Business statistics for competitive advantage with Excel 2010: basics, model building, and cases
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| Language: | English |
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New York [u.a.]
Springer
2012
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| Edition: | 2. ed. |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XVI, 469 S. |
| ISBN: | 9781441998569 |
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| 245 | 1 | 0 | |a Business statistics for competitive advantage with Excel 2010 |b basics, model building, and cases |c Cynthia Fraser |
| 250 | |a 2. ed. | ||
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| adam_text | Titel: Business statistics for competitive advantage with Excel 2010
Autor: Fraser, Cynthia
Jahr: 2012
Contents
Preface...........................................................................................................................................xv
Chapter 1 Statistics for Decision Making and Competitive Advantage................................1
1.1 Statistical Competences Translate into Competitive Advantages.........................................1
1.2 The Path toward Statistical Competence and Competitive Advantage.................................2
1.3 Use Excel for Competitive Advantage..................................................................................2
1 4 Statistical Competence Is Powerful and Yours.....................................................................3
Chapter 2 Describing Your Data..............................................................................................5
2.1 Describe Data with Summary Statistics and Histograms......................................................5
Example 2.1 Yankees Salaries: Is It a Winning Offer? .......................................................5
2.2 Outliers Can Distort the Picture............................................................................................8
Example 2.2 Executive Compensation: Is the Board s Offer on Target? .............................8
2.3 Round Descriptive Statistics...............................................................................................11
2.4 Share the Story that Your Graphics Illustrate.....................................................................11
2.5 Central Tendency, Dispersion, and Skewness Describe Data.............................................11
2.6 Data Are Measured with Quantitative or Categorical Scales..............................................11
2.7 Continuous Data Are Sometimes Normal..........................................................................13
Example 2.3 Normal SAT Scores ........................................................................................13
2.8 The Empirical Rule Simplifies Description........................................................................13
Example 2.4 Class of 10 SATs: This Class Is Normal and Exceptional............................14
2.9 Describe Categorical Variables Graphically: Column and PivotCharts ............................15
Example 2.5 Who Is Honest and Ethical? ..........................................................................15
2.10 Descriptive Statistics Depend on the Data and Your Packaging ........................................16
Excel 2.1 Produce Descriptive Statistics and Histograms ..................................................................17
Executive Compensation.....................................................................................................17
Excel 2.2 Sort to Produce Descriptives Without Outliers...................................................................21
Excel 2.3 Plot a Cumulative Distribution ...........................................................................................23
Excel 2.4 Find and View Distribution Percentages with a PivotChart ...............................................25
Class of 10 Math SATs ......................................................................................................25
Excel 2.5 Produce a Column Chart of a Nominal Variable ................................................................29
Excel Shortcuts at Your Fingertips by Shortcut Key..........................................................33
Significant Digits Guidelines..............................................................................................39
Lab 2 Descriptive Statistics ................................................................................................41
A Typical Executive s Compensation ................................................................................41
Hollywood Politics .............................................................................................................42
Assignment 2-1 Procter Gamble s Global Advertising ..................................................43
Assignment 2-2 Best Practices Survey ...............................................................................43
Assignment 2-3 Shortcut Challenge ...................................................................................44
CASE 2-1 VW Backgrounds ..............................................................................................44
Chapter 3 Hypothesis Tests, Confidence Intervals, and Simulation to Infer
Population Characteristics and Differences .......................................................45
3.1 Sample Means Are Random Variables ...............................................................................45
Example 3.1 Thirsty on Campus: Is There Sufficient Demand? .........................................45
3.2 Infer Whether a Population Mean Exceeds a Target ..........................................................50
3.3 Confidence Intervals Estimate the Population Mean..........................................................52
3.4 Calculate Approximate Confidence Intervals with Mental Math .......................................54
3.5 Margin of Error Is Inversely Proportional To Sample Size ................................................55
3.6 Samples Are Efficient.........................................................................................................56
3.7 Use Monte Carlo Simulation Samples to Incorporate Uncertainty
and Quantify Implications of Assumptions ........................................................................57
3.8 Determine Whether Two Segments Differ with Student t.................................................60
Example 3.2 SmartScribe: Is Income a Useful Base for Segmentation ? ............................60
3.9 Estimate the Extent of Difference Between Two Segments ...............................................62
3.10 Confidence Intervals Complement Hypothesis Tests .........................................................64
3.11 Estimate a Population Proportion from a Sample Proportion.............................................64
Example 3.3 Guinea Pigs....................................................................................................64
3.12 Conditions for Assuming Approximate Normality.............................................................66
3.13 Conservative Confidence Intervals for a Proportion...........................................................66
3.14 Assess the Difference Between Alternate Scenarios or Pairs .............................................68
Example 3.4 Are Socially Desirable Portfolios Undesirable? .......................................69
3.15 Inference from Sample to Population .................................................................................72
Excel 3.1 Test the Level of a Population Mean with a One Sample /Test.........................................74
Thirsty on Campus..............................................................................................................74
Excel 3.2 Make a Confidence Interval for a Population Mean ...........................................................75
Excel 3.3 Illustrate Confidence Intervals with Column Charts...........................................................76
T-Mobile s Service .............................................................................................................76
Excel 3.4 Conduct a Monte Carlo Simulation ....................................................................................80
Excel 3.5 Test the Difference Between Two Segment Means with a Two Sample t Test..................86
Pampers Preemies ...............................................................................................................86
Excel 3.6 Construct a Confidence Interval for the Difference Between Two Segments ....................87
Excel 3.7 Illustrate the Difference Between Two Segment Means with a Column Chart..................88
Excel 3.8 Construct a Pie Chart of Shares ..........................................................................................90
Moral Acceptance of Medical Testing on Animals ............................................................90
Excel 3.9 Test the Difference in Between Alternate Scenarios or Pairs with a Paired / Test..............93
Difference Between Conventional and Socially Desirable Portfolio Ratings.....................93
Excel 3.10 Construct a Confidence Interval for the Difference Between Alternate
Scenarios or Pairs.................................................................................................................94
Lab Practice 3 Inference......................................................................................................97
Cingular s Position in the Cell Phone Service Market........................................................97
Value of a Nationals Uniform..............................................................................................97
Extra Value of a Phillies Uniform.......................................................................................98
Confidence in Chinese Imports...........................................................................................98
Lab 3 Inference: Dell PDA Plans.........................................................................................99
Assignment 3-lThe Marriott Difference............................................................................101
Assignment 3-2 Bottled Water Possibilities......................................................................101
Assignment 3-3 Immigration in the United States.............................................................102
Assignment 3-4 McLattes..................................................................................................103
Assignment 3-5 A Barbie Duff in Stuff.............................................................................103
CASE 3-1 Yankees Versus Marlins: The Value of a Yankee Uniform.............................103
CASE 3-2 Gender Pay.......................................................................................................104
CASE 3-3 Polaski Vodka: Can a Polish Vodka Stand Up to the Russians?......................105
CASE 3-4 American Girl in Starbucks..............................................................................107
Chapter 4 Quantifying the Influence of Performance Drivers
and Forecasting: Regression...............................................................................109
4.1 The Simple Linear Regression Equation Describes the Line...................................................
Relating a Decision Variable to Performance....................................................................109
Example 4.1 HitFlix Movie Rentals...................................................................................110
4.2 F Tests Significance of the Hypothesized Linear Relationship R Square
Summarizes Its Strength and Standard Error Reflects Forecasting Precision...................111
4.3 Test and Infer the Slope.....................................................................................................116
4.4 Analyze Residuals to Learn Whether Assumptions Are Met............................................118
4.5 Prediction Intervals Estimate Average Response..............................................................120
4.6 Use Sensitivity Analysis to Explore Alternative Scenarios...............................................121
4.7 Explanation and Prediction Create a Complete Picture.....................................................122
4.8 Present Regression Results in Concise Format..................................................................123
4.9 Assumptions We Make When We Use Linear Regression................................................123
4.10 Correlation Reflects Linear Association............................................................................124
Example 4.2 HitFlix Movie Rentals...................................................................................124
4.11 Correlation Coefficients Are Key Components of Regression Slopes..............................128
Example 4.3 Pampers........................................................................................................129
4.12 Correlation Complements Regression...............................................................................131
4.13 Linear Regression Is Doubly Useful..................................................................................132
Excel 4.1 Build a Simple Linear Regression Model: Impact of Titles Offered on HitFlix
Movie Rental Revenues.....................................................................................................133
Excel 4.2 Construct Prediction Intervals............................................................................................135
Excel 4.3 Find Correlations Between Variable Pairs.........................................................................142
Lab Practice 4 Oil Price Forecast.......................................................................................145
Lab 4 Simple Regression Dell Slimmer PDA....................................................................147
CASE 4-1 GenderPay (B).................................................................................................149
CASE 4-2 GM Revenue Forecast......................................................................................149
Assignment 4-1 Impact of Defense Spending on Economic Growth................................151
Chapter 5 Market Simulation and Segmentation with Descriptive Statistics,
Inference, Hypothesis Tests, and Regression.....................................................153
5.1 CASE 5-1 Simulation and Segmentation of the Market for Preemie Diapers...................153
5.2 Use PowerPoints to Present Statistical Results for Competitive Advantage.....................164
5.3 Write Memos That Encourage Your Audience to Read and Use Results..........................171
MEMO Re: Importance of Fit Drives Trial Intention........................................................173
Chapter 6 Finance Application: Portfolio Analysis with a Market Index
as a Leading Indicator in Simple Linear Regression........................................175
6.1 Rates of Return Reflect Expected Growth of Stock Prices................................................175
Example 6.1 General Electric and Apple Returns.............................................................175
6.2 Investors Trade Off Risk and Return.................................................................................177
6.3 Beta Measures Risk...........................................................................................................177
6.4 A Portfolio Expected Return, Risk, and Beta Are Weighted Averages
of Individual Stocks...........................................................................................................180
Example 6.2 Three Alternate Portfolios............................................................................181
6.5 Better Portfolios Define The Efficient Frontier.................................................................182
MEMO Re: Recommended Portfolios are Diversified......................................................184
6.6 Portfolio Risk Depends on Correlations with the Market and Stock Variability...............185
Excel 6.1 Estimate Portfolio Expected Rate of Return and Risk.......................................................186
Three Portfolios with Exxon Mobil, IBM, and Apple.......................................................186
Correlations between stocks and the market.....................................................................186
Excel 6.2 Plot Return by Risk to Identify Dominant Portfolios and the Efficient Frontier...............188
Assignment 6-1 Individual Stocks Beta Estimates...........................................................192
Assignment 6-2 Expected Returns and Beta Estimates of Alternate Portfolios................192
Chapter 7 Association between Two Categorical Variables: Contingency Analysis
with Chi Square....................................................................................................193
7.1 Evidence of Association when Conditional Probabilities Differ from Joint
Probabilities.......................................................................................................................193
Example 7.1 Recruiting Stars............................................................................................194
7.2 Chi Square Tests Association Between Two Categorical Variables.................................195
7.3 Chi Square Is Unreliable If Cell Counts Are Sparse..........................................................197
7.4 Simpson s Paradox Can Mislead.......................................................................................199
Example 7.2 American Cars..............................................................................................199
MEMO Re.: Country of Assembly Does Not Affect Older Buyers Choices...................204
7.5 Contingency Analysis Is Demanding.................................................................................205
7.6 Contingency Analysis Is Quick, Easy, and Readily Understood.......................................205
Excel 7.1 Construct Crosstabulations and Assess Association Between Categorical Variables
with PivotTables and PivotCharts......................................................................................206
American Cars.......................................................................... .........206
Excel 7.2 Use Chi Square to Test Association...................................................................................208
Excel 7.3 Conduct Contingency Analysis with Summary Data........................................................211
Marketing Cereal to Children............................................................................................212
Lab 7 Skype Appeal...........................................................................................................215
Assignment 7-1 747s and Jets............................................................................................217
Assignment 7-2 Fit Matters...............................................................................................217
Assignment 7-3 Allied Airlines.........................................................................................218
Assignment 7-4Netbooks in Color....................................................................................218
CASE 7-1 Hybrids for American Car................................................................................219
CASE 7-2 Tony s GREAT Advertising.............................................................................220
Chapter 8 Building Multiple Regression Models................................................................223
8.1 Multiple Regression Models Identify Drivers and Forecast..............................................223
8.2 Use Your Logic to Choose Model Components................................................................224
Example 8.1 Sakura Motors Quest for Cleaner Cars........................................................224
8.3 Multicollinear Variables Are Likely When Few Variable Combinations
Are Popular in a Sample....................................................................................................227
8.4 F Tests the Joint Significance of the Set of Independent Variables..................................227
8.5 Insignificant Parameter Estimates Signal Multicollinearity..............................................229
8.6 Combine or Eliminate Collinear Predictors.......................................................................231
8.7 Partial FTests the Significance of Changes in Model Power...........................................233
8.8 Sensitivity Analysis Quantifies the Marginal Impact of Drivers.......................................235
MEMO Re: Light, responsive, fuel efficient cars with smaller engines are cleanest........238
8.9 Model Building Begins with Logic and Considers Multicollinearity................................239
Excel 8.1 Build and Fit a Multiple Linear Regression Model...........................................................240
Sakura Motors Quest for a Clean Car................................................................................240
Multicollinearity symptoms...............................................................................................241
Excel 8.2 Use Sensitivity Analysis to Compare the Marginal Impacts of Drivers............................246
Lab Practice 8 Multiple Regression: Drivers of Preemie Diaper Fit Importance..............251
Lab 8 Model Building with Multiple Regression: Pricing Dell s Navigreat.....................253
Assignment 8-1 Sakura Motor s Quest for Fuel Efficiency...............................................256
Assignment 8-2 Starting Room Prices at Marriott.............................................................257
Assignment 8-3 Identifying Promising Global Markets....................................................258
Chapter 9 Model Building and Forecasting with Multicollinear Time Series..................261
9.1 Time Series Models Include Decision Variables, External Forces, Leading
Indicators, and Inertia........................................................................................................263
Example 9.1 Home Depot Revenues..................................................................................263
9.2 Indicators of Economic Prosperity Lead Business Performance.......................................264
9.3 Hide the Two Most Recent Datapoints to Validate a Time Series Model.........................264
9.4 Compare Scatterplots to Choose Driver Lags: Visual Inspection......................................265
9.5 The Durbin Watson Statistic Identifies Positive Autocorrelation......................................267
9.6 Assess Residuals to Identify Unaccounted for Trend or Cycles.......................................269
9.7 Forecast the Recent Hidden Points to Assess Predictive Validity.....................................273
9.8 Add the Most Recent Datapoints to Recalibrate................................................................274
MEMO Re: Revenue Recovery Forecast for late 2010 and 2011......................................276
9.9 Inertia and Leading Indicator Components Are Powerful Drivers
and Often Multicollinear....................................................................................................277
Excel 9.1 Build and Fit a Multiple Regression Model with Multicollinear Time Series...................278
Home Depot Revenues......................................................................................................278
Excel 9.2 Assess Autocorrelation of the Residuals............................................................................287
Excel 9.3 Plot Residuals to Identify Unaccounted for Trend, Cycles, or Seasonality.......................287
Excel 9.4 Test the Model s Forecasting Validity...............................................................................293
Excel 9.5 Recalibrate to Forecast.......................................................................................................294
Excel 9.6 Illustrate the Fit and Forecast.............................................................................................296
Excel 9.7 Assess the Impact of Drivers.............................................................................................297
Lab Practice 9 Starbucks in China.....................................................................................299
Lab 9: HP Revenue Forecast..............................................................................................301
CASE 9-1 Revitalizing Dell...............................................................................................305
CASE 9-2 Mattel Revenues Following the Recalls...........................................................307
CASE 9-3 Starbucks in China............................................................................................308
CASE 9-4 Harley-Davidson Revenue Forecast................................................................310
Chapter 10 Indicator Variables..............................................................................................313
10.1 Indicators Modify the Intercept to Account for Segment Differences...............................313
Example 10.1 Hybrid Fuel Economy ...............................................................................313
Example 10.2 Yankees v Marlins Salaries.........................................................................314
10.2 Indicators Estimate the Value of Product Attributes.........................................................317
Example 10.3 New PDA Design .......................................................................................317
10.3 ANOVA Identifies Segment Mean Differences................................................................321
Example 10.4 Background music to create brand interest ..............................................321
10.4 ANOVA and Regression with Indicators are Complementary Substitutes.......................326
10.5 ANOVA and Regression in Excel.....................................................................................328
10.6 Indicators Quantify Shocks in Time Series........................................................................329
Example 10.5 Tyson s Farm Worker Forecast .................................................................329
MEMO Re: Supply of Self Employed Workers Stable Following 09 Contraction...........336
10.7 Indicators Allow Comparison of Segments and Scenarios,
Quantify Shocks, and Offer an Alternative to Analysis of Variance.................................337
Excel 10.1 Use Indicators to Find Part Worth Utilities and Attribute Importances from
Conjoint Analysis Data......................................................................................................338
Excel 10.2 Add Indicator Variables to Account for Segment Differences or Structural
Shifts......................................................................................... 344
Indian Imports of U.S. Products ..................................... ........344
Lab Practice 10 Indicators with Time Series: Impact of Terrorism
and Military Strike on Oil Prices.......................................................................................353
Lab 10-1 ANOVA and Regression with Indicators: Global Ad Spending........................355
Lab 10-2 The H-D Buell Blast..........................................................................................357
Assignment 10-1 Conjoint Analysis of PDA Preferences.................................................359
CASE 10-1 Modeling Growth: Procter Gamble Quarterly Revenues...........................360
CASE 10-2 Store24 (A): Managing Employee Retention
and Store24 (B): Service Quality and Employee Skills.................................362
Chapterll Nonlinear Multiple Regression Models..............................................................365
11.1 Consider a Nonlinear Model When Response Is Not Constant.........................................365
11.2 Tukey s Ladder of Powers.................................................................................................365
11.3 Rescaling y Builds in Synergies.........................................................................................367
Example 11.1 Executive Compensation.............................................................................367
11.4 Sensitivity Analysis Reveals the Relative Strength of Drivers..........................................373
MEMO Re: Executive Compensation Driven by Firm Performance and Age..................376
11.5 Gains from Nonlinear Rescaling Are Significant..............................................................377
11.6 Nonlinear Models Offer the Promise of Better Fit and Better Behavior...........................378
Excel 11.1 Rescale to Build and Fit Nonlinear Regression Models with Linear Regression..............379
Executive Compensation...................................................................................................379
Excel 11.2 Consider Synergies in a Multiplicative Model with Sensitivity Analysis.........................387
Lab Practice 11..................................................................................................................393
Lab 11 Nonlinear Hybrid Sales..........................................................................................394
CASE 11-1 Global Emissions Segmentation: Markets Where Hybrids Might
Have Particular Appeal..................................................................................397
Chapter 12 Indicator Interactions for Segment Differences or Changes in Response......401
12.1 Indicator Interaction with a Continuous Influence Alters Its Partial Slope.......................401
Example 12.1 Gender Discrimination at Slams Club........................................................402
MEMO Re: Women are Paid More than Men at Slam s Club..........................................407
Example 12.2 Car Sales in China......................................................................................408
12.2 Indicator Interactions Capture Segment Differences or Structural Differences
in Response........................................................................................................................413
Excel 12.1 Add Indicator Interactions to Capture Segment Differences or Changes
in Response........................................................................................................................415
Car Sales in China..............................................................................................................415
Lab Practice 12 Car Sales in India.....................................................................................427
Lab 12 Identifying Promising Global Markets II...............................................................429
CASE 12-1 Explain and Forecast Defense Spending for Rolls-Royce..............................431
CASE 12-3 Pilgrim Bank (A): Customer Profitability
and Pilgrim Bank (B): Customer Retention...................................................433
Chapter 13 Logit Regression for Bounded Responses..........................................................435
13.1 Rescaling Probabilities or Shares to Odds Improves Model Validity................................435
Example 13.1 The Import Challenge.................................................................................436
MEMO Re: Fuel Efficiency Drives Hybrid Owner Satisfaction.......................................440
Example 13.2 Presidential Approval Proportion..............................................................441
13.2 Logit Models Provide the Means to Build Valid Models of Shares and
Proportions.........................................................................................................................445
Excel 13.1 Regression of a Limited Dependent Variable Using Logits..............................................446
Proportion Who Would Try Pampers Preemies.................................................................446
Lab 13 T-Mobile s Plans to Capture Share in the Cell Phone Service Market..................453
Assignment 13-1 Big Drug Co Scripts..............................................................................457
Assignment 13-2 Competition in the Netbook Market......................................................457
CASE 13-1 Pilgrim Bank (B): Customer Retention..........................................................458
Index..........................................................................................................................................................461
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| spelling | Fraser, Cynthia Verfasser (DE-588)170602702 aut Business statistics for competitive advantage with Excel 2010 basics, model building, and cases Cynthia Fraser 2. ed. New York [u.a.] Springer 2012 XVI, 469 S. txt rdacontent n rdamedia nc rdacarrier Erscheint auch als Online-Ausgabe 978-1-4419-9857-6 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025013704&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Fraser, Cynthia Business statistics for competitive advantage with Excel 2010 basics, model building, and cases |
| title | Business statistics for competitive advantage with Excel 2010 basics, model building, and cases |
| title_auth | Business statistics for competitive advantage with Excel 2010 basics, model building, and cases |
| title_exact_search | Business statistics for competitive advantage with Excel 2010 basics, model building, and cases |
| title_full | Business statistics for competitive advantage with Excel 2010 basics, model building, and cases Cynthia Fraser |
| title_fullStr | Business statistics for competitive advantage with Excel 2010 basics, model building, and cases Cynthia Fraser |
| title_full_unstemmed | Business statistics for competitive advantage with Excel 2010 basics, model building, and cases Cynthia Fraser |
| title_short | Business statistics for competitive advantage with Excel 2010 |
| title_sort | business statistics for competitive advantage with excel 2010 basics model building and cases |
| title_sub | basics, model building, and cases |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025013704&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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