Data driven business decisions:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Hoboken, NJ
Wiley
2011
|
| Schriftenreihe: | Statistics in practice
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Includes index |
| Beschreibung: | XVIII, 488 S. graph. Darst. 1 CD-ROM (12 cm) |
| ISBN: | 9780470619605 |
Internformat
MARC
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| 035 | |a (DE-599)BVBBV037385612 | ||
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| 100 | 1 | |a Lloyd, Christopher J. |e Verfasser |0 (DE-588)171827791 |4 aut | |
| 245 | 1 | 0 | |a Data driven business decisions |c Chris J. Lloyd |
| 246 | 1 | 3 | |a Data-driven business decisions |
| 264 | 1 | |a Hoboken, NJ |b Wiley |c 2011 | |
| 300 | |a XVIII, 488 S. |b graph. Darst. |e 1 CD-ROM (12 cm) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Statistics in practice | |
| 500 | |a Includes index | ||
| 650 | 4 | |a Decision making |x Statistical methods | |
| 650 | 4 | |a Problem solving |x Statistical methods | |
| 650 | 4 | |a Statistical decision | |
| 650 | 4 | |a Data mining | |
| 856 | 4 | 2 | |m Digitalisierung UB Regensburg |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=022538636&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-022538636 | ||
Datensatz im Suchindex
| _version_ | 1804145672643936256 |
|---|---|
| adam_text | Contents
Preface
To the Student
To the Teacher: How to Build a Course Around This Book
XIII
xv
XVII
chapter
1
How Are We Doing? Data-Driven Views of
Business Performance
1.1
Setting Out Business Data
1.2
Different Kinds of Variables
1.3
The Idea of a Distribution
1.4
Typical Performance: The Sample Mean
1.5
Uncertainty in Performance: SD
1.6
Changing Units
1.7
Shapes of Distributions
1
2
5
8
ІЗ
15
18
20
chapter
2
What Stands Out and Why? Who Wins?
Data-Driven Views of Performance Dynamics
25
2.1
Different Layouts of Business Data
27
2.2
Comparing Performance across Different Segments
29
2.3
Complex Comparisons: Using Pivotables
30
2.4
Unusually High or Low Outcomes: z-Scores
35
2.5
Homogeneous Peer Groups
39
2.6
Combining Different Performance Measures
41
chapter
3
Dealing with Uncertainty and Chance
51
3.1
Framing What Could Happen: Outcomes and Events
52
3.2
How Likely Is It? Probability Basics
55
3.3
Market Segments and Behavior; Probability Tables
57
3.4
Example in Health Care: Testing for a Disease
59
3.5
Conditional Probability
61
3.6
How Strong Is the Relationship? Measuring Dependence
66
3.7
Probability Trees
71
VII
CONTENTS
chapter
4
Let the Data Change Your Views:
The
Bayes
Method
79
4.1
The
Bayes
Method in Pictures
80
4.2
The
Bayes
Method as an Algorithm
81
4.3
Example
1:
A Simple Gambling Game
83
4.4
Example
2:
Bayes
in the Courtroom
87
4.5
Some Typical Business Applications
90
chapter
5
Valuing an Uncertain Payoff
97
5.1
What Is a Probability Distribution?
98
5.2
Displaying a Probability Distribution
100
5.3
The Mean of a Distribution
103
5.4
Example: Fines and Violations
104
5.5
Why Use the Mean?
107
5.6
The Standard Deviation of a Distribution
109
5.7
Comparing Two Distributions
112
5.8
Conditional Distributions and Means
114
chapter
6
Business Problems That Depend on Knowing
How Many
121
6.1
The Binomial Distribution
123
6.2
The Mean and Standard Deviation
125
6.3
The Negative Binomial Distribution
128
6.4
The
Poisson
Distribution
132
6.5
Some Typical Business Applications
135
chapter
7
Business Problems That Depend on Knowing
How Much
141
7.1
The Normal Distribution
142
7.2
Calculating Normal Probabilities in Excel
145
7.3
Combining Normal Variables
149
7.4
Comparing Two Normal Distributions
152
7.5
The Standard Normal Distribution
153
7.6
Example
3:
Dealing with Uncertain Demand
156
7.7
Dealing with Proportional Variation
160
chapter
8
Making Complex Decisions with Trees
169
8.1
Elements of Decision Trees
171
8.2
Solving the Decision Tree
175
8.3
Multistage Decision Trees
181
8.4
Valuing a Decision Option
186
8.5
The Cost of Uncertainty
188
chapter
9
Data, Estimation, and Statistical Reliability
195
9.1
Describing the Past and the Future
197
9.2
How Were the Data Generated?
199
CONTENTS
9.3
Law of Large Numbers
200
9.4
The Variability of the Sample Mean
201
9.5
The Standard Error of the Mean
204
9.6
The Normal Limit Theorem
208
9.7
Samples and Populations
212
chapter
1
0
Managing Mean Performance
219
10.1
Benchmarking Mean Performance
221
10.2
The Statistical Size of a Deviation
224
10.3
Decision Making, Hypothesis Testing, and p-Values
226
10.4
Confidence Intervals
230
10.5
One-Sided and Two-Sided Tests
232
10.6
Using StatproGo
232
10.7
Why Standard Deviation Matters
234
10.8
Assessing Detection Power
235
chapter
1 1
Are These Customers Different? Did the
Intervention Work? Looking at Changes
in Mean Performance
243
11.1
How Variable Is a Difference?
245
11.2
Describing Changes in Mean Performance
247
11.3
Example
2:
Is Product Placement Worth It?
249
11.4
Performing the
ŕ-Test
with StatproGo
255
11.5
Different Standard Deviations
258
11.6
Analyzing Matched-Pairs Data
261
chapter
12
What is My Brand Recognition? Will It Sell?
Analyzing Counts and Proportions
271
12.1
How Accurate Are Percentages?
272
12.2
Tests and Confidence Intervals for Proportions
277
12.3
Assessing Changes in Proportions
280
12.4
Using StatproGo
284
12.5
Alternative Methods
284
chapter
13
Using the Relationship between Shares
to Build a Portfolio
293
13.1
How to Measure Financial Growth
295
13.2
Risk and Return: Both Matter
298
13.3
Correlation and Industry Structure
300
13.4
The Riskiness of a Portfolio
306
13.5
Balancing Risk and Return
310
13.6
Controlling Risk with TBs
312
chapter
14
Investigating Relationships between
Business Variables
319
14.1
Measuring Association with Correlation
320
14.2
Looking at Complex Relationships
324
CONTENTS
14.3
Interpreting Correlations
328
14.4
What Is Autocorrelation?
331
14.5
Untangling Relationships with Partial Correlation
334
chapter
15
Describing the Effect of a Business Input:
Linear Regression
341
15.1
Linear Relationships
342
15.2
The Line of Best Fit
344
15.3
Computing the Least Squares Line
347
15.4
The Regression Model
351
15.5
How Reliable Is the Regression Line?
354
chapter
16
The Reliability of Regression-Based
Decisions
365
16.1
Three Kinds of Questions that Regression Answers
366
16.2
Estimating the Effect of a Change
369
16.3
Estimating the Trend Mean
370
16.4
Prediction
373
16.5
Prediction Errors and What They Tell You
374
chapter
17
Multicausal Relationships and
Multiple Regression
387
17.1
Multilinear Relationships
390
17.2
Multiple Regression
393
17.3
Model Assessment
400
17.4
Prediction and Trend Estimation
404
chapter
18
Product Features, Nonlinear Relationships,
and Market Segments
413
18.1
Accounting for Yes-No Features
415
18.2
Quadratic Relationships
417
18.3
Quadratic Regression
421
18.4
Allowing for Segments and Groups
425
18.5
Automatic Model Selection
429
chapter
19
Analyzing Data That Is Collected Regularly
Over Time
437
19.1
Measuring Growth and Seasonality
439
19.2
How Is the Growth Rate Changing?
443
19.3
Seasonally Adjusting Data
445
19.4
Delayed Effects
448
19.5
Predicting the Future (Using
Autoregression) 453
CONTENTS
XI
chapter
20
Extending Regression Models: The Sky Is
the Limit
461
20.1
Inputs That Have Varying Effects: Interactions
462
20.2
Inputs That Have Proportional Impacts
470
20.3
Case Study: How Effective Are Catalog Mail-Outs?
474
20.4
More on Time Series
478
Index
485
|
| any_adam_object | 1 |
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| author_GND | (DE-588)171827791 |
| author_facet | Lloyd, Christopher J. |
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| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 658 - General management |
| dewey-raw | 658.4/033 |
| dewey-search | 658.4/033 |
| dewey-sort | 3658.4 233 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Informatik Mathematik Wirtschaftswissenschaften |
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| id | DE-604.BV037385612 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T23:23:11Z |
| institution | BVB |
| isbn | 9780470619605 |
| language | English |
| lccn | 2010046373 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-022538636 |
| oclc_num | 730016591 |
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| physical | XVIII, 488 S. graph. Darst. 1 CD-ROM (12 cm) |
| publishDate | 2011 |
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| publisher | Wiley |
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| series2 | Statistics in practice |
| spelling | Lloyd, Christopher J. Verfasser (DE-588)171827791 aut Data driven business decisions Chris J. Lloyd Data-driven business decisions Hoboken, NJ Wiley 2011 XVIII, 488 S. graph. Darst. 1 CD-ROM (12 cm) txt rdacontent n rdamedia nc rdacarrier Statistics in practice Includes index Decision making Statistical methods Problem solving Statistical methods Statistical decision Data mining Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=022538636&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Lloyd, Christopher J. Data driven business decisions Decision making Statistical methods Problem solving Statistical methods Statistical decision Data mining |
| title | Data driven business decisions |
| title_alt | Data-driven business decisions |
| title_auth | Data driven business decisions |
| title_exact_search | Data driven business decisions |
| title_full | Data driven business decisions Chris J. Lloyd |
| title_fullStr | Data driven business decisions Chris J. Lloyd |
| title_full_unstemmed | Data driven business decisions Chris J. Lloyd |
| title_short | Data driven business decisions |
| title_sort | data driven business decisions |
| topic | Decision making Statistical methods Problem solving Statistical methods Statistical decision Data mining |
| topic_facet | Decision making Statistical methods Problem solving Statistical methods Statistical decision Data mining |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=022538636&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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