About the Authors |
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xiv | |
Preface |
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xvii | |
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1 | (14) |
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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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5 | (3) |
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5 | (1) |
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6 | (1) |
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6 | (1) |
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Analytics in Action: Procter & Gamble Uses Business Analytics to Redesign its Supply Chain |
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7 | (1) |
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8 | (1) |
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1.5 Business Analytics in Practice |
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9 | (6) |
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9 | (1) |
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Human Resource (HR) Analytics |
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10 | (1) |
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10 | (1) |
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10 | (1) |
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11 | (1) |
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Analytics for Government and Nonprofits |
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11 | (1) |
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12 | (1) |
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12 | (1) |
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13 | (1) |
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13 | (2) |
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Chapter 2 Descriptive Statistics |
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15 | (55) |
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Analytics in Action: U.S. Census Bureau |
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16 | (1) |
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2.1 Overview of Using Data: Definitions and Goals |
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16 | (1) |
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17 | (4) |
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Population and Sample Data |
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17 | (1) |
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Quantitative and Categorical Data |
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18 | (1) |
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Cross-Sectional and Time Series Data |
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18 | (1) |
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18 | (3) |
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2.3 Modifying Data in Excel |
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21 | (4) |
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Sorting and Filtering Data in Excel |
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21 | (2) |
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Conditional Formatting of Data in Excel |
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23 | (2) |
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2.4 Creating Distributions from Data |
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25 | (10) |
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Frequency Distributions for Categorical Data |
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25 | (2) |
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Relative Frequency and Percent Frequency Distributions |
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27 | (1) |
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Frequency Distributions for Quantitative Data |
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28 | (3) |
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31 | (3) |
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34 | (1) |
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35 | (5) |
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35 | (1) |
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36 | (1) |
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37 | (1) |
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38 | (2) |
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2.6 Measures of Variability |
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40 | (4) |
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41 | (1) |
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41 | (2) |
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43 | (1) |
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44 | (1) |
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2.7 Analyzing Distributions |
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44 | (7) |
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44 | (1) |
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45 | (1) |
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46 | (2) |
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48 | (1) |
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48 | (1) |
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49 | (2) |
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2.8 Measures of Association Between Two Variables |
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51 | (19) |
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51 | (1) |
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52 | (3) |
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55 | (2) |
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57 | (1) |
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57 | (1) |
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58 | (8) |
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Case: Heavenly Chocolates Web Site Transactions |
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66 | (1) |
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Appendix: Creating Box Plots in XLMiner |
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67 | (3) |
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Chapter 3 Data Visualization |
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70 | (53) |
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Analytics in Action: Cincinnati Zoo & Botanical Garden |
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71 | (2) |
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3.1 Overview of Data Visualization |
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73 | (2) |
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Effective Design Techniques |
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73 | (2) |
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75 | (10) |
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77 | (2) |
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79 | (1) |
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80 | (5) |
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85 | (17) |
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85 | (2) |
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87 | (3) |
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Bar Charts and Column Charts |
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90 | (3) |
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A Note on Pie Charts and 3-D Charts |
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93 | (1) |
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93 | (2) |
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95 | (2) |
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Additional Charts for Multiple Variables |
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97 | (4) |
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101 | (1) |
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3.4 Advanced Data Visualization |
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102 | (3) |
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103 | (1) |
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Geographic Information Systems Charts |
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104 | (1) |
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105 | (18) |
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Principles of Effective Data Dashboards |
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106 | (1) |
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Applications of Data Dashboards |
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106 | (2) |
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108 | (1) |
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109 | (1) |
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110 | (8) |
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Case Problem: All-Time Movie Box Office Data |
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118 | (1) |
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Appendix: Creating a Scatter Chart Matrix and a Parallel Coordinates Plot with XLMiner |
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119 | (4) |
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Chapter 4 Linear Regression |
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123 | (79) |
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Analytics in Action: Alliance Data Systems |
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124 | (1) |
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4.1 The Simple Linear Regression Model |
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125 | (2) |
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Regression Model and Regression Equation |
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125 | (1) |
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Estimated Regression Equation |
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126 | (1) |
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127 | (6) |
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Least Squares Estimates of the Regression Parameters |
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129 | (3) |
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Using Excel's Chart Tools to Compute the Estimated Regression Equation |
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132 | (1) |
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4.3 Assessing the Fit of the Simple Linear Regression Model |
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133 | (5) |
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134 | (2) |
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The Coefficient of Determination |
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136 | (1) |
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Using Excel's Chart Tools to Compute the Coefficient of Determination |
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137 | (1) |
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4.4 The Multiple Regression Model |
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138 | (5) |
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Regression Model and Regression Equation |
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138 | (1) |
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Estimated Multiple Regression Equation |
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138 | (1) |
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Least Squares Method and Multiple Regression |
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139 | (1) |
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Butler Trucking Company and Multiple Regression |
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140 | (1) |
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Using Excel's Regression Tool to Develop the Estimated Multiple Regression Equation |
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140 | (3) |
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4.5 Inference and Regression |
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143 | (18) |
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Conditions Necessary for Valid Inference in the Least Squares Regression Model |
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144 | (4) |
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Testing for an Overall Regression Relationship |
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148 | (2) |
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Testing Individual Regression Parameters |
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150 | (3) |
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Addressing Nonsignificant Independent Variables |
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153 | (1) |
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154 | (2) |
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Inference and Very Large Samples |
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156 | (5) |
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4.6 Categorical Independent Variables |
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161 | (4) |
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Butler Trucking Company and Rush Hour |
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161 | (1) |
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Interpreting the Parameters |
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162 | (2) |
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More Complex Categorical Variables |
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164 | (1) |
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4.7 Modeling Nonlinear Relationships |
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165 | (12) |
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Quadratic Regression Models |
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167 | (3) |
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Piecewise Linear Regression Models |
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170 | (3) |
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Interaction Between Independent Variables |
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173 | (4) |
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177 | (25) |
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Variable Selection Procedures |
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177 | (2) |
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179 | (1) |
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180 | (1) |
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180 | (2) |
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182 | (15) |
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Case Problem: Alumni Giving |
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197 | (1) |
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Appendix: Using XLMiner for Regression |
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198 | (4) |
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Chapter 5 Time Series Analysis and Forecasting |
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202 | (49) |
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Analytics in Action: Forecasting Demand for a Broad Line of Office Products |
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203 | (2) |
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205 | (7) |
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205 | (2) |
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207 | (2) |
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209 | (1) |
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Trend and Seasonal Pattern |
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209 | (2) |
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211 | (1) |
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Identifying Time Series Patterns |
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212 | (1) |
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212 | (5) |
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5.3 Moving Averages and Exponential Smoothing |
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217 | (9) |
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217 | (4) |
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221 | (1) |
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221 | (3) |
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224 | (2) |
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5.4 Using Regression Analysis for Forecasting |
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226 | (10) |
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226 | (2) |
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228 | (1) |
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Seasonality Without Trend |
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228 | (2) |
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230 | (1) |
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Using Regression Analysis as a Causal Forecasting Method |
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231 | (4) |
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Combining Causal Variables with Trend and Seasonality Effects |
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235 | (1) |
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Considerations in Using Regression in Forecasting |
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235 | (1) |
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5.5 Determining the Best Forecasting Model to Use |
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236 | (15) |
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237 | (1) |
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237 | (1) |
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238 | (8) |
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Case Problem: Forecasting Food and Beverage Sales |
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246 | (1) |
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Appendix: Using XLMiner for Forecasting |
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247 | (4) |
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251 | (69) |
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Analytics in Action: Online Retailers Using Predictive Analytics to Cater to Customers |
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252 | (1) |
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253 | (1) |
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254 | (1) |
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Treatment of Missing Data |
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254 | (1) |
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Identification of Outliers and Erroneous Data |
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254 | (1) |
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254 | (1) |
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6.3 Unsupervised Learning |
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255 | (14) |
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256 | (9) |
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265 | (4) |
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269 | (51) |
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269 | (4) |
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273 | (4) |
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277 | (1) |
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277 | (6) |
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Classification and Regression Trees |
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283 | (16) |
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299 | (9) |
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308 | (1) |
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309 | (2) |
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311 | (8) |
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Case Problem: Grey Code Corporation |
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319 | (1) |
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Chapter 7 Spreadsheet Models |
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320 | (32) |
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Analytics in Action: Procter and Gamble Sets Inventory Targets Using Spreadsheet Models |
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321 | (1) |
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7.1 Building Good Spreadsheet Models |
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322 | (5) |
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322 | (1) |
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Building a Mathematical Model |
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322 | (2) |
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Spreadsheet Design and Implementing the Model in a Spreadsheet |
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324 | (3) |
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327 | (5) |
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327 | (4) |
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331 | (1) |
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7.3 Some Useful Excel Functions for Modeling |
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332 | (7) |
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332 | (1) |
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333 | (4) |
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337 | (2) |
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7.4 Auditing Spreadsheet Models |
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339 | (13) |
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Trace Precedents and Dependents |
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339 | (1) |
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340 | (1) |
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340 | (1) |
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341 | (1) |
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342 | (1) |
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343 | (1) |
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343 | (1) |
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344 | (6) |
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Case Problem: Retirement Plan |
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350 | (2) |
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Chapter 8 Linear Optimization Models |
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352 | (53) |
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Analytics in Action: Timber Harvesting Model at MeadWestvaco Corporation |
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353 | (1) |
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8.1 A Simple Maximization Problem |
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354 | (4) |
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355 | (2) |
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Mathematical Model for the Par, Inc. Problem |
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357 | (1) |
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8.2 Solving the Par, Inc. Problem |
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358 | (6) |
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The Geometry of the Par, Inc. Problem |
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358 | (2) |
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Solving Linear Programs with Excel Solver |
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360 | (4) |
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8.3 A Simple Minimization Problem |
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364 | (3) |
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364 | (1) |
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Solution for the M&D Chemicals Problem |
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365 | (2) |
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8.4 Special Cases of Linear Program Outcomes |
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367 | (5) |
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Alternative Optimal Solutions |
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367 | (1) |
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368 | (2) |
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370 | (2) |
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372 | (2) |
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Interpreting Excel Solver Sensitivity Report |
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372 | (2) |
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8.6 General Linear Programming Notation and More Examples |
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374 | (12) |
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Investment Portfolio Selection |
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375 | (3) |
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378 | (3) |
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Advertising Campaign Planning |
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381 | (5) |
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8.7 Generating an Alternative Optimal Solution for a Linear Program |
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386 | (19) |
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388 | (1) |
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389 | (1) |
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390 | (8) |
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Case Problem: Investment Strategy |
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398 | (1) |
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Appendix: Solving Linear Optimization Models Using Analytic Solver Platform |
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399 | (6) |
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Chapter 9 Integer Linear Optimization Models |
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405 | (43) |
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Analytics in Action: Optimizing the Transport of Oil Rig Crews |
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406 | (1) |
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9.1 Types of Integer Linear Optimization Models |
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406 | (1) |
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9.2 Eastborne Realty, An Example of Integer Optimization |
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407 | (3) |
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The Geometry of Linear All-Integer Optimization |
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408 | (2) |
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9.3 Solving Integer Optimization Problems with Excel Solver |
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410 | (5) |
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A Cautionary Note About Sensitivity Analysis |
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414 | (1) |
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9.4 Applications Involving Binary Variables |
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415 | (11) |
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415 | (1) |
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416 | (4) |
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420 | (4) |
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Product Design and Market Share Optimization |
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424 | (2) |
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9.5 Modeling Flexibility Provided by Binary Variables |
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426 | (2) |
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Multiple-Choice and Mutually Exclusive Constraints |
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427 | (1) |
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k out of n Alternatives Constraint |
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427 | (1) |
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Conditional and Corequisite Constraints |
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427 | (1) |
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9.6 Generating Alternatives in Binary Optimization |
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428 | (20) |
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430 | (1) |
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430 | (1) |
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431 | (10) |
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Case Problem: Applecore Children's Clothing |
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441 | (1) |
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Appendix: Solving Integer Linear Optimization Problems Using Analytic Solver Platform |
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442 | (6) |
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Chapter 10 Nonlinear Optimization Models |
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448 | (37) |
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Analytics in Action: Intercontinental Hotels Optimizes Retail Pricing |
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449 | (1) |
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10.1 A Production Application: Par, Inc. Revisited |
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449 | (6) |
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450 | (1) |
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450 | (3) |
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Solving Nonlinear Optimization Models Using Excel Solver |
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453 | (1) |
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Sensitivity Analysis and Shadow Prices in Nonlinear Models |
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454 | (1) |
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10.2 Local and Global Optima |
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455 | (4) |
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Overcoming Local Optima with Excel Solver |
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457 | (2) |
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459 | (2) |
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10.4 Markowitz Portfolio Model |
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461 | (4) |
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10.5 Forecasting Adoption of a New Product |
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465 | (20) |
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469 | (1) |
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470 | (1) |
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470 | (7) |
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Case Problem: Portfolio Optimization with Transaction Costs |
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477 | (3) |
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Appendix: Solving Nonlinear Optimization Problems with Analytic Solver Platform |
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480 | (5) |
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Chapter 11 Monte Carlo Simulation |
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485 | (65) |
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Analytics in Action: Reducing Patient Infections in the ICU |
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486 | (1) |
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487 | (1) |
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487 | (1) |
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487 | (1) |
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488 | (1) |
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488 | (1) |
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11.2 Simulation Modeling with Native Excel Functions |
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488 | (10) |
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Use of Probability Distributions to Represent Random Variables |
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489 | (2) |
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Generating Values for Random Variables with Excel |
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491 | (4) |
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Executing Simulation Trials with Excel |
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495 | (1) |
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Measuring and Analyzing Simulation Output |
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495 | (3) |
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11.3 Simulation Modeling with Analytic Solver Platform |
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498 | (20) |
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499 | (1) |
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Spreadsheet Model for Land Shark |
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499 | (1) |
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Generating Values for Land Shark's Random Variables |
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500 | (3) |
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Tracking Output Measures for Land Shark |
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503 | (1) |
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Executing Simulation Trials and Analyzing Output for Land Shark |
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504 | (2) |
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506 | (1) |
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Spreadsheet Model for Zappos |
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507 | (3) |
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Modeling Random Variables for Zappos |
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510 | (5) |
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Tracking Output Measures for Zappos |
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515 | (2) |
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Executing Simulation Trials and Analyzing Output for Zappos |
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517 | (1) |
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11.4 Simulation Optimization |
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518 | (6) |
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11.5 Simulation Considerations |
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524 | (26) |
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Verification and Validation |
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524 | (1) |
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Advantages and Disadvantages of Using Simulation |
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524 | (1) |
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525 | (1) |
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526 | (1) |
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527 | (9) |
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Case Problem: Four Corners |
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536 | (1) |
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Appendix 11.1 Incorporating Dependence Between Random Variables |
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537 | (8) |
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Appendix 11.2 Probability Distributions for Random Variables |
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545 | (5) |
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Chapter 12 Decision Analysis |
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550 | (59) |
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Analytics in Action: Phytopharm's New Product Research and Development |
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551 | (1) |
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552 | (2) |
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553 | (1) |
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553 | (1) |
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12.2 Decision Analysis Without Probabilities |
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554 | (3) |
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554 | (1) |
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555 | (1) |
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555 | (2) |
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12.3 Decision Analysis with Probabilities |
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557 | (4) |
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557 | (2) |
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559 | (1) |
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560 | (1) |
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12.4 Decision Analysis with Sample Information |
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561 | (7) |
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Expected Value of Sample Information |
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566 | (1) |
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Expected Value of Perfect Information |
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567 | (1) |
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12.5 Computing Branch Probabilities with Bayes' Theorem |
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568 | (3) |
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571 | (38) |
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Utility and Decision Analysis |
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573 | (4) |
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577 | (3) |
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Exponential Utility Function |
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580 | (1) |
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581 | (1) |
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582 | (2) |
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584 | (11) |
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Case Problem: Property Purchase Strategy |
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595 | (1) |
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Appendix: Using Analytic Solver Platform to Create Decision Trees |
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596 | (13) |
Appendix A Basics of Excel |
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609 | (12) |
Appendix B Data Management and Microsoft Access |
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621 | (38) |
Appendix C Answers to Even-Numbered Exercises (online) |
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References |
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659 | (2) |
Index |
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661 | |