Business statistics for competitive advantage with Excel 2007: basics, model building, and cases
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Springer
2009
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XVIII, 410 S. graph. Darst. 279 mm x 216 mm |
| ISBN: | 9780387744025 |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV023327505 | ||
| 003 | DE-604 | ||
| 005 | 20090112 | ||
| 007 | t | ||
| 008 | 080604s2009 d||| |||| 00||| eng d | ||
| 015 | |a 08,N17,0250 |2 dnb | ||
| 016 | 7 | |a 988178060 |2 DE-101 | |
| 020 | |a 9780387744025 |c Pb. : EUR 64.15 (freier Pr.), sfr 99.50 (freier Pr.) |9 978-0-387-74402-5 | ||
| 024 | 3 | |a 9780387744025 | |
| 028 | 5 | 2 | |a 12103577 |
| 035 | |a (OCoLC)602265783 | ||
| 035 | |a (DE-599)DNB988178060 | ||
| 040 | |a DE-604 |b ger |e rakddb | ||
| 041 | 0 | |a eng | |
| 050 | 0 | |a HD30.23 | |
| 084 | |a ST 371 |0 (DE-625)143672: |2 rvk | ||
| 084 | |a 330 |2 sdnb | ||
| 100 | 1 | |a Fraser, Cynthia |e Verfasser |0 (DE-588)134080114 |4 aut | |
| 245 | 1 | 0 | |a Business statistics for competitive advantage with Excel 2007 |b basics, model building, and cases |c Cynthia Fraser |
| 264 | 1 | |a New York, NY |b Springer |c 2009 | |
| 300 | |a XVIII, 410 S. |b graph. Darst. |c 279 mm x 216 mm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 630 | 0 | 4 | |a Microsoft Excel (Computer file) |
| 650 | 4 | |a Mathematisches Modell | |
| 650 | 4 | |a Commercial statistics | |
| 650 | 4 | |a Decision making |x Mathematical models | |
| 650 | 4 | |a Mathematical statistics | |
| 650 | 0 | 7 | |a Excel 2007 |0 (DE-588)7558101-2 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Wettbewerbsvorteil |0 (DE-588)4219652-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Statistik |0 (DE-588)4056995-0 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Excel 2007 |0 (DE-588)7558101-2 |D s |
| 689 | 0 | 1 | |a Statistik |0 (DE-588)4056995-0 |D s |
| 689 | 0 | 2 | |a Wettbewerbsvorteil |0 (DE-588)4219652-8 |D s |
| 689 | 0 | |5 DE-604 | |
| 856 | 4 | 2 | |m Digitalisierung UB Bayreuth |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016511494&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
Datensatz im Suchindex
| _version_ | 1805083709131456512 |
|---|---|
| adam_text |
Contents
Preface
xvii
Chapter
1
Statistics for Decision Making and Competitive
Advantage
1
ł
. 1
Statistical Competences Translate Into Competitive Advantages
1
1.2
Attain Statistical Competences And Competitive Advantage
With This Text
1
1.3
Follow The Path Toward Statistical Competence and Competitive
Advantage
2
1.4
Use Excel for Competitive Advantage
3
1.5
Statistical Competence Is Satisfying
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
7
Example
2.2
Executive Compensation: Is the Board's Offer
on Target?
7
2.3
Round Descriptive Statistics
10
2.4
Central Tendency and Dispersion Describe Data
11
2.5
Data Is Measured With Quantitative or Categorical Scales
11
2.6
Continuous Data Tend To Be Normal
12
Example
2.3
Normal
SA T
Scores
12
2.7
The Empirical Rule Simplifies Description
13
Example
2.4
Class of
'06
SATs: This Class is Normal
¿¿Exceptional
13
2.8
Describe Categorical Variables Graphically: Column
and PivotCharts
15
Example
2.5
Who Is Honest
&
Ethical?
15
2.9
Descriptive Statistics Depend On The Data
16
Excel
2.1
Produce descriptive statistics and view distributions
with histograms
17
Excel
2.2
Sort to produce
descriptives
without outliers
20
Excel
2.3
Plot a cumulative distribution
23
Contents
Excel 2.4 Find
and view distribution percentages with a PivotTable
and PivotChart
24
Excel
2.5
Produce a column chart from a PivotChart of a nominal variable
27
Excel Shortcuts at Your Fingertips
29
Lab
2
Descriptive Statistics
31
Assignment
2-І
Procter
&
Gamble's Global Advertising
33
CASE
2-
J VW
Backgrounds
34
Chapter
3
Hypothesis Tests,
Confídence
Intervals and Simulation
to Infer Population Characteristics and Differences
35
3.1
Sample Means Are Random Variables
35
Example
3.1
Thirsty on Campus: Is there Sufficient Demand?
35
3.2
Use Sample Data to Determine Whether Or Not
μ
Is Likely
To Exceed A Target
38
3.3
Confidence Intervals Estimate the Population Mean From A Sample
41
3.4
Round
/
to Calculate Approximate
95%
Confidence Intervals
With Mental Math
43
3.5
Margin of Error Is Inversely Proportional To Sample Size
43
3.6
Samples Are Efficient
44
3.7
Use Monte Carlo Simulation with Sample Statistics To Incorporate
Uncertainty and Quantify Implications Of Assumptions
44
3.8
Determine Whether There Is a Difference Between Two Segments
With Student
t
48
Example
3.2
Pampers Preemies: Is Income a Useful Base
for Segmentation?
48
3.9
Estimate the Extent of Difference between Two Segments
With Student
t
49
3.10
Confidence Intervals Complement Hypothesis Tests
50
3.11
Estimation of a Population Proportion from a Sample Proportion
50
Example
3.3
Guinea Pigs
50
3.12
Conditions for Assuming Approximate Normality to Make
Confidence Intervals for Proportions
53
3.13
Conservative Confidence Intervals for a Proportion
53
3.14
Assess the Difference between Alternate Scenarios or Pairs
With Student
t
54
Example
3.4
Are "Socially Desirable
"
Portfolios Undesirable?
55
3.15
Inference from Sample to Population
58
Excel
3.1
Test the level of a population mean with a one sample
t
test
59
Excel
3.2
Make a confidence interval for a population mean
60
Contents
Excel 3.3
Illustrate population confidence intervals with a clustered
column chart
61
Excel
3.4
Conduct a Monte Carlo simulation with Crystal Ball
65
Excel
3.5
Test the difference between two segments with a two sample
t
test
69
Excel
3.6
Construct a confidence interval for the difference between
two segments
70
Excel
3.7
Illustrate the difference between two segment means
with a column chart
71
Excel
3.8
Construct a pie chart of shares
72
Excel
3.9
Test the difference in levels between alternate scenarios
or pairs with a paired
t
test
74
Excel
3.10
Construct a confidence interval for the difference between
alternate scenarios or pairs
76
Excel Shortcuts at Your Fingertips
78
Lab Practice
3
Inference
80
Lab
3
Inference
82
Assignment
3-1
Bottled Water Possibilities
83
Assignment
3-2
Immigration in the U.S.
84
Assignment
3-3
McLattes
84
Assignment
3-4
A Barbie Duff in Stuff
85
CASE
3-1
Yankees
v
Marlins: The Value of a Yankee Uniform
85
CASE
3-2
Gender Pay
86
CASE
3-3
Polaski Vodka: Can a Polish Vodka Stand Up
to the Russians?
86
CASE
3-4
American Girl in StarbucL·
88
Chapter
4
Quantifying the Influence of Performance Drivers
and Forecasting: Regression
91
4.1
The Simple Linear Regression Equation Describes the Line Relating
A Decision Variable to Performance
91
Example
4.1
HitFlix Movie Rentals
92
4.2
F
Tests the Significance of the Hypothesized Linear Relationship,
RSquare Summarizes Its Strength and Standard Error Reflects
Forecasting Precision
93
4.3
The Population Slope Is Tested And Inferred From Our Sample
96
4.4
Analyze Residuals To Learn Whether Assumptions Have Been Met
98
4.5 95%
Prediction Intervals Acknowledge That Individual
Elements Differ
99
4.6
Use Sensitivity Analysis to Explore Alternative Scenarios
101
Contents
4.7 95%
Conditional Mean Prediction Intervals Of Average
Performance Gauge Average Performance Response To A Driver
101
4.8
Explanation And Prediction Create A Complete Picture
102
4.9
Present Regression Results In Concise Format
103
4.10
We Make Assumptions When We Use Linear Regression
104
4.11
Correlation Is A Standardized Covariance
105
Example
4.2
HitFlix Movie Rentals
105
4.12
Correlation Coefficients Are Key Components Of Regression
Slopes
109
Example
4.3
Pampers
110
4.13
Correlation Summarizes Linear Association
113
4.14
Linear Regression Is Doubly Useful
113
Excel
4.1
Fit a simple linear regression model
114
Excel
4.2
Construct prediction and conditional mean prediction intervals
118
Excel
4.3
Find correlations between variable pairs
124
Excel Shortcuts at Your Fingertips
126
Lab
4
Regression
128
CASE
4-1
GenderPay (B)
130
CASE
4-2
GMRevenue Forecast
131
Assignment
4-І
Impact of Defense Spending on Economic Growth
133
Chapter
5
Marketing Segmentation with Descriptive Statistics,
Inference, Hypothesis Tests and Regression
135
CASE
5-1
Segmentation of the Market for Preemie Diapers
135
5.1
Guide to Effective PowerPoint Presentations and Writing
Memos
that your Audience will Read
145
5.2
Write
Memos
that Encourage Your Audience to Read
and Use Results
147
MEMO Re: Importance of Fit Drives Trial Intention
148
Chapter
6
Finance Application: Portfolio Analysis
with a Market Index as a Leading Indicator
in Simple Linear Regression
149
6.1
Rates of Return Reflect Expected Growth of Stock Prices
149
Example
6.
J
Goldman Sachs and Yahoo Returns
149
6.2
Investors Trade Off Risk And Return
152
6.3
Beta Measures Risk
152
Example
6.2
Four diverse stocks
153
Contents
6.4
A Portfolio's Expected Return, Risk and Beta Are Weighted
Averages of Individual Stocks
158
Example
6.3
Four
A ¡ternate
Portfolios
158
6.5
Better Portfolios Define The Efficient Frontier
161
MEMO Re: Recommended Portfolios Include Lockheed
Martin and Apple
162
6.6
Portfolio Risk Depends On the Covariances between Individual
Stocks' Rates of Return and The Market Rate Of Return
163
Excel
6.1
Estimate portfolio expected rate of return and risk
164
Excel
6.2
Plot return by risk to identify dominant portfolios and the Efficient
Frontier
166
Assignment
6-І
Individual Stocks
'
Beta Estimates
169
Assignment
6-2
Expected Returns and Beta Estimates of Alternate
Portfolios
169
Assignment
6-3
Portfolio Comparison
170
Chapter
7
Association between Two Categorical
Variables: Contingency Analysis with Chi Square
171
7.1
When Conditional Probabilities Differ From Joint Probabilities,
There Is Evidence of Association
171
Example
7.1
Recruiting Stars
172
7.2
Chi Square Tests Association between Two Categorical Variables
174
7.3
Chi Square Is Unreliable If Cell Counts Are Sparse
175
7.4
Simpson's Paradox Can Mislead
177
Example
7.2
American Cars
177
MEMO Re: Country of Manufacture Does Not Affect Older
Buyers'Choices
183
7.5
Contingency Analysis Is Demanding
184
7.6
Contingency Analysis Is Quick, Easy, and Readily Understood
184
Excel
7.1
Construct crosstabulations and assess association between
categorical variables with PivotTables and PivotCharts
185
Excel
7.2
Use
chi
square to test association
187
Excel
7.3
Conduct contingency analysis with summary data
190
Excel Shortcuts at Your Fingertips
193
Assignment
7-1
747s and Jets
195
Assignment
7-2
Fit Matters
195
Assignment
7-3
A Hied
A ir
lines
196
CASE
7-1
Hybrids for American Car \
97
CASE
7-2
Tony's GREAT Advertising
198
Contents
Chapter
8 Building Multiple Regression Models 201
8.1 Multiple Regression Models
Identify Drivers and Forecast
201
8.2
Use Your Logic to Choose Model Components
201
Example
8.1
Sakura Motors Quest for Cleaner Cars
202
8.3
Multicollinear Variables Are Likely When Few Variable
Combinations Are Popular In a Sample
203
8.4
F
Tests the Joint Significance of the Set of Independent Variables
204
8.5
Insignificant Parameter Estimates Signal Multicollinearity
205
8.6
Combine or Eliminate
Collinear
Predictors
205
8.7
Partial
F
Tests the Significance of Changes in Model Power
207
8.8
Sensitivity Analysis Quantifies the Marginal Impact Of Drivers
211
MEMO Re: Light, responsive, fuel efficient cars with smaller
engines are cleanest
214
8.9
Model Building Begins With Logic and Considers
Multicollinearity
215
Excel
8.1
Build and fit a multiple linear regression model
216
Excel
8.2
Use sensitivity analysis to compare the marginal impacts
of drivers
221
Lab Practice
8 228
Lab
8
Model Building with Multiple Regression
230
Assignment
8-1 233
Chapter
9
Model Building and Forecasting with Multicollinear
Time Series
235
9.1
Time Series Models Include Decision Variables, External Forces,
Leading Indicators, And Inertia
237
Example
9.1
Home Depot Revenues
238
9.2
Indicators of Economic Prosperity Lead Business Performance
238
9.3
Inertia from Loyal Customers Drives Performance
238
9.4
Compare Scatterplots across Time to Choose Length of Lags
For Drivers of Delayed Response: Visual Inspection
239
9.5
Hide the Two Most Recent
Datapoints
to Validate a Time Series
Model
241
9.6
Correlations Guide Choice of Lags
241
9.7
The
Durbin
Watson Statistics Identifies Autocorrelation
242
9.8
Assess Residuals to Identify Unaccounted For Trend or Cycles
243
9.9
Forecast the Recent, Hidden Points to Assess Predictive Validity
246
Contents
9.10
Add the Most Recent
Datapoints
to Recalibrate
246
MEMO Re: Revenue Decline Forecast Following New Home
Sales Downturn
248
9.11
Inertia and Leading Indicator Components Are Powerful Drivers
and Often Multicollinear
249
Excel
9.1
Build and
fît a
multiple regression model with multicollinear
time series
250
Chapter
9
Lab: HP Revenue Forecast
266
CASE
9-І
Dell: Overcoming Roadblocks to Growth
268
CASE
9-2
Mattel Revenues Following the Recalls
270
CASE
9-3
StarbucL· in China
272
Chapter
10
Indicator Variables
275
10.1
Indicators Modify the Intercept to Account for Segment
Differences
275
Example
10.1
Hybrid Fuel Economy
275
Example
10.2
Yankees
v
Marlins Salaries
276
10.2
Indicators Estimate the Value of Product Attributes
278
Example
10.3
New PDA Design
278
10.3
Indicators Quantify Seasonality in Time Series
283
Example
10.4
Tyson's Farm Worker Forecast
283
MEMO Re: Declining Supply of Self Employed Agriculture
Workers
290
10.4
Indicators Add Structural Shifts in Time Series
291
Example
10.5
Leadership Changes Influence US Imports
by India
291
10.5
Indicators Allow Comparison of Segments and Scenarios
And Quantify Structural Shifts
294
Excel
10.1
Use indicators to find part worth utilities and attribute
importances from conjoint analysis data
295
Excel
10.2
Add indicator variables to account for segment differences
or structural shifts
299
Lab Practice
10 306
Assignment
10-1
Conjoint Analysis of PDA Preferences
308
CASE
10-1
Modeling Growth: Procter
&
Gamble Quarterly
Revenues
309
CASE
10-2
Store24 (A): Managing Employee Retention
and Store24 (B): Service Quality and Employee Skills
3
\
2
Contents
Chapter
11
Nonlinear Multiple Regression Models
313
11.1
Consider a Nonlinear Model When Response Is Not Constant
313
11.2
Tukey
'
s
Ladder of Powers
ЗІЗ
11.3
Rescaling
y
Builds in Synergies
315
Example
11.1
Executive Compensation
315
11.4
Sensitivity Analysis Reveals the Relative Strength of Drivers
320
MEMO Re: Executive Compensation Driven by Firm
Performance and Age
323
11.5
Gains from Nonlinear Rescaling Are Significant
324
11.6
Nonlinear Models Offer the Promise of Better Fit
and Better Behavior
325
Excel
11.1
Rescale
to build and fit nonlinear regression models with linear
regression
326
Excel
11.2
Consider synergies in sensitivity analysis with a nonlinear model
334
Lab Practice
11 338
CASE
11-1
Global Emissions Segmentation: Markets Where
Hybrids Might Have Particular Appeal
339
Chapter
12
Indicator Interactions for Structural Differences
or Changes in Response
343
12.1
Indicator Interaction with a Continuous Influence Alters
Its Partial Slope
343
Example
12.1
Gender Discrimination at Slams Club
344
MEMO Re: Women are Paid More than Men at Slam's Club
350
Example
12.2
Car Sales in China
351
12.2
Indicator Interactions Capture Segment Differences or Structural
Differences in Response
358
Excel
12.1
Add indicator interactions to capture segment differences
or structural differences in response
359
Lab Practice
12 370
CASE
12-1
Explain and Forecast Defense Spending for Rolls-Royce
372
CASE
12-2
Haier 's U.S. Refrigerator Strategy
375
Chapter
13
Logit Regression for Bounded Responses
377
13.1
Rescaling Probabilities or Shares to Odds Improves Model Validity
377
Example
13.1
The Import Challenge
378
MEMO Re: Fuel Efficiency Drives Hybrid Owner Satisfaction
385
Example
13.2
Presidential Approval Proportion
386
Contents
13.2 Logit Models
Provide the Means to Build Valid Models of Shares
And Proportions
390
Excel
13.1
Rescale a
limited dependent variable to logits
391
Assignment
13-1
Big Drug Co Scripts
399
CASE
13-1
Alltel'
s
Plans to Capture Share in the Cell Phone
Service Market
400
CASE
13-2
Pilgrim Bank (A): Profitability and Pilgrim
Bank (B): Customer Retention
403
Index
405 |
| adam_txt |
Contents
Preface
xvii
Chapter
1
Statistics for Decision Making and Competitive
Advantage
1
ł
. 1
Statistical Competences Translate Into Competitive Advantages
1
1.2
Attain Statistical Competences And Competitive Advantage
With This Text
1
1.3
Follow The Path Toward Statistical Competence and Competitive
Advantage
2
1.4
Use Excel for Competitive Advantage
3
1.5
Statistical Competence Is Satisfying
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
7
Example
2.2
Executive Compensation: Is the Board's Offer
on Target?
7
2.3
Round Descriptive Statistics
10
2.4
Central Tendency and Dispersion Describe Data
11
2.5
Data Is Measured With Quantitative or Categorical Scales
11
2.6
Continuous Data Tend To Be Normal
12
Example
2.3
Normal
SA T
Scores
12
2.7
The Empirical Rule Simplifies Description
13
Example
2.4
Class of
'06
SATs: This Class is Normal
¿¿Exceptional
13
2.8
Describe Categorical Variables Graphically: Column
and PivotCharts
15
Example
2.5
Who Is Honest
&
Ethical?
15
2.9
Descriptive Statistics Depend On The Data
16
Excel
2.1
Produce descriptive statistics and view distributions
with histograms
17
Excel
2.2
Sort to produce
descriptives
without outliers
20
Excel
2.3
Plot a cumulative distribution
23
Contents
Excel 2.4 Find
and view distribution percentages with a PivotTable
and PivotChart
24
Excel
2.5
Produce a column chart from a PivotChart of a nominal variable
27
Excel Shortcuts at Your Fingertips
29
Lab
2
Descriptive Statistics
31
Assignment
2-І
Procter
&
Gamble's Global Advertising
33
CASE
2-
J VW
Backgrounds
34
Chapter
3
Hypothesis Tests,
Confídence
Intervals and Simulation
to Infer Population Characteristics and Differences
35
3.1
Sample Means Are Random Variables
35
Example
3.1
Thirsty on Campus: Is there Sufficient Demand?
35
3.2
Use Sample Data to Determine Whether Or Not
μ
Is Likely
To Exceed A Target
38
3.3
Confidence Intervals Estimate the Population Mean From A Sample
41
3.4
Round
/
to Calculate Approximate
95%
Confidence Intervals
With Mental Math
43
3.5
Margin of Error Is Inversely Proportional To Sample Size
43
3.6
Samples Are Efficient
44
3.7
Use Monte Carlo Simulation with Sample Statistics To Incorporate
Uncertainty and Quantify Implications Of Assumptions
44
3.8
Determine Whether There Is a Difference Between Two Segments
With Student
t
48
Example
3.2
Pampers Preemies: Is Income a Useful Base
for Segmentation?
48
3.9
Estimate the Extent of Difference between Two Segments
With Student
t
49
3.10
Confidence Intervals Complement Hypothesis Tests
50
3.11
Estimation of a Population Proportion from a Sample Proportion
50
Example
3.3
Guinea Pigs
50
3.12
Conditions for Assuming Approximate Normality to Make
Confidence Intervals for Proportions
53
3.13
Conservative Confidence Intervals for a Proportion
53
3.14
Assess the Difference between Alternate Scenarios or Pairs
With Student
t
54
Example
3.4
Are "Socially Desirable
"
Portfolios Undesirable?
55
3.15
Inference from Sample to Population
58
Excel
3.1
Test the level of a population mean with a one sample
t
test
59
Excel
3.2
Make a confidence interval for a population mean
60
Contents
Excel 3.3
Illustrate population confidence intervals with a clustered
column chart
61
Excel
3.4
Conduct a Monte Carlo simulation with Crystal Ball
65
Excel
3.5
Test the difference between two segments with a two sample
t
test
69
Excel
3.6
Construct a confidence interval for the difference between
two segments
70
Excel
3.7
Illustrate the difference between two segment means
with a column chart
71
Excel
3.8
Construct a pie chart of shares
72
Excel
3.9
Test the difference in levels between alternate scenarios
or pairs with a paired
t
test
74
Excel
3.10
Construct a confidence interval for the difference between
alternate scenarios or pairs
76
Excel Shortcuts at Your Fingertips
78
Lab Practice
3
Inference
80
Lab
3
Inference
82
Assignment
3-1
Bottled Water Possibilities
83
Assignment
3-2
Immigration in the U.S.
84
Assignment
3-3
McLattes
84
Assignment
3-4
A Barbie Duff in Stuff
85
CASE
3-1
Yankees
v
Marlins: The Value of a Yankee Uniform
85
CASE
3-2
Gender Pay
86
CASE
3-3
Polaski Vodka: Can a Polish Vodka Stand Up
to the Russians?
86
CASE
3-4
American Girl in StarbucL·
88
Chapter
4
Quantifying the Influence of Performance Drivers
and Forecasting: Regression
91
4.1
The Simple Linear Regression Equation Describes the Line Relating
A Decision Variable to Performance
91
Example
4.1
HitFlix Movie Rentals
92
4.2
F
Tests the Significance of the Hypothesized Linear Relationship,
RSquare Summarizes Its Strength and Standard Error Reflects
Forecasting Precision
93
4.3
The Population Slope Is Tested And Inferred From Our Sample
96
4.4
Analyze Residuals To Learn Whether Assumptions Have Been Met
98
4.5 95%
Prediction Intervals Acknowledge That Individual
Elements Differ
99
4.6
Use Sensitivity Analysis to Explore Alternative Scenarios
101
Contents
4.7 95%
Conditional Mean Prediction Intervals Of Average
Performance Gauge Average Performance Response To A Driver
101
4.8
Explanation And Prediction Create A Complete Picture
102
4.9
Present Regression Results In Concise Format
103
4.10
We Make Assumptions When We Use Linear Regression
104
4.11
Correlation Is A Standardized Covariance
105
Example
4.2
HitFlix Movie Rentals
105
4.12
Correlation Coefficients Are Key Components Of Regression
Slopes
109
Example
4.3
Pampers
110
4.13
Correlation Summarizes Linear Association
113
4.14
Linear Regression Is Doubly Useful
113
Excel
4.1
Fit a simple linear regression model
114
Excel
4.2
Construct prediction and conditional mean prediction intervals
118
Excel
4.3
Find correlations between variable pairs
124
Excel Shortcuts at Your Fingertips
126
Lab
4
Regression
128
CASE
4-1
GenderPay (B)
130
CASE
4-2
GMRevenue Forecast
131
Assignment
4-І
Impact of Defense Spending on Economic Growth
133
Chapter
5
Marketing Segmentation with Descriptive Statistics,
Inference, Hypothesis Tests and Regression
135
CASE
5-1
Segmentation of the Market for Preemie Diapers
135
5.1
Guide to Effective PowerPoint Presentations and Writing
Memos
that your Audience will Read
145
5.2
Write
Memos
that Encourage Your Audience to Read
and Use Results
147
MEMO Re: Importance of Fit Drives Trial Intention
148
Chapter
6
Finance Application: Portfolio Analysis
with a Market Index as a Leading Indicator
in Simple Linear Regression
149
6.1
Rates of Return Reflect Expected Growth of Stock Prices
149
Example
6.
J
Goldman Sachs and Yahoo Returns
149
6.2
Investors Trade Off Risk And Return
152
6.3
Beta Measures Risk
152
Example
6.2
Four diverse stocks
153
Contents
6.4
A Portfolio's Expected Return, Risk and Beta Are Weighted
Averages of Individual Stocks
158
Example
6.3
Four
A ¡ternate
Portfolios
158
6.5
Better Portfolios Define The Efficient Frontier
161
MEMO Re: Recommended Portfolios Include Lockheed
Martin and Apple
162
6.6
Portfolio Risk Depends On the Covariances between Individual
Stocks' Rates of Return and The Market Rate Of Return
163
Excel
6.1
Estimate portfolio expected rate of return and risk
164
Excel
6.2
Plot return by risk to identify dominant portfolios and the Efficient
Frontier
166
Assignment
6-І
Individual Stocks
'
Beta Estimates
169
Assignment
6-2
Expected Returns and Beta Estimates of Alternate
Portfolios
169
Assignment
6-3
Portfolio Comparison
170
Chapter
7
Association between Two Categorical
Variables: Contingency Analysis with Chi Square
171
7.1
When Conditional Probabilities Differ From Joint Probabilities,
There Is Evidence of Association
171
Example
7.1
Recruiting Stars
172
7.2
Chi Square Tests Association between Two Categorical Variables
174
7.3
Chi Square Is Unreliable If Cell Counts Are Sparse
175
7.4
Simpson's Paradox Can Mislead
177
Example
7.2
American Cars
177
MEMO Re: Country of Manufacture Does Not Affect Older
Buyers'Choices
183
7.5
Contingency Analysis Is Demanding
184
7.6
Contingency Analysis Is Quick, Easy, and Readily Understood
184
Excel
7.1
Construct crosstabulations and assess association between
categorical variables with PivotTables and PivotCharts
185
Excel
7.2
Use
chi
square to test association
187
Excel
7.3
Conduct contingency analysis with summary data
190
Excel Shortcuts at Your Fingertips
193
Assignment
7-1
747s and Jets
195
Assignment
7-2
Fit Matters
195
Assignment
7-3
A Hied
A ir
lines
196
CASE
7-1
Hybrids for American Car \
97
CASE
7-2
Tony's GREAT Advertising
198
Contents
Chapter
8 Building Multiple Regression Models 201
8.1 Multiple Regression Models
Identify Drivers and Forecast
201
8.2
Use Your Logic to Choose Model Components
201
Example
8.1
Sakura Motors Quest for Cleaner Cars
202
8.3
Multicollinear Variables Are Likely When Few Variable
Combinations Are Popular In a Sample
203
8.4
F
Tests the Joint Significance of the Set of Independent Variables
204
8.5
Insignificant Parameter Estimates Signal Multicollinearity
205
8.6
Combine or Eliminate
Collinear
Predictors
205
8.7
Partial
F
Tests the Significance of Changes in Model Power
207
8.8
Sensitivity Analysis Quantifies the Marginal Impact Of Drivers
211
MEMO Re: Light, responsive, fuel efficient cars with smaller
engines are cleanest
214
8.9
Model Building Begins With Logic and Considers
Multicollinearity
215
Excel
8.1
Build and fit a multiple linear regression model
216
Excel
8.2
Use sensitivity analysis to compare the marginal impacts
of drivers
221
Lab Practice
8 228
Lab
8
Model Building with Multiple Regression
230
Assignment
8-1 233
Chapter
9
Model Building and Forecasting with Multicollinear
Time Series
235
9.1
Time Series Models Include Decision Variables, External Forces,
Leading Indicators, And Inertia
237
Example
9.1
Home Depot Revenues
238
9.2
Indicators of Economic Prosperity Lead Business Performance
238
9.3
Inertia from Loyal Customers Drives Performance
238
9.4
Compare Scatterplots across Time to Choose Length of Lags
For Drivers of Delayed Response: Visual Inspection
239
9.5
Hide the Two Most Recent
Datapoints
to Validate a Time Series
Model
241
9.6
Correlations Guide Choice of Lags
241
9.7
The
Durbin
Watson Statistics Identifies Autocorrelation
242
9.8
Assess Residuals to Identify Unaccounted For Trend or Cycles
243
9.9
Forecast the Recent, Hidden Points to Assess Predictive Validity
246
Contents
9.10
Add the Most Recent
Datapoints
to Recalibrate
246
MEMO Re: Revenue Decline Forecast Following New Home
Sales Downturn
248
9.11
Inertia and Leading Indicator Components Are Powerful Drivers
and Often Multicollinear
249
Excel
9.1
Build and
fît a
multiple regression model with multicollinear
time series
250
Chapter
9
Lab: HP Revenue Forecast
266
CASE
9-І
Dell: Overcoming Roadblocks to Growth
268
CASE
9-2
Mattel Revenues Following the Recalls
270
CASE
9-3
StarbucL· in China
272
Chapter
10
Indicator Variables
275
10.1
Indicators Modify the Intercept to Account for Segment
Differences
275
Example
10.1
Hybrid Fuel Economy
275
Example
10.2
Yankees
v
Marlins Salaries
276
10.2
Indicators Estimate the Value of Product Attributes
278
Example
10.3
New PDA Design
278
10.3
Indicators Quantify Seasonality in Time Series
283
Example
10.4
Tyson's Farm Worker Forecast
283
MEMO Re: Declining Supply of Self Employed Agriculture
Workers
290
10.4
Indicators Add Structural Shifts in Time Series
291
Example
10.5
Leadership Changes Influence US Imports
by India
291
10.5
Indicators Allow Comparison of Segments and Scenarios
And Quantify Structural Shifts
294
Excel
10.1
Use indicators to find part worth utilities and attribute
importances from conjoint analysis data
295
Excel
10.2
Add indicator variables to account for segment differences
or structural shifts
299
Lab Practice
10 306
Assignment
10-1
Conjoint Analysis of PDA Preferences
308
CASE
10-1
Modeling Growth: Procter
&
Gamble Quarterly
Revenues
309
CASE
10-2
Store24 (A): Managing Employee Retention
and Store24 (B): Service Quality and Employee Skills
3
\
2
Contents
Chapter
11
Nonlinear Multiple Regression Models
313
11.1
Consider a Nonlinear Model When Response Is Not Constant
313
11.2
Tukey
'
s
Ladder of Powers
ЗІЗ
11.3
Rescaling
y
Builds in Synergies
315
Example
11.1
Executive Compensation
315
11.4
Sensitivity Analysis Reveals the Relative Strength of Drivers
320
MEMO Re: Executive Compensation Driven by Firm
Performance and Age
323
11.5
Gains from Nonlinear Rescaling Are Significant
324
11.6
Nonlinear Models Offer the Promise of Better Fit
and Better Behavior
325
Excel
11.1
Rescale
to build and fit nonlinear regression models with linear
regression
326
Excel
11.2
Consider synergies in sensitivity analysis with a nonlinear model
334
Lab Practice
11 338
CASE
11-1
Global Emissions Segmentation: Markets Where
Hybrids Might Have Particular Appeal
339
Chapter
12
Indicator Interactions for Structural Differences
or Changes in Response
343
12.1
Indicator Interaction with a Continuous Influence Alters
Its Partial Slope
343
Example
12.1
Gender Discrimination at Slams Club
344
MEMO Re: Women are Paid More than Men at Slam's Club
350
Example
12.2
Car Sales in China
351
12.2
Indicator Interactions Capture Segment Differences or Structural
Differences in Response
358
Excel
12.1
Add indicator interactions to capture segment differences
or structural differences in response
359
Lab Practice
12 370
CASE
12-1
Explain and Forecast Defense Spending for Rolls-Royce
372
CASE
12-2
Haier 's U.S. Refrigerator Strategy
375
Chapter
13
Logit Regression for Bounded Responses
377
13.1
Rescaling Probabilities or Shares to Odds Improves Model Validity
377
Example
13.1
The Import Challenge
378
MEMO Re: Fuel Efficiency Drives Hybrid Owner Satisfaction
385
Example
13.2
Presidential Approval Proportion
386
Contents
13.2 Logit Models
Provide the Means to Build Valid Models of Shares
And Proportions
390
Excel
13.1
Rescale a
limited dependent variable to logits
391
Assignment
13-1
Big Drug Co Scripts
399
CASE
13-1
Alltel'
s
Plans to Capture Share in the Cell Phone
Service Market
400
CASE
13-2
Pilgrim Bank (A): Profitability and Pilgrim
Bank (B): Customer Retention
403
Index
405 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Fraser, Cynthia |
| author_GND | (DE-588)134080114 |
| author_facet | Fraser, Cynthia |
| author_role | aut |
| author_sort | Fraser, Cynthia |
| author_variant | c f cf |
| building | Verbundindex |
| bvnumber | BV023327505 |
| callnumber-first | H - Social Science |
| callnumber-label | HD30 |
| callnumber-raw | HD30.23 |
| callnumber-search | HD30.23 |
| callnumber-sort | HD 230.23 |
| callnumber-subject | HD - Industries, Land Use, Labor |
| classification_rvk | ST 371 |
| ctrlnum | (OCoLC)602265783 (DE-599)DNB988178060 |
| discipline | Informatik Wirtschaftswissenschaften |
| discipline_str_mv | Informatik Wirtschaftswissenschaften |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>00000nam a2200000 c 4500</leader><controlfield tag="001">BV023327505</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20090112</controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">080604s2009 d||| |||| 00||| eng d</controlfield><datafield tag="015" ind1=" " ind2=" "><subfield code="a">08,N17,0250</subfield><subfield code="2">dnb</subfield></datafield><datafield tag="016" ind1="7" ind2=" "><subfield code="a">988178060</subfield><subfield code="2">DE-101</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9780387744025</subfield><subfield code="c">Pb. : EUR 64.15 (freier Pr.), sfr 99.50 (freier Pr.)</subfield><subfield code="9">978-0-387-74402-5</subfield></datafield><datafield tag="024" ind1="3" ind2=" "><subfield code="a">9780387744025</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">12103577</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)602265783</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DNB988178060</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rakddb</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">HD30.23</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 371</subfield><subfield code="0">(DE-625)143672:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">330</subfield><subfield code="2">sdnb</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Fraser, Cynthia</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)134080114</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Business statistics for competitive advantage with Excel 2007</subfield><subfield code="b">basics, model building, and cases</subfield><subfield code="c">Cynthia Fraser</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">New York, NY</subfield><subfield code="b">Springer</subfield><subfield code="c">2009</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">XVIII, 410 S.</subfield><subfield code="b">graph. Darst.</subfield><subfield code="c">279 mm x 216 mm</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="630" ind1="0" ind2="4"><subfield code="a">Microsoft Excel (Computer file)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mathematisches Modell</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Commercial statistics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Decision making</subfield><subfield code="x">Mathematical models</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mathematical statistics</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Excel 2007</subfield><subfield code="0">(DE-588)7558101-2</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Wettbewerbsvorteil</subfield><subfield code="0">(DE-588)4219652-8</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Statistik</subfield><subfield code="0">(DE-588)4056995-0</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Excel 2007</subfield><subfield code="0">(DE-588)7558101-2</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Statistik</subfield><subfield code="0">(DE-588)4056995-0</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">Wettbewerbsvorteil</subfield><subfield code="0">(DE-588)4219652-8</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">Digitalisierung UB Bayreuth</subfield><subfield code="q">application/pdf</subfield><subfield code="u">http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016511494&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA</subfield><subfield code="3">Inhaltsverzeichnis</subfield></datafield></record></collection> |
| id | DE-604.BV023327505 |
| illustrated | Illustrated |
| index_date | 2024-07-02T20:56:16Z |
| indexdate | 2024-07-20T07:52:50Z |
| institution | BVB |
| isbn | 9780387744025 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016511494 |
| oclc_num | 602265783 |
| open_access_boolean | |
| owner | DE-703 |
| owner_facet | DE-703 |
| physical | XVIII, 410 S. graph. Darst. 279 mm x 216 mm |
| publishDate | 2009 |
| publishDateSearch | 2009 |
| publishDateSort | 2009 |
| publisher | Springer |
| record_format | marc |
| spelling | Fraser, Cynthia Verfasser (DE-588)134080114 aut Business statistics for competitive advantage with Excel 2007 basics, model building, and cases Cynthia Fraser New York, NY Springer 2009 XVIII, 410 S. graph. Darst. 279 mm x 216 mm txt rdacontent n rdamedia nc rdacarrier Microsoft Excel (Computer file) Mathematisches Modell Commercial statistics Decision making Mathematical models Mathematical statistics Excel 2007 (DE-588)7558101-2 gnd rswk-swf Wettbewerbsvorteil (DE-588)4219652-8 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Excel 2007 (DE-588)7558101-2 s Statistik (DE-588)4056995-0 s Wettbewerbsvorteil (DE-588)4219652-8 s DE-604 Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016511494&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Fraser, Cynthia Business statistics for competitive advantage with Excel 2007 basics, model building, and cases Microsoft Excel (Computer file) Mathematisches Modell Commercial statistics Decision making Mathematical models Mathematical statistics Excel 2007 (DE-588)7558101-2 gnd Wettbewerbsvorteil (DE-588)4219652-8 gnd Statistik (DE-588)4056995-0 gnd |
| subject_GND | (DE-588)7558101-2 (DE-588)4219652-8 (DE-588)4056995-0 |
| title | Business statistics for competitive advantage with Excel 2007 basics, model building, and cases |
| title_auth | Business statistics for competitive advantage with Excel 2007 basics, model building, and cases |
| title_exact_search | Business statistics for competitive advantage with Excel 2007 basics, model building, and cases |
| title_exact_search_txtP | Business statistics for competitive advantage with Excel 2007 basics, model building, and cases |
| title_full | Business statistics for competitive advantage with Excel 2007 basics, model building, and cases Cynthia Fraser |
| title_fullStr | Business statistics for competitive advantage with Excel 2007 basics, model building, and cases Cynthia Fraser |
| title_full_unstemmed | Business statistics for competitive advantage with Excel 2007 basics, model building, and cases Cynthia Fraser |
| title_short | Business statistics for competitive advantage with Excel 2007 |
| title_sort | business statistics for competitive advantage with excel 2007 basics model building and cases |
| title_sub | basics, model building, and cases |
| topic | Microsoft Excel (Computer file) Mathematisches Modell Commercial statistics Decision making Mathematical models Mathematical statistics Excel 2007 (DE-588)7558101-2 gnd Wettbewerbsvorteil (DE-588)4219652-8 gnd Statistik (DE-588)4056995-0 gnd |
| topic_facet | Microsoft Excel (Computer file) Mathematisches Modell Commercial statistics Decision making Mathematical models Mathematical statistics Excel 2007 Wettbewerbsvorteil Statistik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016511494&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT frasercynthia businessstatisticsforcompetitiveadvantagewithexcel2007basicsmodelbuildingandcases |