| Preface |
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xiii | |
| Acknowledgements |
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xvi | |
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Chapter 1 Statistics for Decision Making and Competitive Advantage |
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1 | (4) |
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1.1 Statistical Competences Translate into Competitive Advantages |
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1 | (1) |
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1.2 The Path Toward Statistical Competence and Competitive Advantage |
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2 | (1) |
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1.3 Use Excel for Competitive Advantage |
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2 | (1) |
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1.4 Statistical Competence Is Powerful and Yours |
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3 | (2) |
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Chapter 2 Describing Your Data |
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5 | (42) |
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2.1 Describe Data with Summary Statistics and Histograms |
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5 | (4) |
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2.2 Round Descriptive Statistics |
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9 | (1) |
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2.3 Share the Story That Your Graphics Illustrate |
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9 | (1) |
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2.4 Data Is Measured with Quantitative or Categorical Scales |
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10 | (1) |
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2.5 Continuous Data Are Sometimes Normal |
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11 | (1) |
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2.6 The Empirical Rule Simplifies Description |
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12 | (1) |
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2.7 Outliers Can Distort the Picture |
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13 | (1) |
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2.8 Central Tendency, Dispersion and Skewness Describe Data |
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14 | (1) |
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2.9 Describe Categorical Variables Graphically |
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14 | (1) |
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2.10 Descriptive Statistics Depend On the Data and Rely on Your Packaging |
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15 | (32) |
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Excel 2.1 Produce Descriptive Statistics |
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17 | (8) |
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Excel 2.2 Sort to Produce Descriptives Without Outliers |
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25 | (1) |
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Excel 2.3 Plot a Cumulative Distribution |
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26 | (3) |
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Excel 2.4 Use a PivotTable to Sort by Industry |
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29 | (2) |
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Excel 2.5 Produce a Column Chart of a Nominal Variable |
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31 | (3) |
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Excel Shortcuts Used in
Chapter 2 |
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34 | (3) |
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Significant Digits Guidelines |
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37 | (2) |
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39 | (2) |
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Assignment 2.1 Procter & Gamble's Global Advertising |
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41 | (1) |
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Assignment 2.2 Best Practices Survey |
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42 | (1) |
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Assignment 2.3 Shortcut Challenge |
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43 | (1) |
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43 | (1) |
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Case 2.2 Global Smelter Costs at Alcoa |
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43 | (4) |
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Chapter 3 Hypothesis Tests, Confidence Intervals to Infer Population Characteristics and Differences |
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47 | (54) |
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3.1 Sample Means Are Random Variables |
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47 | (4) |
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3.2 Infer Whether a Population Mean Exceeds a Target |
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51 | (2) |
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3.3 Critical t Provides a Benchmark |
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53 | (1) |
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3.4 Confidence Intervals Estimate the Population Mean |
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54 | (2) |
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3.5 Calculate Approximate Confidence Intervals with Mental Math |
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56 | (1) |
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3.6 Margin of Error Is Inversely Proportional to Sample Size |
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57 | (1) |
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3.7 Determine Whether Two Segments Differ with Student t |
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58 | (4) |
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3.8 Estimate the Extent of Difference Between Two Segments |
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62 | (1) |
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3.9 Estimate a Population Proportion from a Sample Proportion |
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63 | (2) |
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3.10 Conditions for Assuming Approximate Normality |
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65 | (1) |
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3.11 Conservative Confidence Intervals for a proportion |
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65 | (2) |
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3.12 Assess the Difference Between Alternate Scenarios or Pairs |
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67 | (4) |
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3.13 Inference from Sample to Population |
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71 | (30) |
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Excel 3.1 Test the Level of a Population Mean with a One Sample t test |
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73 | (1) |
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Excel 3.2 Make a Confidence Interval for a Population Mean |
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74 | (1) |
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Excel 3.3 Illustrate Confidence Intervals with Column Charts |
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75 | (5) |
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Excel 3.4 Test the Difference Between Two Segment Means with a Two Sample t test |
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80 | (1) |
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Excel 3.5 Construct a Confidence Interval for the Difference Between Two Segments |
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81 | (3) |
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Excel 3.6 Illustrate the Difference Between Two Segment Means with a Column Chart |
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84 | (1) |
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Excel 3.7 Construct a Pie Chart of Shares |
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85 | (2) |
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Excel 3.8 Test the Difference in Between Alternate Scenarios or Pairs with a Paired t test |
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87 | (1) |
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Excel 3.9 Construct a Confidence Interval for the Difference Between Alternate Scenarios or Pairs |
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88 | (1) |
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89 | (1) |
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Cingular's Position in the Cell Phone Service Market |
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89 | (1) |
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Value of a Nationals Uniform |
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89 | (1) |
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Confidence in Chinese Imports |
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90 | (1) |
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Lab 3.2 Inference: Dell Smartphone Plans |
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91 | (2) |
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Assignment 3.1 The Marriott Difference |
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93 | (1) |
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Assignment 3.2 Immigration in the U.S. |
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93 | (1) |
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94 | (1) |
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Assignment 3.4 A Barbie Duff in Stuff |
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94 | (1) |
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Assignment 3.5 Alcoa Smelters |
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94 | (3) |
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Case 3.1 Yankees v Marlins: The Value of a Yankee Uniform |
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97 | (1) |
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97 | (1) |
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Case 3.3 Polaski Vodka: Can a Polish Vodka Stand Up to the Russians? |
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98 | (3) |
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Chapter 4 Simulation to Infer Future Performance Levels Given Assumptions |
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101 | (36) |
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4.1 Specify Assumptions Concerning Future Performance Drivers |
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101 | (4) |
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4.2 Compare Best and Worst Case Performance Outcomes |
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105 | (1) |
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4.3 Spread and Shape Assumptions Influence Possible Outcomes |
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106 | (1) |
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4.4 Monte Carlo Simulation of the Distribution of Performance Outcomes |
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107 | (5) |
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4.5 Monte Carlo Simulation Reveals Possible Outcomes Given Assumptions |
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112 | (25) |
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Excel 4.1 Set Up a Spreadsheet to Link Simulated Performance Components |
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113 | (2) |
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Excel 4.2 View a Simulated Sample with a Histogram |
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115 | (16) |
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Lab 4 Inference: Dell Android Smartphone Plans |
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131 | (2) |
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Case 4.1 American Girl in Starbucks |
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133 | (1) |
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Case 4.2 Can Whole Foods Hold On? |
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133 | (2) |
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Case 4.3 Chipotle's Ambitions to Triple Share of Top 100 Chain Sales in the Recession Rebound |
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135 | (2) |
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Chapter 5 Simple Regression for Long Range Forecasts |
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137 | (38) |
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5.1 The Simple Linear Regression Equation Describes the Line Relating an Independent Variable to Performance |
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138 | (1) |
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5.2 Hide the Two Most Recent Datapoints to Validate a Time Series Model |
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138 | (3) |
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5.3 Test and Infer the Slope |
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141 | (3) |
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5.4 The Regression Standard Error Reflects Model Precision |
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144 | (1) |
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5.5 Prediction Intervals Estimate Average Population Response |
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145 | (1) |
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5.6 Rsquare Summarizes Strength of the Hypothesized Linear Relationship and F Tests Its Significance |
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146 | (3) |
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5.7 Assess Residuals to Learn Whether Assumptions Are Met |
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149 | (2) |
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5.8 Recalibrate to Update a Valid Model |
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151 | (2) |
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5.9 Present Regression Results in Concise Format |
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153 | (1) |
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5.10 Assumptions We Make When We Use Linear Regression |
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154 | (1) |
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5.11 Correlation Reflects Linear Association |
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154 | (3) |
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5.12 Correlation Coefficients Are Key Components of Regression Slopes |
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157 | (1) |
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5.13 Correlation Complements Regression |
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158 | (1) |
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5.14 Linear Regression Is Doubly Useful |
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158 | (17) |
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Excel 5.1 Build a Simple Linear Regression Model |
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159 | (1) |
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Excel 5.2 Assess Residuals |
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160 | (2) |
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Excel 5.3 Construct Prediction Intervals to Validate |
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162 | (3) |
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Excel 5.4 Recalibrate and Present Fit and Forecast in a Scatterplot |
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165 | (5) |
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Excel 5.5 Find Correlations Between Variable Pairs |
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170 | (1) |
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Lab 5 Forecast Concha y Toro Exports to Latin America |
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171 | (2) |
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Assignment 5.1 Forecast Concha y Toro Exports to Europe and Asia |
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173 | (2) |
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Chapter 6 Consolidating Multiple Naive Forecasts with Monte Carlo |
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175 | (12) |
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6.1 Use Monte Carlo to Integrate Multiple Uncertain Naive Forecasts |
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176 | (1) |
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6.2 Monte Carlo Offers Likely Possibilities from Consolidated Multiple Naive Forecasts |
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177 | (10) |
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Excel 6.1 Use Monte Carlo to Produce a 95% Prediction Interval of Consolidated Possibilities from Multiple Naive Forecasts |
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178 | (3) |
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Lab 6 Forecast Concha y Toro Consolidated Exports to the New World |
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181 | (2) |
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Assignment 6 Forecast Concha y Toro Consolidated Exports Worldwide |
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183 | (2) |
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Case 6 Can Arcos Dorados Hold On? |
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185 | (2) |
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Chapter 7 Presenting Statistical Analysis Results to Management |
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187 | (20) |
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7.1 Use PowerPoints to Present Statistical Results for Competitive Advantage |
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187 | (7) |
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7.2 Write Memos that Encourage Your Audience to Read and Use Results |
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194 | (13) |
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MEMO Re: Worldwide exports forecast to grow modestly through 2016 |
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196 | (3) |
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Case 7 Segmentation of the Market for Preemie Diapers |
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199 | (1) |
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The Market for Preemie Diapers |
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200 | (1) |
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200 | (1) |
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201 | (1) |
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202 | (5) |
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Chapter 8 Finance Application: Portfolio Analysis with a Market Index as a Leading Indicator in Simple Linear Regression |
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207 | (22) |
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8.1 Rates of Return Reflect Expected Growth of Stock Prices |
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207 | (2) |
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8.2 Investors Trade Off Risk and Return |
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209 | (1) |
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209 | (4) |
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8.4 A Portfolio Expected Return, Risk and Beta Are Weighted Averages of Individual Stocks |
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213 | (1) |
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8.5 Better Portfolios Define the Efficient Frontier |
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214 | (3) |
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MEMO Re: Recommended Portfolio is Diversified |
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216 | (1) |
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8.6 Portfolio Risk Depends on Correlations with the Market and Stock Variability |
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217 | (12) |
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Excel 8.1 Estimate Portfolio Expected Rate of Return and Risk |
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218 | (2) |
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Excel 8.2 Plot Return by Risk to Identify Dominant Portfolios and the Efficient Frontier |
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220 | (5) |
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Lab 8 Portfolio Risk and Return |
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225 | (2) |
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Assignment 8 Portfolio Risk and Return |
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227 | (2) |
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Chapter 9 Association Between Two Categorical Variables: Contingency Analysis with Chi Square |
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229 | (30) |
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9.1 When Conditional Probabilities Differ from Joint Probabilities, There Is Evidence of Association |
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229 | (2) |
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9.2 Chi Square Tests Association Between Two Categorical Variables |
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231 | (2) |
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9.3 Chi Square Is Unreliable If Cell Counts Are Sparse |
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233 | (2) |
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9.4 Simpson's Paradox Can Mislead |
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235 | (6) |
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MEMO Re.: Country of Assembly Does Not Affect Older Buyers' Choices |
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240 | (1) |
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9.5 Contingency Analysis Is Demanding |
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241 | (1) |
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9.6 Contingency Analysis Is Quick, Easy, and Readily Understood |
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241 | (18) |
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Excel 9.1 Construct Crosstabulations and Assess Association Between Categorical Variables with PivotTables and PivotCharts |
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242 | (2) |
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Excel 9.2 Use Chi Square to Test Association |
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244 | (2) |
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Excel 9.3 Conduct Contingency Analysis with Summary Data |
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246 | (5) |
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251 | (2) |
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Assignment 9.1 Wine Preferences by Global Region |
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253 | (1) |
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Assignment 9.2 Fit Matters |
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253 | (1) |
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Assignment 9.3 Netbooks in Color |
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253 | (2) |
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Case 9.1 Hybrids for American Car |
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255 | (1) |
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Case 9.2 Tony's GREAT Advertising |
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255 | (1) |
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Case 9.3 Hybrid Motivations |
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256 | (3) |
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Chapter 10 Building Multiple Regression Models |
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259 | (44) |
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10.1 Explanatory Multiple Regression Models Identify Drivers and Forecast |
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259 | (1) |
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10.2 Use Your Logic to Choose Model Components |
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260 | (3) |
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10.3 Multicollinear Variables Are Likely When Few Variable Combinations Are Popular in a Sample |
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263 | (1) |
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10.4 F Tests the Joint Significance of the Set of Independent Variables |
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263 | (2) |
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10.5 Insignificant Parameter Estimates Signal Multicollinearity |
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265 | (2) |
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10.6 Combine or Eliminate Collinear Predictors |
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267 | (5) |
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10.7 Decide Whether Insignificant Drivers Matter |
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272 | (2) |
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10.8 Sensitivity Analysis Quantifies the Marginal Impact of Drivers |
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274 | (4) |
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MEMO Re: Light, responsive, fuel efficient cars with smaller engines are cleanest |
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277 | (1) |
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10.9 Model Building Begins With Logic and Considers Multicollinearity |
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278 | (25) |
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Excel 10.1 Build and Fit a Multiple Linear Regression Model |
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279 | (5) |
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Excel 10.2 Use Sensitivity Analysis to Compare the Marginal Impacts of Drivers |
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284 | (9) |
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Lab 10 Model Building with Multiple Regression: Pricing Dell's Navigreat |
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293 | (4) |
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Assignment 10.1 Sakura Motor's Quest for Fuel Efficiency |
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297 | (2) |
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Case 10 1 Fast Food Nations |
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299 | (1) |
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Case 10.2 Chasing Chipotle's Success |
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299 | (2) |
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Case 10.3 Costco's Warehouse Location Scheme |
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301 | (2) |
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Chapter 11 Indicator Variables |
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303 | (36) |
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11.1 Indicators Modify the Intercept to Account for Segment Differences |
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303 | (3) |
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11.2 Indicators Estimate the Value of Product Attributes |
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306 | (4) |
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11.3 Indicators Estimate Segment Mean Differences |
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310 | (4) |
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11.4 Analysis of Variance Offers an Alternative to Regression with Indicators |
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314 | (4) |
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11.5 ANOVA and Regression with Indicators Are Complementary Substitutes |
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318 | (1) |
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11.6 ANOVA and Regression in Excel |
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319 | (20) |
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Excel 11.1 Use Indicators to Find Part Worths and Attribute Importances |
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320 | (5) |
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Excel 11.2 Use ANOVA to Test Equivalence of Mean Interest Ratings |
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325 | (4) |
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Lab 11.1 Revere Bank Profits |
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329 | (2) |
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Lab 11.2 Power PowerPoints |
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331 | (2) |
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Lab 11.3 ANOVA and Regression with Indicators: Powerful PowerPoints |
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333 | (2) |
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Assignment 11 Forecasting Chipotle Revenue in the Long Range |
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335 | (2) |
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Case 11 Store24 (A): Managing Employee Retention and Store24 (B): Service Quality and Employee Skills |
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337 | (2) |
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Chapter 12 Model Building and Forecasting with Multicollinear Time Series |
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339 | (56) |
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12.1 Time Series Models Include Decision Variables, External Forces, and Leading Indicators |
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342 | (1) |
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12.2 Indicators of Economic Prosperity Lead Business Performance |
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343 | (1) |
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12.3 Hide the Two Most Recent Datapoints to Validate a Time Series Model |
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343 | (1) |
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12.4 Compare Scatterplots to Choose Driver Lags: Visual Inspection |
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344 | (3) |
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12.5 Assess Residuals to Identify Unaccounted for Trend or Cycles |
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347 | (5) |
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12.6 Forecast the Recent, Hidden Points to Assess Predictive Validity |
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352 | (1) |
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12.7 Add the Most Recent Datapoints to Recalibrate |
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352 | (2) |
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12.8 Compare Part Worths to Assess Driver Importances |
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354 | (2) |
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MEMO Re: Slow, Stable Growth Forecast in Next Four Quarters |
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355 | (1) |
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12.9 Leading Indicator Components Are Powerful Drivers and Often Multicollinear |
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356 | (39) |
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Excel 12.1 Build and Fit a Multiple Regression Model with Multicollinear Time Series |
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358 | (2) |
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Excel 12.2 Create Potential Driver Lags |
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360 | (2) |
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Excel 12.3 Select the Most Promising Driver |
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362 | (2) |
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Excel 12.4 Plot Residuals to Identify Unaccounted for Trend, Cycles, or Seasonality and Assess Autocorrelation |
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364 | (7) |
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Excel 12.5 Test the Model's Forecasting Validity |
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371 | (2) |
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Excel 12.6 Recalibrate to Forecast |
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373 | (1) |
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Excel 12.7 Illustrate the Fit and Forecast |
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374 | (1) |
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Excel 12.8 Assess the Impact of Drivers |
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375 | (4) |
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Lab 12.1 What Is Driving WFM Revenues... and What Revenues Can WFM Expect Next Year? |
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379 | (4) |
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Lab 12.2 What Is Driving WFM Revenues... and What Revenues Can WFM Expect Next Year? |
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383 | (2) |
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Case 12 McDonalds Revenue Drivers and Future Prospects |
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385 | (5) |
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Case 12.1 Chipotle Quarterly Revenues Model and Forecast |
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390 | (5) |
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Chapter 13 Nonlinear Multiple Regression Models |
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395 | (52) |
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13.1 Consider a Nonlinear Model When Response Is Not Constant |
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395 | (1) |
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13.2 Skewness Signals Nonlinear Response |
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395 | (4) |
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13.3 Rescalingy Builds in Interactions |
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399 | (5) |
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13.4 The Margin of Error Is Not Constant with a Nonlinear Model |
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404 | (1) |
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13.5 Sensitivity Analysis Enables Scenario Comparisons |
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404 | (6) |
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13.6 Nonlinear Models Inform Monte Carlo Simulation |
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410 | (1) |
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13.7 Gains from Nonlinear Rescaling Are Significant |
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411 | (1) |
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13.8 Nonlinear Models Offer the Promise of Better Fit and Better Behavior |
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412 | (35) |
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Excel 13.1 Rescale to Build and Fit Nonlinear Regression Models with Linear Regression |
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413 | (14) |
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Excel 13.2 Compare Scenarios with Sensitivity Analysis |
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427 | (4) |
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Excel 13.3 Use Nonlinear Regression Estimates with Monte Carlo Simulation |
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431 | (6) |
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Lab 13.1 Nonlinear Forecasting LAN Airlines Passenger Revenues: Building the Model |
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437 | (2) |
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Lab 13.2 Nonlinear Forecasting LAN Airlines Passenger Revenues: Describe the Model |
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439 | (2) |
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Lab 13.3 Forecasting with Uncertain Drivers: LAN Passenger Revenues |
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441 | (2) |
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Assignment 13.1 Billionaires in 2020 |
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443 | (2) |
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Assignment 13.2 Primary Aluminum Production in 2020 |
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445 | (2) |
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Chapter 14 Nonlinear Explanatory Multiple Regression Models |
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447 | (26) |
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14.1 Sensitivity Analysis Reveals the Relative Strength of Drivers |
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451 | (2) |
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14.2 Sensitivity Analysis with Nonlinear Models Reveals Interactions |
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453 | (20) |
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Excel 14.1 Build a Nonlinear Model with Cross Sectional Data |
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454 | (4) |
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Excel 14.2 Sensitivity Analysis of Scenarios and Driver Influence |
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458 | (5) |
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Lab 14 Mattel's Acquisition of Radica |
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463 | (2) |
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Assignment 14 Identifying Promising Global Markets |
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465 | (2) |
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Case 14.1 Promising Global Markets for EVs |
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467 | (2) |
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Case 14.2 Chasing Whole Foods' Success |
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469 | (2) |
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Case 14.3 Promising Global Markets for Water Purification |
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471 | (2) |
| Index |
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473 | |
