |
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616 | (2) |
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618 | (3) |
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621 | (2) |
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Product Design and Market Share Optimization |
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623 | (3) |
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13.5 Modeling Flexibility Provided by Binary Variables |
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626 | (2) |
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Multiple-Choice and Mutually Exclusive Constraints |
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626 | (1) |
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k Out of n Alternatives Constraint |
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627 | (1) |
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Conditional and Corequisite Constraints |
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627 | (1) |
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13.6 Generating Alternatives in Binary Optimization |
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628 | (2) |
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630 | (1) |
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631 | (1) |
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632 | (11) |
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Case Problem: Applecore Children's Clothing |
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643 | (3) |
Chapter 14 Nonlinear Optimization Models |
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646 | (32) |
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Analytics in Action: InterContinental Hotels |
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647 | (1) |
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14.1 A Production Application: Par, Inc. Revisited |
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647 | (5) |
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647 | (1) |
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648 | (2) |
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Solving Nonlinear Optimization Models Using Excel Solver |
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650 | (1) |
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Sensitivity Analysis and Shadow Prices in Nonlinear Models |
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651 | (1) |
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14.2 Local and Global Optima |
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652 | (5) |
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Overcoming Local Optima with Excel Solver |
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655 | (2) |
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657 | (1) |
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14.4 Markowitz Portfolio Model |
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658 | (5) |
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14.5 Forecasting Adoption of a New Product |
|
|
663 | (3) |
|
|
666 | (1) |
|
|
667 | (1) |
|
|
667 | (8) |
|
Case Problem: Portfolio Optimization with Transaction Costs |
|
|
675 | (3) |
Chapter 15 Decision Analysis |
|
678 | (46) |
|
Analytics in Action: Phytopharm |
|
|
679 | (1) |
|
|
680 | (2) |
|
|
681 | (1) |
|
|
681 | (1) |
|
15.2 Decision Analysis without Probabilities |
|
|
682 | (3) |
|
|
682 | (1) |
|
|
683 | (1) |
|
|
683 | (2) |
|
15.3 Decision Analysis with Probabilities |
|
|
685 | (4) |
|
|
685 | (2) |
|
|
687 | (1) |
|
|
688 | (1) |
|
15.4 Decision Analysis with Sample Information |
|
|
689 | (6) |
|
Expected Value of Sample Information |
|
|
694 | (1) |
|
Expected Value of Perfect Information |
|
|
694 | (1) |
|
15.5 Computing Branch Probabilities with Bayes' Theorem |
|
|
695 | (3) |
|
|
698 | (26) |
|
Utility and Decision Analysis |
|
|
699 | (4) |
|
|
703 | (3) |
|
Exponential Utility Function |
|
|
706 | (2) |
|
|
708 | (1) |
|
|
708 | (2) |
|
|
710 | (11) |
|
Case Problem: Property Purchase Strategy |
|
|
721 | (3) |
Appendix A: Basics Of Excel |
|
724 | (12) |
Appendix B: Database Basics With Microsoft Access |
|
736 | (38) |
References |
|
774 | (2) |
Index |
|
776 | |