Business statistics for contemporary decision making
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| 020 | |a 9780470931462 |c hbk |9 978-0-470-93146-2 | ||
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| 100 | 1 | |a Black, Ken |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Business statistics for contemporary decision making |c Ken Black |
| 250 | |a 7. ed. | ||
| 264 | 1 | |a Hoboken, NJ |b Wiley |c 2012 | |
| 300 | |a XXVII, 850 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
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IMITAI li
UNIT
IVI
INTRODUCTION
1
Introduction
to Statistics
02
2
Charts and Graphs
18
3
Descriptive Statistics
52
4
Probability
98
DISTRIBUTIONS AND SAMPLING
5
Discrete Distributions
142
6
Continuous Distributions
184
7
Sampling and Sampling Distributions
222
MAKING INFERENCES ABOUT POPULATION
PARAMETERS
8
Statistical Inference: Estimation for Single Populations
9
Statistical Inference: Hypothesis Testing for Single
Populations
294
10
Statistical Inferences about Two Populations
348
11
Analysis of Variance and Design of Experiments
408
REGRESSION ANALYSIS AND FORECASTING
12
Simple Regression Analysis and Correlation
470
13
Multiple Regression Analysis
522
14
Building Multiple Regression Models
552
15
Time-Series Forecasting and Index Numbers
602
NONPARAMETRIC STATISTICS AND QUALITY
16
Analysis of Categorical Data
658
17
Nonparametric Statistics
684
18
Statistical Quality Control
734
APPENDICES
A Tables
779
В
Answers to Selected Odd-Numbered Quantitative
Problems
819
GLOSSARY
829
INDEX
839
256
19
Supplement
1
Supplement
2
Supplement
3
The following materials are available at www.wiley.com/college/black
Decision Analysis C19-2
Summation Notation Sl-l
Derivation of Simple Regression Formulas for Slope
and
у
Intercept S2-1
Advanced Exponential Smoothing S3-1
viii
CONTENTS
Preface
xvii
About the Author
xxvii
U N
I T;* I
INTRODUCTION
1
Introduction to Statistics
02
Decision Dilemma: Statistics Describe the State
of Business in India s Countryside
03
1.1
Statistics in Business
04
1.2
Basic Statistical Concepts
05
1.3
Variables and Data
07
1.4
Data Measurement
07
Nominal Level
08
Ordinal Level
08
Interval Level
09
Ratio Level
09
Comparison of the Four Levels of Data
10
Statistical Analysis Using the Computer:
Excel and Jviinitab
11
Summary
13
Key Terms
13
Supplementary Problems
13
Analyzing the Databases
14
Case; DiGiorno Pizza: Introducing a Frozen Pizza to
Compete with Carry-Out
16
2
Charts and Graphs
18
Decision Dilemma: Container Shipping Companies
19
21
Frequency Distributions
20
Class Midpoint
20
Relative Frequency
21
Cumulative Frequency
21
Quantitative Data Graphs
23
Histograms
23
Using Histograms to Get an Initial Overview
of the Data
25
Frequency Polygons
25
Ogives
26
Dot Plots
27
Stem-and-Leaf Plots
27
Qualitative Data
Graphs
31
Pie Charts
31
Bar Graphs
32
Pareto Charts
34
Charts and Graphs for Two Variables
38
Cross Tabulation
38
Scatter Plot
38
2.2
2.3
2.4
Summary
42
Key Terms
43
Supplementary Problems
43
Analyzing the Databases
47
Case: Soap Companies Do Battle
48
Using the Computer
49
3
Descriptive Statistics
52
Decision Dilemma: Laundry Statistics
53
3.1
Measures of Central Tendency:
Ungrouped Data
53
Mode
54
Median
54
Mean
55
Percentiles
57
Steps in Determining the Location of
a Percentile
57
Quartiles
58
3.2
Measures of Variability: Ungrouped Data
61
Range
61
Interquartile Range
62
Mean Absolute Deviation, Variance, and
Standard Deviation
63
Mean Absolute Deviation
64
Variance
65
Standard Deviation
66
Meaning of Standard Deviation
66
Empirical Rule
66
Chebyshev s Theorem
68
Population Versus Sample Variance and
Standard Deviation
69
Computational Formulas for Variance and
Standard Deviation
70
z
Scores
72
Coefficient of Variation
73
3.3
Measures of Central Tendency and
Variability: Grouped Data
76
Measures of Central Tendency
76
Mean
77
Median
77
Mode
78
Measures of Variability
78
3.4
Measures of Shape
83
Skewness
83
Skewness and the Relationship of the Mean,
Median, and Mode
84
Coefficient of Skewness
84
Kurtosis
84
Box-and-Whisker Plots and Five-Number
Summary
85
ix
X
Contents
3.5
Descriptive Statistics on the Computer
87
Summary
89
Key Terms
90
Formulas
90
Supplementary Problems
91
Analyzing the Databases
95
Case: Coca-Cola Develops the African Market
95
Using the Computer
97
4
Probability
98
Decision Dilemma: Equity of the Sexes in the
Workplace
99
4.1
Introduction to Probability
100
4.2
Methods of Assigning Probabilities
100
Classical Method of Assigning Probabilities
100
Relative Frequency of Occurrence
101
Subjective Probability
102
4.3
Structure of Probability
102
Experiment
102
Event
102
Elementary Events
102
Sample Space
103
Unions and Intersections
103
Mutually Exclusive Events
104
Independent Events
104
Collectively Exhaustive Events
105
Complementary Events
105
Counting the Possibilities
105
The mn Counting Rule
105
Sampling from a Population with
Replacement
106
Combinations: Sampling from a Population
Without Replacement
106
4.4
Marginal, Union, Joint, and Conditional
Probabilities
107
4.5
Addition Laws
109
Joint Probability Tables
110
Complement of a Union
113
Special Law of Addition
114
4.6
Multiplication Laws
117
General Law of Multiplication
117
Special Law of Multiplication
119
4.7
Conditional Probability
122
Independent Events
125
4.8
Revision of Probabilities:
Bayes
Rule
129
Summary
134
Key Terms
134
Formulas
135
Supplementary Problems
135
Analyzing the Databases
139
Case: Colgate-Palmolive Makes a Total Effort
139
DISTRIBUTIONS AND SAMPLING
5
Discrete Distributions
142
Decision Dilemma: Life with a Cell Phone
143
5.1
Discrete Versus Continuous Distributions
144
5.2
Describing a Discrete Distribution
145
Mean, Variance, and Standard Deviation of
Discrete Distributions
146
Mean or Expected Value
146
Variance and Standard Deviation of a
Discrete Distribution
146
5.3
Binomial Distribution
149
Solving a Binomial Problem
150
Using the Binomial Table
153
Using the Computer to Produce a Binomial
Distribution
154
Mean and Standard Deviation of a Binomial
Distribution
155
Graphing Binomial Distributions
156
5.4
Poisson
Distribution
160
Working
Poisson
Problems by Formula
162
Using the
Poisson
Tables
163
Mean and Standard Deviation of
a Poisson
Distribution
164
Graphing
Poisson
Distributions
165
Using the Computer to Generate
Poisson
Distributions
165
Approximating Binomial Problems by the
Poisson
Distribution
166
5.5
Hypergeometric Distribution
170
Using the Computer to Solve for Hypergeometric
Distribution Probabilities
172
Summary
175
Key Terms
175
Formulas
176
Supplementary Problems
176
Analyzing the Databases
181
Case: Whole Foods Market Grows Through Mergers and
Acquisitions
181
Using the Computer
182
6
Continuous Distributions
184
Decision Dilemma: The Cost of Human Resources
185
6.1
The Uniform Distribution
185
Determining Probabilities in a Uniform
Distribution
187
Using the Computer to Solve for Uniform
Distribution Probabilities
189
6.2
Normal Distribution
190
History of the Normal Distribution
191
Contents Xl
Probability Density Function of the Normal
Distribution
191
Standardized Normal Distribution
192
Solving Normal Curve Problems
193
Using the Computer to Solve for Normal
Distribution Probabilities
200
6.3
Using the Normal Curve to Approximate
Binomial Distribution Problems
202
Correcting for Continuity
204
6.4
Exponential Distribution
208
Probabilities of the Exponential
Distribution
209
Using the Computer to Determine Exponential
Distribution Probabilities
211
Summary
213
Key Terms
214
Formulas
214
Supplementary Problems
214
Analyzing the Databases
218
Case: Mercedes Goes After Younger
Buyers
218
Using the Computer
219
7
Sampling and Sampling
Distributions
222
Decision Dilemma: What Is the Attitude of
Maquiladora
Workers?
223
7.1
Sampling
223
Reasons for Sampling
224
Reasons for Taking a Census
224
Frame
225
Random Versus Nonrandom
Sampling
226
Random Sampling Techniques
226
Simple Random Sampling
226
Stratified Random Sampling
228
Systematic Sampling
229
Cluster (or Area) Sampling
229
Nonrandom Sampling
230
Convenience Sampling
231
Judgment Sampling
232
Quota Sampling
232
Snowball Sampling
232
Sampling Error
233
Nonsampling
Errors
233
7·2
Sampling Distribution of
χ
234
Sampling from a Finite Population
241
7-3
Sampling Distribution of
ρ
244
Summary
248
Key Terms
248
Formulas
248
Supplementary Problems
249
Analyzing the Databases
251
Case: Shell Attempts to Return to Premiere Status
251
Using the Computer
252
MAKING INFERENCES ABOUT
POPULATION PARAMETERS
8
Statistical Inference: Estimation
for Single Populations
256
Decision Dilemma: Compensation for
Purchasing Managers
257
8.1
Estimating the Population Mean Using the
ζ
Statistic
{σ
Known)
259
Finite Correction Factor
262
Estimating the Population Mean Using the
ζ
Statistic when the Sample Size Is Small
263
Using the Computer to Construct
ζ
Confidence
Intervals for the Mean
264
8.2
Estimating the Population Mean Using the
t
Statistic
(σ
Unknown)
266
The
f
Distribution
267
Robustness
267
Characteristics of the
ŕ
Distribution
267
Reading the
ŕ
Distribution Table
267
Confidence Intervals to Estimate the Population
Mean Using the
f
Statistic
268
Using the Computer to Construct
t
Confidence
Intervals for the Mean
270
8.3
Estimating the Population Proportion
273
Using the Computer to Construct Confidence
Intervals of the Population Proportion
276
8.4
Estimating the Population Variance
277
8.5
Estimating Sample Size
281
Sample Size when Estimating
μ
281
Determining Sample Size when Estimating
ρ
283
Summary
286
Key Terms
286
Formulas
286
Supplementary Problems
287
Analyzing the Databases
290
Case: The Container Store
290
Using the Computer
291
9
Statistical Inference: Hypothesis
Testing for Single Populations
294
Decision Dilemma: Word-of-Mouth Business Referrals
and Influential
295
9.1
Introduction to Hypothesis Testing
296
Types of Hypotheses
297
Research Hypotheses
297
Xli Contents
Statistical Hypotheses
298
Substantive Hypotheses
300
Using the HTAB System to
Test Hypotheses
301
Rejection and Nonrejection Regions
303
Type I and Type II Errors
304
9.2
Testing Hypotheses About a Population
Mean Using the
z
Statistic
[σ
Known)
305
Testing the Mean with a Finite Population
307
Using the p-Value to Test Hypotheses
308
Using the Critical Value Method to
Test Hypotheses
309
Using the Computer to Test Hypotheses About a
Population Mean Using the
ζ
Statistic
312
9.3
Testing Hypotheses About a
Population Mean Using the {Statistic
(σ
Unknown)
314
Using the Computer to Test Hypotheses About a
Population Mean Using the
f
Test
318
9.4
Testing Hypotheses About a Proportion
321
Using the Computer to Test Hypotheses About a
Population Proportion
325
9.5
Testing Hypotheses About a Variance
327
9.6
Solving for Type II Errors
330
Some Observations About Type II Errors
335
Operating Characteristic and Power Curves
335
Effect of Increasing Sample Size on the
Rejection Limits
337
Summary
340
Key Terms
341
Formulas
341
Supplementary Problems
341
Analyzing the Databases
344
Case: Frito-Lay Targets the Hispanic Market
344
Using the Computer
346
10
Statistical Inferences about
Two Populations
348
Decision Dilemma: Online Shopping
349
10.1
Hypothesis Testing and Confidence
Intervals About the Difference in
Two Means Using the
ζ
Statistic
(Population Variances Known)
352
Hypothesis Testing
353
Confidence Intervals
356
Using the Computer to Test Hypotheses About
the Difference in Two Population Means
Using the
ζ
Test
358
10.2
Hypothesis Testing and Confidence Intervals
About the Difference in Two Means:
Independent Samples and Population
Variances Unknown
361
Hypothesis Testing
361
Using the Computer to Test Hypotheses and
Construct Confidence Intervals about the
Difference in Two Population Means Using the
t
Test
363
Confidence Intervals
366
10.3
Statistical Inferences for Two Related
Populations
371
Hypothesis Testing
371
Using the Computer to Make Statistical Inferences
about Two Related Populations
373
Confidence Intervals
376
10.4
Statistical Inferences About Two Population
Proportions,
ρλ
-
p2
381
Hypothesis Testing
381
Confidence Intervals
385
Using the Computer to Analyze the Difference
in Two Proportions
386
10.5
Testing Hypotheses About Two Population
Variances
388
Using the Computer to Test Hypotheses About
Two Population Variances
392
Summary
397
Key Terms
397
Formulas
397
Supplementary Problems
398
Analyzing the Databases
403
Case: Seitz Corporation: Producing Quality Gear-Driven and
Linear-Motion Products
403
Using the Computer
404
11
Analysis of Variance and Design
of Experiments
408
Decision Dilemma: Job and Career Satisfaction of Foreign
Self-Initiated Expatriates
409
11.1
Introduction to Design of Experiments
410
11.2
The Completely Randomized Design
{One-Way ANOVA)
412
One-Way Analysis of Variance
413
Reading the
F
Distribution Table
417
Using the Computer for One-Way ANOVA
417
Comparison of
Fand
ŕ
Values
418
11.3
Multiple Comparison Tests
424
Tukey s Honestly Significant Difference (HSD) Test:
The Case of Equal Sample Sizes
424
Using the Computer to Do Multiple
Comparisons
426
Tukey-Kramer Procedure: The Case of Unequal
Sample Sizes
428
11.4
The Randomized Block Design
432
Using the Computer to Analyze Randomized Block
Designs
436
11.5
A Factorial Design (Two-Way ANOVA)
442
Advantages of the Factorial Design
442
Factorial Designs with Two Treatments
443
Contents
ХШ
Applications 443
Statistically Testing the Factorial Design
444
Interaction
445
Using a Computer to Do a Two-Way ANOVA
450
Summary
459
Key Terms
460
Formulas
460
Supplementary Problems
461
Analyzing the Databases
464
Case: The
Clarkson
Company: A Division of Tyco
International
465
Using the Computer
466
REGRESSION ANALYSIS AND
FORECASTING
12
Simple Regression Analysis
and Correlation
470
Decision Dilemma: Predicting International Hourly
Wages by the Price of a Big Mac
471
12.1
12.2
12.3
12.4
12.5
12.6
12.7
Correlation
472
Introduction to Simple Regression
Analysis
475
Determining the Equation of
the Regression Line
476
Residual Analysis
483
Using Residuals to Test the Assumptions of the
Regression Model
485
Using the Computer for Residual Analysis
486
Standard Error of the Estimate
490
Coefficient of Determination
493
Relationship Between rand r2
495
Hypothesis Tests for the Slope of the
Regression Model and Testing the Overall
Model
495
Testing the Slope
495
Testing the Overall Model
499
Estimation
500
Confidence Intervals to Estimate the Conditional
Mean of y:
μΫίχ
500
Prediction Intervals to Estimate a Single
Value of
y
501
Using Regression to Develop a Forecasting
Trend Line
504
Determining the Equation of the Trend Line
505
Forecasting Using the Equation of the
Trend Line
506
Alternate Coding for Time Periods
507
42.10
Interpreting the Output
510
Summary
514
Key Term
с
12.8
12.9
Formulas
515
Supplementary Problems
515
Analyzing the Databases
519
Case: Delta Wire Uses Training as a Weapon
519
Using the Computer
521
13
Multiple Regression Analysis
522
Decision Dilemma: Are You Going to Hate Your
New Job?
523
13.1
The Multiple Regression Model
524
Multiple Regression Model with Two Independent
Variables (First-Order)
525
Determining the Multiple Regression Equation
526
A Multiple Regression Model
526
13.2
Significance Tests of the Regression Model
and Its Coefficients
531
Testing the Overall Model
531
Significance Tests of the Regression
Coefficients
533
13.3
Residuals, Standard Error of the Estimate,
and R2
536
Residuals
536
SSE and Standard Error of the Estimate
537
Coefficient of Multiple Determination
(fî2)
538
Adjusted R2
539
13.4
Interpreting Multiple Regression Computer
Output
541
A Reexaminaron
of the Multiple
Regression Output
541
Summary
545
Key Terms
546
Formulas
546
Supplementary Problems
546
Analyzing the Databases
549
Case: Starbucks Introduces Debit Card
549
Using the Computer
550
14
Building Multiple Regression
Models
552
Decision Dilemma: Determining Compensation
for CEOs
553
14.1
Nonlinear Models: Mathematical
Transformation
554
Polynomial Regression
554
Tukey s Ladder of Transformations
557
Regression Models with Interaction
558
Model Transformation
560
14.2
Indicator (Dummy) Variables
566
14.3
Model-Building: Search Procedures
572
Search Procedures
574
All Possible Regressions
574
XIV Contents
Stepwise Regression 574
Forward Selection
578
Backward Elimination
578
14.4
Multicollinearity
582
14.5
Logistic Regression
584
An Example
584
The Logistic Regression Model
586
Interpreting the Output
587
Determining Logistic Regression Model
Testing the Overall Model
588
Testing Individual Predictor Variables
589
Summary
593
Key Terms
594
Formulas
594
Supplementary Problems
595
Analyzing the Databases
598
Case: Virginia Semiconductor
598
Using the Computer
600
15
Time-Series Forecasting and
Index Numbers
602
588
15.2
608
608
611
15.3
Decision Dilemma: Forecasting Air Pollution
603
15.1
Introduction to Forecasting
604
Time-Series Components
604
The Measurement of Forecasting Error
605
Error
605
Mean Absolute Deviation (MAD)
605
Mean Square Error
(MSE)
606
Smoothing Techniques
Naive Forecasting Models
Averaging Models
609
Simple Averages
609
Moving Averages
609
Weighted Moving Averages
Exponential Smoothing
613
Trend Analysis
618
Linear Regression Trend Analysis
618
Regression Trend Analysis Using
Quadratic Models
620
Holt s Two-Parameter Exponential Smoothing
Method
623
Seasonal Effects
625
Decomposition
625
Finding Seasonal Effects with the Computer
628
Winters Three-Parameter Exponential Smoothing
Method
628
Autocorrelation and
Autoregression
Autocorrelation
630
Ways to Overcome the Autocorrelation
Problem
633
Addition of Independent Variables
633
Transforming Variables
634
Autoregression 634
15.4
15.5
630
15.6
Index Numbers
637
Simple Index Numbers
638
Unweighted Aggregate Price Index Numbers
638
Weighted Aggregate Price Index Numbers
639
Laspeyres Price Index
640
Paasche Price Index
641
Summary
646
Key Terms
647
Formulas
647
Supplementary Problems
647
Analyzing the Databases
652
Case: Debourgh Manufacturing Company
653
Using the Computer
654
NONPAR AM ETRIC STATISTICS
AND QUALITY
16
Analysis of Categorical Data
658
Decision Dilemma: Selecting Suppliers in the Electronics
Industry
659
16.1
Chi-Square Goodness-of-Fit Test
660
Testing a Population Proportion by Using the
Chi-Square Goodness-of-Fit Test as an
Alternative Technique to the
z
Test
666
16.2
Contingency Analysis: Chi-Square Test
of Independence
670
Summary
680
Key Terms
680
Formulas
680
Supplementary Problems
680
Analyzing the Databases
682
Case: Foot Locker in the Shoe Mix
682
Using the Computer
683
17
Nonparametric Statistics
684
Decision Dilemma: How Is the Doughnut
Business?
685
17.1
Runs Test
687
Small-Sample Runs Test
688
Large-Sample Runs Test
689
17.2
Mann-Whitney
U
Test
692
Small-Sample Case
692
Large-Sample Case
694
17.3
Wilcoxon Matched-Pairs Signed
Rank Test
700
Small-Sample Case
(n s
15) 700
Large-Sample Case (n
> 15) 702
17.4
Kruskal-Wallis Test
708
Contents
XV
17.5 Friedman Test 713
17.6
Spearman s Rank Correlation
719
Summary
724
Key
Terms
725
Formulas
725
Supplementary Problems
725
Analyzing the Databases
730
Case: Schwinn
731
Using the Computer
732
18
Statistical Quality Control
734
Decision Dilemma: Italy s
Piaggio
Makes a Comeback
735
18.1
Introduction to Quality Control
736
What Is Quality Control?
736
Total Quality Management
737
Deming s
U
Points
738
Quality Gurus
739
Six Sigma
739
Design for Six Sigma
741
Lean Manufacturing
741
Some Important Quality Concepts
741
Benchmarking
742
Just-in-Time Inventory Systems
742
Reengineering
743
Failure Mode and Effects Analysis
744
Poka-Yoke
745
Quality Circles and Six Sigma Teams
745
18.2
Process Analysis
747
Flowcharts
747
Pareto Analysis
748
Cause-and-Effect (Fishbone) Diagrams
749
Control Charts
750
Check Sheets or Checklists
751
Histogram
752
Scatter Chart or Scatter Diagram
752
18.3
Control Charts
753
Variation
754
Types of Control Charts
754
χ
Chart
754
R
Charts
758
p
Charts
759
с
Charts
762
Interpreting Control Charts
764
Summary
770
Key Terms
771
Formulas
771
Supplementary Problems
772
Analyzing the Databases
775
Case: Robotron-ELOTHERM
776
Using the Computer
777
APPENDICES
A Tables
779
В
Answers to Selected Odd-Numbered
Quantitative Problems
819
GLOSSARY
829
INDEX
839
The following materials are available at www.wiley.com/college/black
19
Decision Analysis C19-2
Décision
Dilemma: Decision Making at the CEO Level C19-3
19.1
The Decision Table and Decision Making
Under Certainty C19-4
Decision Table C19-4
Decision Making Under Certainty C19-5
19.2
Decision Making Under Uncertainty C19-6
Maximax Criterion C19-6
Maximin
Criterion C19-6
Hurwicz Criterion C19-7
Minimax Regret C19-9
19.3
Decision Making Under Risk C19-14
Decision Trees C19-14
Expected Monetary Value (EMV) C19-14
Expected Value of Perfect Information C19-18
Utility C19-19
19.4
Revising Probabilities in Light of Sample
Information C19-22
Expected Value of Sample Information C19-25
Summary C19-32
Key Terms C19-33
Formula C19-33
Supplementary Problems C19-33
Analyzing the Databases C19-36
Case: Fletcher-Terry: On the Cutting Edge C19-36
SUPPLEMENTS
1
Summation Notation Sl-l
2
Derivation of Simple Regression
Formulas for Slope and
y
Intercept S2-1
3
Advanced Exponential Smoothing S3-1
Exponential Smoothing with Trend Effects:
Holt s Method S3-1
Exponential Smoothing with Both Trend and
Seasonally: Winter s Method S3-2
Some Practice Problems S3-5
|
| any_adam_object | 1 |
| author | Black, Ken |
| author_facet | Black, Ken |
| author_role | aut |
| author_sort | Black, Ken |
| author_variant | k b kb |
| building | Verbundindex |
| bvnumber | BV039799393 |
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| ctrlnum | (OCoLC)751717348 (DE-599)BVBBV039799393 |
| dewey-full | 519.502465 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.502465 |
| dewey-search | 519.502465 |
| dewey-sort | 3519.502465 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik Wirtschaftswissenschaften |
| edition | 7. ed. |
| format | Book |
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| id | DE-604.BV039799393 |
| illustrated | Illustrated |
| indexdate | 2024-09-27T16:26:41Z |
| institution | BVB |
| isbn | 9780470931462 9781118024119 |
| language | English |
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| spellingShingle | Black, Ken Business statistics for contemporary decision making Betriebswirtschaftliche Statistik (DE-588)4006243-0 gnd |
| subject_GND | (DE-588)4006243-0 (DE-588)4123623-3 |
| title | Business statistics for contemporary decision making |
| title_auth | Business statistics for contemporary decision making |
| title_exact_search | Business statistics for contemporary decision making |
| title_full | Business statistics for contemporary decision making Ken Black |
| title_fullStr | Business statistics for contemporary decision making Ken Black |
| title_full_unstemmed | Business statistics for contemporary decision making Ken Black |
| title_short | Business statistics for contemporary decision making |
| title_sort | business statistics for contemporary decision making |
| topic | Betriebswirtschaftliche Statistik (DE-588)4006243-0 gnd |
| topic_facet | Betriebswirtschaftliche Statistik Lehrbuch |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024659897&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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