About The Authors |
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xvii | |
Preface |
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xix | |
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1 | (18) |
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3 | (1) |
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1.2 Business Analytics Defined |
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4 | (1) |
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1.3 A Categorization of Analytical Methods and Models |
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5 | (1) |
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5 | (1) |
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5 | (1) |
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6 | (1) |
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6 | (4) |
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8 | (1) |
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8 | (1) |
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8 | (1) |
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8 | (2) |
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1.5 Business Analytics in Practice |
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10 | (3) |
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10 | (1) |
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Human Resource (HR) Analytics |
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11 | (1) |
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11 | (1) |
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11 | (1) |
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12 | (1) |
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Analytics for Government and Nonprofits |
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12 | (1) |
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12 | (1) |
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13 | (1) |
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1.6 Legal and Ethical Issues in the Use of Data and Analytics |
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13 | (3) |
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16 | (1) |
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16 | (3) |
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Available in the MindTap Reader |
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Appendix: Getting Started with R and RStudio |
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Appendix: Basic Data Manipulation with R |
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Chapter 2 Descriptive Statistics |
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19 | (66) |
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2.1 Overview of Using Data: Definitions and Goals |
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20 | (2) |
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22 | (3) |
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Population and Sample Data |
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22 | (1) |
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Quantitative and Categorical Data |
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22 | (1) |
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Cross-Sectional and Time Series Data |
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22 | (1) |
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22 | (3) |
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2.3 Modifying Data in Excel |
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25 | (5) |
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Sorting and Filtering Data in Excel |
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25 | (3) |
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Conditional Formatting of Data in Excel |
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28 | (2) |
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2.4 Creating Distributions from Data |
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30 | (10) |
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Frequency Distributions for Categorical Data |
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30 | (1) |
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Relative Frequency and Percent Frequency Distributions |
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31 | (1) |
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Frequency Distributions for Quantitative Data |
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32 | (3) |
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35 | (3) |
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38 | (2) |
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40 | (5) |
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40 | (1) |
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41 | (1) |
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42 | (1) |
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42 | (3) |
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2.6 Measures of Variability |
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45 | (3) |
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45 | (1) |
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46 | (1) |
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47 | (1) |
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48 | (1) |
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2.7 Analyzing Distributions |
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48 | (8) |
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49 | (1) |
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50 | (1) |
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50 | (1) |
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51 | (2) |
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53 | (1) |
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53 | (3) |
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2.8 Measures of Association Between Two Variables |
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56 | (6) |
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56 | (2) |
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58 | (3) |
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61 | (1) |
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62 | (7) |
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62 | (2) |
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64 | (2) |
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Identification of Erroneous Outliers and Other Erroneous Values |
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66 | (2) |
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68 | (1) |
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69 | (1) |
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70 | (1) |
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71 | (10) |
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Case Problem 1 Heavenly Chocolates Web Site Transactions |
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81 | (1) |
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Case Problem 2 African Elephant Populations |
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82 | (3) |
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Available in the MindTap Reader |
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Appendix: Descriptive Statistics with R |
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Chapter 3 Data Visualization |
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85 | (72) |
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3.1 Overview of Data Visualization |
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88 | (3) |
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Effective Design Techniques |
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88 | (3) |
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91 | (11) |
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92 | (1) |
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93 | (3) |
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96 | (4) |
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Recommended PivotTables in Excel |
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100 | (2) |
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102 | (18) |
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102 | (2) |
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Recommended Charts in Excel |
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104 | (1) |
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105 | (4) |
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Bar Charts and Column Charts |
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109 | (1) |
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A Note on Pie Charts and Three-Dimensional Charts |
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110 | (2) |
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112 | (1) |
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113 | (2) |
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Additional Charts for Multiple Variables |
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115 | (3) |
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118 | (2) |
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3.4 Advanced Data Visualization |
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120 | (5) |
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120 | (3) |
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Geographic Information Systems Charts |
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123 | (2) |
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125 | (3) |
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Principles of Effective Data Dashboards |
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125 | (1) |
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Applications of Data Dashboards |
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126 | (2) |
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128 | (1) |
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128 | (1) |
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129 | (10) |
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Case Problem 1 Pelican stores |
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139 | (1) |
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Case Problem 2 Movie Theater Releases |
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140 | (1) |
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Appendix: Data Visualization in Tableau |
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141 | (16) |
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Available in the MindTap Reader |
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Appendix: Creating Tabular and Graphical Presentations with R |
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Chapter 4 Probability: An Introduction to Modeling Uncertainty |
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157 | (56) |
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4.1 Events and Probabilities |
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159 | (1) |
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4.2 Some Basic Relationships of Probability |
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160 | (3) |
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160 | (1) |
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161 | (2) |
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4.3 Conditional Probability |
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163 | (8) |
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168 | (1) |
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168 | (1) |
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169 | (2) |
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171 | (2) |
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Discrete Random Variables |
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171 | (1) |
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Continuous Random Variables |
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172 | (1) |
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4.5 Discrete Probability Distributions |
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173 | (12) |
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Custom Discrete Probability Distribution |
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173 | (2) |
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Expected Value and Variance |
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175 | (3) |
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Discrete Uniform Probability Distribution |
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178 | (1) |
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Binomial Probability Distribution |
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179 | (3) |
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Poisson Probability Distribution |
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182 | (3) |
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4.6 Continuous Probability Distributions |
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185 | (13) |
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Uniform Probability Distribution |
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185 | (2) |
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Triangular Probability Distribution |
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187 | (2) |
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Normal Probability Distribution |
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189 | (5) |
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Exponential Probability Distribution |
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194 | (4) |
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198 | (1) |
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198 | (2) |
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200 | (9) |
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Case Problem 1 Hamilton County Judges |
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209 | (1) |
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Case Problem 2 McNeil's Auto Mall |
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210 | (1) |
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Case Problem 3 Gebhardt Electronics |
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211 | (2) |
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Available in the MindTap Reader |
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Appendix: Discrete Probability Distributions with R |
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Appendix: Continuous Probability Distributions with R |
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Chapter 5 Descriptive Data Mining |
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213 | (40) |
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215 | (11) |
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Measuring Distance Between Observations |
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215 | (3) |
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218 | (3) |
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Hierarchical Clustering and Measuring Dissimilarity Between Clusters |
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221 | (4) |
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Hierarchical Clustering Versus k-Means Clustering |
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225 | (1) |
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226 | (3) |
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Evaluating Association Rules |
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228 | (1) |
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229 | (6) |
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Voice of the Customer at Triad Airline |
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229 | (2) |
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Preprocessing Text Data for Analysis |
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231 | (1) |
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232 | (2) |
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Computing Dissimilarity Between Documents |
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234 | (1) |
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234 | (1) |
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235 | (1) |
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235 | (2) |
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237 | (14) |
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Case Problem 1 Big Ten Expansion |
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251 | (1) |
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Case Problem 2 Know Thy Customer |
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251 | (2) |
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Available in the MindTap Reader |
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Appendix: Getting Started with Rattle in R |
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Appendix: k-Means Clustering with R |
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Appendix: Hierarchical Clustering with R |
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Appendix: Association Rules with R |
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Appendix: Text Mining with R |
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Appendix: R/Rattle Settings to Solve Chapter 5 Problems |
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Appendix: Opening and Saving Excel Files in JMP Pro |
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Appendix: Hierarchical Clustering with JMP Pro |
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Appendix: k-Means Clustering with JMP Pro |
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Appendix: Association Rules with JMP Pro |
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Appendix: Text Mining with JMP Pro |
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Appendix: JMP Pro Settings to Solve Chapter 5 Problems |
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Chapter 6 Statistical Inference |
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253 | (74) |
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256 | (4) |
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Sampling from a Finite Population |
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256 | (1) |
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Sampling from an Infinite Population |
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257 | (3) |
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260 | (2) |
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262 | (1) |
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6.3 Sampling Distributions |
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262 | (11) |
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Sampling Distribution of x |
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265 | (5) |
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Sampling Distribution of p |
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270 | (3) |
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273 | (10) |
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Interval Estimation of the Population Mean |
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273 | (7) |
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Interval Estimation of the Population Proportion |
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280 | (3) |
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283 | (18) |
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Developing Null and Alternative Hypotheses |
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283 | (3) |
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Type I and Type II Errors |
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286 | (1) |
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Hypothesis Test of the Population Mean |
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287 | (11) |
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Hypothesis Test of the Population Proportion |
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298 | (3) |
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6.6 Big Data, Statistical Inference, and Practical Significance |
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301 | (9) |
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301 | (1) |
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302 | (1) |
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303 | (1) |
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Understanding What Big Data Is |
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304 | (1) |
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Big Data and Sampling Error |
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305 | (1) |
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Big Data and the Precision of Confidence Intervals |
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306 | (1) |
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Implications of Big Data for Confidence Intervals |
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307 | (1) |
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Big Data, Hypothesis Testing, and p Values |
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308 | (2) |
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Implications of Big Data in Hypothesis Testing |
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310 | (1) |
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310 | (1) |
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311 | (3) |
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314 | (10) |
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Case Problem 1 Young Professional Magazine |
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324 | (1) |
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Case Problem 2 Quality Associates, Inc. |
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325 | (2) |
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Available in the MindTap Reader |
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Appendix: Random Sampling with R |
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Appendix: Interval Estimation with R |
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Appendix: Hypothesis Testing with R |
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Chapter 7 Linear Regression |
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327 | (80) |
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7.1 Simple Linear Regression Model |
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329 | (2) |
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329 | (1) |
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Estimated Regression Equation |
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329 | (2) |
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331 | (6) |
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Least Squares Estimates of the Regression Parameters |
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333 | (2) |
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Using Excel's Chart Tools to Compute the Estimated Regression Equation |
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335 | (2) |
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7.3 Assessing the Fit of the Simple Linear Regression Model |
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337 | (4) |
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337 | (2) |
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The Coefficient of Determination |
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339 | (1) |
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Using Excel's Chart Tools to Compute the Coefficient of Determination |
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340 | (1) |
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7.4 The Multiple Regression Model |
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341 | (5) |
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341 | (1) |
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Estimated Multiple Regression Equation |
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341 | (1) |
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Least Squares Method and Multiple Regression |
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342 | (1) |
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Butler Trucking Company and Multiple Regression |
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342 | (1) |
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Using Excel's Regression Tool to Develop the Estimated Multiple Regression Equation |
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343 | (3) |
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7.5 Inference and Regression |
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346 | (12) |
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Conditions Necessary for Valid Inference in the Least Squares Regression Model |
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347 | (4) |
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Testing Individual Regression Parameters |
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351 | (3) |
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Addressing Nonsignificant Independent Variables |
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354 | (1) |
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355 | (3) |
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7.6 Categorical Independent Variables |
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358 | (5) |
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Butler Trucking Company and Rush Hour |
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358 | (2) |
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Interpreting the Parameters |
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360 | (1) |
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More Complex Categorical Variables |
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361 | (2) |
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7.7 Modeling Nonlinear Relationships |
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363 | (12) |
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Quadratic Regression Models |
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364 | (4) |
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Piecewise Linear Regression Models |
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368 | (2) |
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Interaction Between Independent Variables |
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370 | (5) |
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375 | (2) |
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Variable Selection Procedures |
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375 | (1) |
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376 | (1) |
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7.9 Big Data and Regression |
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377 | (5) |
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Inference and Very Large Samples |
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377 | (3) |
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380 | (2) |
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7.10 Prediction with Regression |
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382 | (2) |
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384 | (1) |
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384 | (2) |
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386 | (16) |
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Case Problem 1 Alumni Giving |
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402 | (2) |
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Case Problem 2 Consumer Research, Inc. |
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404 | (1) |
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Case Problem 3 Predicting Winnings for NASCAR Drivers |
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405 | (2) |
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Available in the MindTap Reader |
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Appendix: Simple Linear Regression with R |
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Appendix: Multiple Linear Regression with R |
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Appendix: Regression Variable Selection Procedures with R |
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Chapter 8 Time Series Analysis and Forecasting |
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407 | (52) |
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410 | (7) |
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410 | (2) |
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412 | (1) |
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413 | (1) |
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Trend and Seasonal Pattern |
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414 | (3) |
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417 | (1) |
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Identifying Time Series Patterns |
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417 | (1) |
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417 | (4) |
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8.3 Moving Averages and Exponential Smoothing |
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421 | (9) |
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422 | (4) |
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426 | (4) |
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8.4 Using Regression Analysis for Forecasting |
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430 | (10) |
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430 | (2) |
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Seasonality Without Trend |
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432 | (1) |
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433 | (3) |
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Using Regression Analysis as a Causal Forecasting Method |
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436 | (3) |
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Combining Causal Variables with Trend and Seasonality Effects |
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439 | (1) |
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Considerations in Using Regression in Forecasting |
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440 | (1) |
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8.5 Determining the Best Forecasting Model to Use |
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440 | (1) |
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441 | (1) |
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441 | (1) |
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442 | (8) |
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Case Problem 1 Forecasting Food and Beverage Sales |
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450 | (1) |
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Case Problem 2 Forecasting Lost Sales |
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450 | (2) |
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Appendix: Using the Excel Forecast Sheet |
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452 | (7) |
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Available in the MindTap Reader |
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Appendix: Forecasting with R |
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Chapter 9 Predictive Data Mining |
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459 | (50) |
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9.1 Data Sampling, Preparation, and Partitioning |
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461 | (3) |
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461 | (1) |
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462 | (1) |
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463 | (1) |
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464 | (7) |
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Evaluating the Classification of Categorical Outcomes |
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464 | (6) |
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Evaluating the Estimation of Continuous Outcomes |
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470 | (1) |
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471 | (4) |
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475 | (3) |
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Classifying Categorical Outcomes with k-Nearest Neighbors |
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475 | (2) |
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Estimating Continuous Outcomes with k-Nearest Neighbors |
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477 | (1) |
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9.5 Classification and Regression Trees |
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478 | (11) |
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Classifying Categorical Outcomes with a Classification Tree |
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478 | (5) |
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Estimating Continuous Outcomes with a Regression Tree |
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483 | (2) |
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485 | (4) |
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489 | (2) |
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491 | (1) |
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492 | (13) |
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Case Problem: Grey Code Corporation |
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505 | (4) |
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Available in the MindTap Reader |
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Appendix: Classification via Logistic Regression with R |
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Appendix: k-Nearest Neighbor Classification with R |
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Appendix: k-Nearest Neighbor Regression with R |
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Appendix: Individual Classification Trees with R |
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Appendix: Individual Regression Trees with R |
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Appendix: Random Forests of Classification Trees with R |
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Appendix: Random Forests of Regression Trees with R |
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Appendix: R/Rattle Settings to Solve Chapter 9 Problems |
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Appendix: Data Partitioning with JMP Pro |
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Appendix: Classification via Logistic Regression with JMP Pro |
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Appendix: k-Nearest Neighbors Classification and Regression with JMP Pro |
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Appendix: Individual Classification and Regression Trees with JMP Pro |
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Appendix: Random Forests of Classification or Regression Trees with JMP Pro |
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Appendix: JMP Pro Settings to Solve Chapter 9 Problems |
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Chapter 10 Spreadsheet Models |
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509 | (38) |
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10.1 Building Good Spreadsheet Models |
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511 | (5) |
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511 | (1) |
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Building a Mathematical Model |
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511 | (2) |
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Spreadsheet Design and Implementing the Model in a Spreadsheet |
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513 | (3) |
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516 | (9) |
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516 | (2) |
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518 | (2) |
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520 | (5) |
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10.3 Some Useful Excel Functions for Modeling |
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525 | (7) |
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526 | (2) |
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528 | (2) |
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530 | (2) |
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10.4 Auditing Spreadsheet Models |
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532 | (4) |
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Trace Precedents and Dependents |
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532 | (1) |
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532 | (2) |
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534 | (1) |
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534 | (1) |
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535 | (1) |
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10.5 Predictive and Prescriptive Spreadsheet Models |
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536 | (1) |
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537 | (1) |
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537 | (1) |
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538 | (6) |
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Case Problem: Retirement Plan |
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544 | (3) |
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Chapter 11 Monte Carlo Simulation |
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547 | (62) |
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11.1 Risk Analysis for Sanotronics LLC |
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549 | (12) |
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549 | (1) |
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550 | (1) |
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550 | (1) |
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Sanotronics Spreadsheet Model |
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550 | (1) |
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Use of Probability Distributions to Represent Random Variables |
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551 | (2) |
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Generating Values for Random Variables with Excel |
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553 | (4) |
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Executing Simulation Trials with Excel |
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557 | (1) |
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Measuring and Analyzing Simulation Output |
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557 | (4) |
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11.2 Inventory Policy Analysis for Promus Corp |
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561 | (7) |
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Spreadsheet Model for Promus |
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562 | (1) |
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Generating Values for Promus Corp's Demand |
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563 | (2) |
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Executing Simulation Trials and Analyzing Output |
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565 | (3) |
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11.3 Simulation Modeling for Land Shark Inc. |
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568 | (12) |
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Spreadsheet Model for Land Shark |
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569 | (1) |
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Generating Values for Land Shark's Random Variables |
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570 | (2) |
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Executing Simulation Trials and Analyzing Output |
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572 | (3) |
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Generating Bid Amounts with Fitted Distributions |
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575 | (5) |
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11.4 Simulation with Dependent Random Variables |
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580 | (5) |
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Spreadsheet Model for Press Teag Worldwide |
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580 | (5) |
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11.5 Simulation Considerations |
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585 | (1) |
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Verification and Validation |
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585 | (1) |
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Advantages and Disadvantages of Using Simulation |
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585 | (1) |
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586 | (1) |
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Summary of Steps for Conducting a Simulation Analysis |
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586 | (1) |
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587 | (1) |
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587 | (13) |
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Case Problem: Four Corners |
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600 | (2) |
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Appendix: Common Probability Distributions for Simulation |
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602 | (7) |
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Chapter 12 Linear Optimization Models |
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609 | (54) |
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12.1 A Simple Maximization Problem |
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611 | (3) |
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612 | (2) |
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Mathematical Model for the Par, Inc. Problem |
|
|
614 | (1) |
|
12.2 Solving the Par, Inc. Problem |
|
|
614 | (7) |
|
The Geometry of the Par, Inc. Problem |
|
|
615 | (2) |
|
Solving Linear Programs with Excel Solver |
|
|
617 | (4) |
|
12.3 A Simple Minimization Problem |
|
|
621 | (2) |
|
|
621 | (1) |
|
Solution for the M&D Chemicals Problem |
|
|
621 | (2) |
|
12.4 Special Cases of Linear Program Outcomes |
|
|
623 | (5) |
|
Alternative Optimal Solutions |
|
|
624 | (1) |
|
|
625 | (1) |
|
|
626 | (2) |
|
12.5 Sensitivity Analysis |
|
|
628 | (2) |
|
Interpreting Excel Solver Sensitivity Report |
|
|
628 | (2) |
|
12.6 General Linear Programming Notation and More Examples |
|
|
630 | (12) |
|
Investment Portfolio Selection |
|
|
631 | (2) |
|
|
633 | (4) |
|
Maximizing Banner Ad Revenue |
|
|
637 | (5) |
|
12.7 Generating an Alternative Optimal Solution for a Linear Program |
|
|
642 | (2) |
|
|
644 | (1) |
|
|
645 | (1) |
|
|
646 | (14) |
|
Case Problem: Investment Strategy |
|
|
660 | (3) |
|
Chapter 13 Integer Linear Optimization Models |
|
|
663 | (40) |
|
13.1 Types of Integer Linear Optimization Models |
|
|
664 | (1) |
|
13.2 Eastborne Realty, an Example of Integer Optimization |
|
|
665 | (3) |
|
The Geometry of Linear All-Integer Optimization |
|
|
666 | (2) |
|
13.3 Solving Integer Optimization Problems with Excel Solver |
|
|
668 | (5) |
|
A Cautionary Note About Sensitivity Analysis |
|
|
671 | (2) |
|
13.4 Applications Involving Binary Variables |
|
|
673 | (10) |
|
|
673 | (2) |
|
|
675 | (3) |
|
|
678 | (2) |
|
Product Design and Market Share Optimization |
|
|
680 | (3) |
|
13.5 Modeling Flexibility Provided by Binary Variables |
|
|
683 | (2) |
|
Multiple-Choice and Mutually Exclusive Constraints |
|
|
683 | (1) |
|
K Out of n Alternatives Constraint |
|
|
684 | (1) |
|
Conditional and Corequisite Constraints |
|
|
684 | (1) |
|
13.6 Generating Alternatives in Binary Optimization |
|
|
685 | (2) |
|
|
687 | (1) |
|
|
688 | (1) |
|
|
689 | (12) |
|
Case Problem: Applecore Children's Clothing |
|
|
701 | (2) |
|
Chapter 14 Nonlinear Optimization Models |
|
|
703 | (34) |
|
14.1 A Production Application: Par, Inc. Revisited |
|
|
704 | (5) |
|
|
704 | (1) |
|
|
705 | (2) |
|
Solving Nonlinear Optimization Models Using Excel Solver |
|
|
707 | (1) |
|
Sensitivity Analysis and Shadow Prices in Nonlinear Models |
|
|
708 | (1) |
|
14.2 Local and Global Optima |
|
|
709 | (5) |
|
Overcoming Local Optima with Excel Solver |
|
|
712 | (2) |
|
|
714 | (1) |
|
14.4 Markowitz Portfolio Model |
|
|
715 | (5) |
|
14.5 Adoption of a New Product: The Bass Forecasting Model |
|
|
720 | (3) |
|
|
723 | (1) |
|
|
724 | (1) |
|
|
724 | (8) |
|
Case Problem: Portfolio Optimization with Transaction Costs |
|
|
732 | (5) |
|
Chapter 15 Decision Analysis |
|
|
737 | (46) |
|
|
739 | (2) |
|
|
740 | (1) |
|
|
740 | (1) |
|
15.2 Decision Analysis Without Probabilities |
|
|
741 | (3) |
|
|
741 | (1) |
|
|
742 | (1) |
|
|
742 | (2) |
|
15.3 Decision Analysis with Probabilities |
|
|
744 | (4) |
|
|
744 | (2) |
|
|
746 | (1) |
|
|
747 | (1) |
|
15.4 Decision Analysis with Sample Information |
|
|
748 | (6) |
|
Expected Value of Sample Information |
|
|
753 | (1) |
|
Expected Value of Perfect Information |
|
|
753 | (1) |
|
15.5 Computing Branch Probabilities with Bayes' Theorem |
|
|
754 | (3) |
|
|
757 | (10) |
|
Utility and Decision Analysis |
|
|
758 | (4) |
|
|
762 | (3) |
|
Exponential Utility Function |
|
|
765 | (2) |
|
|
767 | (1) |
|
|
767 | (2) |
|
|
769 | (11) |
|
Case Problem: Property Purchase Strategy |
|
|
780 | (3) |
Multi-Chapter Case Problems Capital State University Game-Day Magazines |
|
783 | (2) |
Hanover Inc. |
|
785 | (2) |
Appendix A Basics of Excel |
|
787 | (12) |
Appendix B Database Basics with Microsoft Access |
|
799 | (38) |
Appendix C Solutions to Even-Numbered Problems (MindTap Reader) |
|
References |
|
837 | (2) |
Index |
|
839 | |