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Business statistics for competitive advantage with Excel 2007: basics, model building, and cases

Author: Fraser, Cynthia Publisher: Springer, 2009.Language: EnglishDescription: 410 p. : Graphs/Ill. ; 26 cm.ISBN: 9780387744025Type of document: BookBibliography/Index: Includes index
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Book Europe Campus
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Print HA29 .F73 2009
(Browse shelf)
Available 001245639
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Includes index


Business Statistics for Competitive Advantage with Excel 2007 Basics, Model Building, and Cases Contents Preface xvii Chapter 1 Statistics for Decision Making and Competitive Advantage 1.1 1.2 1.3 1.4 1.5 Statistical Competences Translate Into Competitive Advantages Attain Statistical Competences And Competitive Advantage With This Text Follow The Path Toward Statistical Competence and Competitive Advantage Use Excel for Competitive Advantage Statistical Competence Is Satisfying 1 1 1 2 3 3 Chapter 2 Describing Your Data 2.1 Describe Data With Summary Statistics And Histograms Example 2.1 Yankees' Salaries: Is it a Winning Offer? 2.2 Outliers Can Distort The Picture Example 2.2 Executive Compensation: Is the Board's Offer on Target? 2.3 Round Descriptive Statistics 2.4 Central Tendency and Dispersion Describe Data 2.5 Data Is Measured With Quantitative or Categorical Scales 2.6 Continuous Data Tend To Be Normal Example 2.3 Normal SAT Scores 2.7 The Empirical Rule Simplifies Description Example 2.4 Class of '06 SATs: This Class is Normal and Exceptional 2.8 Describe Categorical Variables Graphically: Column and PivotCharts Example 2.5 Who Is Honest and Ethical? 2.9 Descriptive Statistics Depend On The Data Excel 2.1 Produce descriptive statistics and view distributions with histograms Excel 2.2 Sort to produce descriptives without outliers Excel 2.3 Plot a cumulative distribution 5 5 5 7 7 10 11 11 12 12 13 13 15 15 16 17 20 23 Excel 2.4 Find and view distribution percentages with a PivotTable and PivotChart Excel 2.5 Produce a column chart from a PivotChart of a nominal variable Excel Shortcuts at Your Fingertips Lab 2 Descriptive Statistics Assignment 2-1 Procter and Gamble's Global Advertising CASE 2-1 VW Backgrounds 24 27 29 31 33 34 Chapter 3 Hypothesis Tests, Confidence Intervals and Simulation to Infer Population Characteristics and Differences 3.1 Sample Means Are Random Variables Example 3.1 Thirsty on Campus: Is there Sufficient Demand? 3.2 Use Sample Data to Determine Whether Or Not p Is Likely To Exceed A Target 3.3 Confidence Intervals Estimate the Population Mean From A Sample 3.4 Round t to Calculate Approximate 95% Confidence Intervals With Mental Math 3.5 Margin of Error Is Inversely Proportional To Sample Size 3.6 Samples Are Efficient 3.7 Use Monte Carlo Simulation with Sample Statistics To Incorporate Uncertainty and Quantify Implications Of Assumptions 3.8 Determine Whether There Is a Difference Between Two Segments With Student t Example 3.2 Pampers Preemies: Is Income a Useful Base for Segmentation? 3.9 Estimate the Extent of Difference between Two Segments With Student t 3.10 Confidence Intervals Complement Hypothesis Tests 3.11 Estimation of a Population Proportion from a Sample Proportion Example 3.3 Guinea Pigs 3.12 Conditions for Assuming Approximate Normality to Make Confidence Intervals for Proportions 3.13 Conservative Confidence Intervals for a Proportion 3.14 Assess the Difference between Alternate Scenarios or Pairs With Student t Example 3.4 Are "Socially Desirable" Portfolios Undesirable? 3.15 Inference from Sample to Population Excel 3.1 Test the level of a population mean with a one sample t test Excel 3.2 Make a confidence interval for a population mean 35 35 35 38 41 43 43 44 44 48 48 49 50 50 50 53 53 54 55 58 59 60 Excel 3.3 Illustrate population confidence intervals with a clustered column chart Excel 3.4 Conduct a Monte Carlo simulation with Crystal Ball Excel 3.5 Test the difference between two segments with a two sample t test Excel 3.6 Construct a confidence interval for the difference between two segments Excel 3.7 Illustrate the difference between two segment means with a column chart Excel 3.8 Construct a pie chart of shares Excel 3.9 Test the difference in levels between alternate scenarios or pairs with a paired t test Excel 3.10 Construct a confidence interval for the difference between alternate scenarios or pairs Excel Shortcuts at Your Fingertips Lab Practice 3 Inference Lab 3 Inference Assignment 3-1 Bottled Water Possibilities Assignment 3-2 Immigration in the U.S. Assignment 3-3 McLattes Assignment 3-4 A Barbie Duff in Stuff CASE 3-1 Yankees v Marlins.- The Value of a Yankee Uniform CASE 3-2 Gender Pay CASE 3-3 Polaski Vodka: Can a Polish Vodka Stand Up to the Russians? CASE 3-4 American Girl in Starbucks 61 65 69 70 71 72 74 76 78 80 82 83 84 84 85 85 86 86 88 Chapter 4 Quantifying the Influence of Performance Drivers and Forecasting: Regression 4.1 The Simple Linear Regression Equation Describes the Line Relating A Decision Variable to Performance Example 4.1 HitFlix Movie Rentals F Tests the Significance of the Hypothesized Linear Relationship, RSquare Summarizes Its Strength and Standard Error Reflects Forecasting Precision The Population Slope Is Tested And Inferred From Our Sample Analyze Residuals To Learn Whether Assumptions Have Been Met 95% Prediction Intervals Acknowledge That Individual Elements Differ Use Sensitivity Analysis to Explore Alternative Scenarios 91 91 92 4.2 4.3 4.4 4.5 4.6 93 96 98 99 101 4.7 4.8 4.9 4.10 4.11 4.12 95% Conditional Mean Prediction Intervals Of Average Performance Gauge Average Performance Response To A Driver Explanation And Prediction Create A Complete Picture Present Regression Results In Concise Format We Make Assumptions When We Use Linear Regression Correlation Is A Standardized Covariance Example 4.2 HitFlix Movie Rentals Correlation Coefficients Are Key Components Of Regression Slopes Example 4.3 Pampers Correlation Summarizes Linear Association Linear Regression Is Doubly Useful 101 102 103 104 105 105 109 110 113 113 114 118 124 126 128 130 131 133 4.13 4.14 Excel 4.1 Fit a simple linear regression model Excel 4.2 Construct prediction and conditional mean prediction intervals Excel 4.3 Find correlations between variable pairs Excel Shortcuts at Your Fingertips Lab 4 Regression CASE 4-1 GenderPay (B) CASE 4-2 GM Revenue Forecast Assignment 4-1 Impact of Defense Spending on Economic Growth Chapter 5 Marketing Segmentation with Descriptive Statistics, Inference, Hypothesis Tests and Regression 5.1 5.2 CASE 5-1 Segmentation of the Market for Preemie Diapers Guide to Effective PowerPoint Presentations and Writing Memos that your Audience will Read Write Memos that Encourage Your Audience to Read and Use Results MEMO Re: Importance of Fit Drives Trial Intention 135 135 145 147 148 Chapter 6 Finance Application: Portfolio Analysis with a Market Index as a Leading Indicator in Simple Linear Regression 6.1 6.2 6.3 Rates of Return Reflect Expected Growth of Stock Prices Example 6.1 Goldman Sachs and Yahoo Returns Investors Trade Off Risk And Return Beta Measures Risk Example 6.2 Four diverse stocks 149 149 149 152 152 153 6.4 A Portfolio's Expected Return, Risk and Beta Are Weighted Averages of Individual Stocks Example 6.3 Four Alternate Portfolios 6.5 Better Portfolios Define The Efficient Frontier MEMO Re: Recommended Portfolios Include Lockheed Martin and Apple 6.6 Portfolio Risk Depends On the Covariances between Individual Stocks' Rates of Return and The Market Rate Of Return Excel 6.1 Estimate portfolio expected rate of return and risk Excel 6.2 Plot return by risk to identify dominant portfolios and the Efficient Frontier Assignment 6-1 Individual Stocks' Beta Estimates Assignment 6-2 Expected Returns and Beta Estimates of Alternate Portfolios Assignment 6-3 Portfolio Comparison 158 158 161 162 163 164 166 169 169 170 Chapter 7 Association between Two Categorical Variables: Contingency Analysis with Chi Square 7.1 When Conditional Probabilities Differ From Joint Probabilities, There Is Evidence of Association Example 7.1 Recruiting Stars 7.2 Chi Square Tests Association between Two Categorical Variables 7.3 Chi Square Is Unreliable If Cell Counts Are Sparse 7.4 Simpson's Paradox Can Mislead Example 7.2 American Cars MEMO Re: Country of Manufacture Does Not Affect Older Buyers' Choices 7.5 Contingency Analysis Is Demanding 7.6 Contingency Analysis Is Quick, Easy, and Readily Understood Excel 7.1 Construct crosstabulations and assess association between categorical variables with PivotTables and PivotCharts Excel 7.2 Use chi square to test association Excel 7.3 Conduct contingency analysis with summary data Excel Shortcuts at Your Fingertips Assignment 7-1 747s and Jets Assignment 7-2 Fit Matters Assignment 7-3 Allied Airlines CASE 7-1 Hybrids for American Car CASE 7-2 Tony's GREAT Advertising 171 171 172 174 175 177 177 183 184 184 185 187 190 193 195 195 196 197 198 Chapter 8 Building Multiple Regression Models 8.1 8.2 Multiple Regression Models Identify Drivers and Forecast Use Your Logic to Choose Model Components Example 8.1 Sakura Motors Quest for Cleaner Cars 8.3 Multicollinear Variables Are Likely When Few Variable Combinations Are Popular In a Sample 8.4 F Tests the Joint Significance of the Set of Independent Variables 8.5 Insignificant Parameter Estimates Signal Multicollinearity 8.6 Combine or Eliminate Collinear Predictors 8.7 Partial F Tests the Significance of Changes in Model Power 8.8 Sensitivity Analysis Quantifies the Marginal Impact Of Drivers MEMO Re: Light, responsive, fuel efficient cars with smaller engines are cleanest 8.9 Model Building Begins With Logic and Considers Multicollinearity Excel 8.1 Build and fit a multiple linear regression model Excel 8.2 Use sensitivity analysis to compare the marginal impacts of drivers Lab Practice 8 Lab 8 Model Building with Multiple Regression Assignment 8-1 201 201 201 202 203 204 205 205 207 211 214 215 216 221 228 230 233 Chapter 9 Model Building and Forecasting with Multicollinear Time Series 9.1 Time Series Models Include Decision Variables, External Forces, Leading Indicators, And Inertia Example 9.1 Home Depot Revenues Indicators of Economic Prosperity Lead Business Performance Inertia from Loyal Customers Drives Performance Compare Scatterplots across Time to Choose Length of Lags For Drivers of Delayed Response: Visual Inspection Hide the Two Most Recent Datapoints to Validate a Time Series Model Correlations Guide Choice of Lags The Durbin Watson Statistics Identifies Autocorrelation Assess Residuals to Identify Unaccounted For Trend or Cycles Forecast the Recent, Hidden Points to Assess Predictive Validity 235 237 238 238 238 239 241 241 242 243 246 9.2 9.3 9.4 9.5 9.6 9.7 9.8 9.9 9.10 Add the Most Recent Datapoints to Recalibrate 246 248 249 250 266 268 270 272 MEMO Re: Revenue Decline Forecast Following New Home Sales Downturn 9.11 Inertia and Leading Indicator Components Are Powerful Drivers and Often Multicollinear Excel 9.1 Build and fit a multiple regression model with multicollinear time series Chapter 9 Lab: HP Revenue Forecast CASE 9-1 Dell: Overcoming Roadblocks to Growth CASE 9-2 Mattel Revenues Following the Recalls CASE 9-3 Starbucks in China Chapter 10 Indicator Variables 10.1 Indicators Modify the Intercept to Account for Segment Differences Example 10.1 Hybrid Fuel Economy Example 10.2 Yankees v Marlins Salaries 10.2 Indicators Estimate the Value of Product Attributes Example 10.3 New PDA Design 10.3 Indicators Quantify Seasonality in Time Series Example 10.4 Tyson's Farm Worker Forecast MEMO Re: Declining Supply of Self Employed Agriculture Workers 10.4 Indicators Add Structural Shifts in Time Series Example 10.5 Leadership Changes Influence US Imports by India 10.5 Indicators Allow Comparison of Segments and Scenarios And Quantify Structural Shifts Excel 10.1 Use indicators to find part worth utilities and attribute importances from conjoint analysis data Excel 10.2 Add indicator variables to account for segment differences or structural shifts Lab Practice 10 Assignment 10-1 Conjoint Analysis of PDA Preferences CASE 10-1 Modeling Growth: Procter and Gamble Quarterly Revenues CASE 10-2 Store24 (A): Managing Employee Retention and Store24 (B): Service Quality and Employee Skills 275 275 275 276 278 278 283 283 290 291 291 294 295 299 306 308 309 312 Chapter 11 Nonlinear Multiple Regression Models Consider a Nonlinear Model When Response Is Not Constant Tukey's Ladder of Powers Rescaling y Builds in Synergies Example 11.1 Executive Compensation 11.4 Sensitivity Analysis Reveals the Relative Strength of Drivers MEMO Re: Executive Compensation Driven by Firm Performance and Age 11.5 Gains from Nonlinear Rescaling Are Significant 11.6 Nonlinear Models Offer the Promise of Better Fit and Better Behavior Excel 11.1 Rescale to build and fit nonlinear regression models with linear regression Excel 11.2 Consider synergies in sensitivity analysis with a nonlinear model Lab Practice 11 CASE 11-1 Global Emissions Segmentation: Markets Where Hybrids Might Have Particular Appeal 11.1 11.2 11.3 313 313 313 315 315 320 323 324 325 326 334 338 339 Chapter 12 Indicator Interactions for Structural Differences or Changes in Response 12.1 Indicator Interaction with a Continuous Influence Alters Its Partial Slope Example 12.1 Gender Discrimination at Slams Club MEMO Re: Women are Paid More than Men at Slam's Club Example 12.2 Car Sales in China 12.2 Indicator Interactions Capture Segment Differences or Structural Differences in Response Excel 12.1 Add indicator interactions to capture segment differences or structural differences in response Lab Practice 12 CASE 12-1 Explain and Forecast Defense Spending for Rolls-Royce CASE 12-2 Haier's U.S. Refrigerator Strategy 343 343 344 350 351 358 359 370 372 375 Chapter 13 Logit Regression for Bounded Responses 13.1 Rescaling Probabilities or Shares to Odds Improves Model Validity Example 13.1 The Import Challenge MEMO Re: Fuel Efficiency Drives Hybrid Owner Satisfaction Example 13.2 Presidential Approval Proportion 377 377 378 385 386 Logit Models Provide the Means to Build Valid Models of Shares And Proportions Excel 13.1 Rescale a limited dependent variable to logits Assignment 13-1 Big Drug Co Scripts CASE 13-1 Alltel's Plans to Capture Share in the Cell Phone Service Market CASE 13-2 Pilgrim Bank (A): Profitability and Pilgrim Bank (B): Customer Retention 13.2 390 391 399 400 403 Index 405

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