Basic business statistics: concepts and applications
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boston [u.a.]
Pearson
2015
|
| Ausgabe: | 13. ed., global ed. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | 840 S. Ill., graph. Darst. |
| ISBN: | 1292069023 9781292069029 9780321870025 |
Internformat
MARC
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| 100 | 1 | |a Berenson, Mark L. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Basic business statistics |b concepts and applications |c Mark L. Berenson ; David M. Levine ; Kathryn A. Szabat |
| 250 | |a 13. ed., global ed. | ||
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| 650 | 4 | |a Mathematical statistics | |
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Datensatz im Suchindex
| _version_ | 1804151994061946880 |
|---|---|
| adam_text | Titel: Basic business statistics
Autor: Berenson, Mark L
Jahr: 2015
Contents
Preface 19
Getting Started: Important
Things to Learn First 29
USING STATISTICS: You Cannot Escape from Data 29
GS.l Statistics: A Way of Thinking 30
GS.2 Data: What Is It? 31
GS.3 Business Analytics: The Changing Face of
Statistics 32
Big Data 32
Statistics: An Important Part of Your Business
Education 33
GS.4 Software and Statistics 34
Excel and Minitab Guides 34
REFERENCES 35
KEY TERMS 35
EXCEL GUIDE 36
EG1. Getting Started with Microsoft Excel 36
EG2. Entering Data 36
EG3. Opening and Saving Workbooks 37
EG4. Creating and Copying Worksheets 38
EG5. Printing Worksheets 38
MINITAB GUIDE 39
MG1. Getting Started with Minitab 39
MG2. Entering Data 39
MG3. Opening and Saving Worksheets and Projects 39
MG4. Creating and Copying Worksheets 40
MG5. Printing Parts of a Project 40
1 Defining and
Collecting Data 41
USING STATISTICS: Beginning of the End ... Or the End
of the Beginning? 41
1.1 Defining Data 42
Establishing the Variable Type 42
1.2 Measurement Scales for Variables 43
Nominal and Ordinal Scales 43
Interval and Ratio Scales 44
1.3 Collecting Data 46
Data Sources 46
Populations and Samples 47
Data Formatting 47
Data Cleaning 48
Recoding Variables 48
1.4 Types of Sampling Methods 49
Simple Random Sample 50
Systematic Sample 51
Stratified Sample 51
Cluster Sample 51
1.5 Types of Survey Errors 52
Coverage Error 53
Nonresponse Error 53
Sampling Error 53
Measurement Error 53
Ethical Issues About Surveys 54
THINK ABOUT THIS: New Media Surveys/Old Sampling
Problems 54
USING STATISTICS: Beginning of the End ... Revisited 55
SUMMARY 56
REFERENCES 56
KEY TERMS 56
CHECKING YOUR UNDERSTANDING 57
CHAPTER REVIEW PROBLEMS 57
CASES FOR CHAPTER 1 58
Managing Ashland MultiComm Services 58
CardioGood Fitness 58
Clear Mountain State Student Surveys 59
Learning with the Digital Cases 59
CHAPTER 1 EXCEL GUIDE 61
EG 1.1 Defining Data 61
EG1.2 Measurement Scales for Variables 61
EG1.3 Collecting Data 61
EG 1.4 Types of Sampling Methods 61
CHAPTER 1 MINITAB GUIDE 62
MG1.1 Defining Data 62
MG1.2 Measurement Scales for Variables 62
MG 1.3 Collecting Data 63
MG1.4 Types of Sampling Methods 63
2 Organizing and
Visualizing Variables 64
USING STATISTICS: The Choice Is Yours 64
How to Proceed with This Chapter 65
2.1 Organizing Categorical Variables 66
The Summary Table 66
The Contingency Table 67
2.2 Organizing Numerical Variables 70
The Ordered Array 70
The Frequency Distribution 71
Classes and Excel Bins 73
The Relative Frequency Distribution and the Percentage
Distribution 73
The Cumulative Distribution 75
Stacked and Unstacked Data 77
2.3 Visualizing Categorical Variables 79
The Bar Chart 79
The Pie Chart 80
The Pareto Chart 81
The Side-by-Side Bar Chart 83
2.4 Visualizing Numerical Variables 85
The Stem-and-Leaf Display 85
The Histogram 87
The Percentage Polygon 88
The Cumulative Percentage Polygon (Ogive) 89
2.5 Visualizing Two Numerical Variables 93
The Scatter Plot 93
The Time-Series Plot 94
2.6 Organizing Many Categorical Variables 96
2.7 Challenges in Organizing and Visualizing Variables 98
Obscuring Data 98
Creating False Impressions 99
Chartjunk 100
Guidelines for Constructing Visualizations 102
USING STATISTICS: The Choice Is Yours, Revisited 103
SUMMARY 103
REFERENCES 104
KEY EQUATIONS 104
KEY TERMS 105
CHECKING YOUR UNDERSTANDING 105
CHAPTER REVIEW PROBLEMS 105
CASES FOR CHAPTER 2 110
Managing Ashland MultiComm Services 110
Digital Case 111
CardioGood Fitness 111
The Choice Is Yours Follow-Up 111
Clear Mountain State Student Surveys 112
CHAPTER 2 EXCEL GUIDE 113
EG2.1 Organizing Categorical Variables 113
EG2.2 Organizing Numerical Variables 115
EG2.3 Visualizing Categorical Variables 117
EG2.4 Visualizing Numerical Variables 119
EG2.5 Visualizing Two Numerical Variables 122
EG2.6 Organizing Many Categorical Variables 122
CHAPTER 2 MIN1TAB GUIDE 123
MG2.1 Organizing Categorical Variables 123
MG2.2 Organizing Numerical Variables 124
MG2.3 Visualizing Categorical Variables 124
MG2.4 Visualizing Numerical Variables 126
MG2.5 Visualizing Two Numerical Variables 128
MG2.6 Organizing Many Categorical Variables 128
3 Numerical Descriptive
Measures 129
USING STATISTICS: More Descriptive Choices 129
3.1 Central Tendency 130
The Mean 130
The Median 132
The Mode 133
The Geometric Mean 134
3.2 Variation and Shape 135
The Range 135
The Variance and the Standard Deviation 136
The Coefficient of Variation 140
Z Scores 141
Shape: Skewness and Kurtosis 142
VISUAL EXPLORATIONS: Exploring Descriptive Statistics 145
3.3 Exploring Numerical Data 148
Quartiles 148
The Interquartile Range 150
The Five-Number Summary 151
The Boxplot 152
3.4 Numerical Descriptive Measures for a Population 155
The Population Mean 155
The Population Variance and Standard Deviation 156
The Empirical Rule 157
The Chebyshev Rule 158
3.5 The Covariance and the Coefficient of Correlation 159
The Covariance 160
The Coefficient of Correlation 161
3.6 Descriptive Statistics: Pitfalls and Ethical Issues 165
USING STATISTICS: More Descriptive Choices, Revisited 166
SUMMARY 166
REFERENCES 167
KEY EQUATIONS 167
KEY TERMS 168
CHECKING YOUR UNDERSTANDING 168
CHAPTER REVIEW PROBLEMS 169
CASES FOR CHAPTER 3 172
Managing Ashland MultiComm Services 172
Digital Case 172
CardioGood Fitness 172
More Descriptive Choices Follow-up 172
Clear Mountain State Student Surveys 172
CHAPTER 3 EXCEL GUIDE 173
EG3.1 Central Tendency 173
EG3.2 Variation and Shape 173
EG3.3 Exploring Numerical Data 174
EG3.4 Numerical Descriptive Measures for a Population 175
EG3.5 The Covariance and the Coefficient of Correlation 175
CHAPTER 3 MINITAB GUIDE 176
MG3.1 Central Tendency 176
MG3.2 Variation and Shape 176
MG3.3 Exploring Numerical Data 177
MG3.4 Numerical Descriptive Measures for a Population 177
MG3.5 The Covariance and the Coefficient of Correlation 177
4 Basic Probability 179
USING STATISTICS: Possibilities at M R Electronics
World 179
4.1 Basic Probability Concepts 180
Events and Sample Spaces 181
Contingency Tables and Venn Diagrams 183
Simple Probability 183
Joint Probability 184
Marginal Probability 185
General Addition Rule 186
4.2 Conditional Probability 189
Computing Conditional Probabilities 189
Decision Trees 191
Independence 193
Multiplication Rules 194
Marginal Probability Using the General Multiplication
Rule 195
4.3 Bayes Theorem 197
THINK ABOUT THIS: Divine Providence and Spam 200
4.4 Counting Rules 202
4.5 Ethical Issues and Probability 205
USING STATISTICS: Possibilities at M R Electronics World,
Revisited 206
SUMMARY 206
REFERENCES 206
KEY EQUATIONS 207
KEY TERMS 207
CHECKING YOUR UNDERSTANDING 208
CHAPTER REVIEW PROBLEMS 208
CASES FOR CHAPTER 4 210
Digital Case 210
CardioGood Fitness 210
The Choice Is Yours Follow-Up 210
Clear Mountain State Student Surveys 210
CHAPTER 4 EXCEL GUIDE 211
EG4.1 Basic Probability Concepts 211
EG4.2 Conditional Probability 211
EG4.3 Bayes Theorem 211
EG4.4 Counting Rules 211
CHAPTER 4 MINITAB GUIDE 212
MG4.1 Basic Probability Concepts 212
MG4.2 Conditional Probability 212
MG4.3 Bayes Theorem 212
MG4.4 Counting Rules 212
5 Discrete Probability
Distributions 213
USING STATISTICS: Events of Interest at Ricknel Home
Centers 213
5.1 The Probability Distribution for a Discrete Variable 214
Expected Value of a Discrete Variable 214
Variance and Standard Deviation of a Discrete Variable 215
5.2 Covariance of a Probability Distribution and Its
Application in Finance 217
Covariance 218
Expected Value, Variance, and Standard Deviation of the
Sum of Two Variables 219
Portfolio Expected Return and Portfolio Risk 219
5.3 Binomial Distribution 223
5.4 Poisson Distribution 230
5.5 Hypergeometric Distribution 234
5.6 Using the Poisson Distribution to Approximate
the Binomial Distribution (online) 237
USING STATISTICS: Events of Interest at Ricknel Homecenters,
Revisited 237
SUMMARY 237
REFERENCES 237
KEY EQUATIONS 238
KEY TERMS 238
CHECKING YOUR UNDERSTANDING 239
CHAPTER REVIEW PROBLEMS 239
CASES FOR CHAPTER 5 241
Managing Ashland MultiComm Services 241
Digital Case 242
CHAPTER 5 EXCEL GUIDE 243
EG5.1 The Probability Distribution for a Discrete Variable 243
EG5.2 Covariance of a Probability Distribution and its Application
in Finance 243
EG5.3 Binomial Distribution 243
EG5.4 Poisson Distribution 244
EG5.5 Hypgeometric Distribution 244
CHAPTER 5 MINITAB GUIDE 245
MG5.1 The Probability Distribution for a Discrete Variable 245
MG5.2 Covariance and its Application in Finance 245
MG5.3 Binomial Distribution 245
MG5.4 Poisson Distribution 245
MG5.5 Hypergeometric Distribution 246
6 The Normal Distribution
and Other Continuous
Distributions 247
USING STATISTICS: Normal Downloading at MyTVLab 247
6.1 Continuous Probability Distributions 248
6.2 The Normal Distribution 248
Computing Normal Probabilities 250
Finding X Values 255
VISUAL EXPLORATIONS: Exploring the Normal
Distribution 259
THINK ABOUT THIS: What Is Normal? 259
6.3 Evaluating Normality 261
Comparing Data Characteristics to Theoretical
Properties 261
Constructing the Normal Probability Plot 263
6.4 The Uniform Distribution 265
6.5 The Exponential Distribution 268
6.6 The Normal Approximation to the Binomial Distribution
(online) 270
USING STATISTICS: Normal Downloading at MyTVLab,
Revisited 270
SUMMARY 271
REFERENCES 271
KEY EQUATIONS 271
KEY TERMS 272
CHECKING YOUR UNDERSTANDING 272
CHAPTER REVIEW PROBLEMS 272
CASES FOR CHAPTER 6 273
Managing Ashland MultiComm Services 273
Digital Case 274
CardioGood Fitness 274
More Descriptive Choices Follow-up 274
Clear Mountain State Student Surveys 274
CHAPTER 6 EXCEL GUIDE 275
EG6.1 Continuous Probability Distributions 275
EG6.2 The Normal Distribution 275
EG6.3 Evaluating Normality 275
EG6.4 The Uniform Distribution 276
EG6.5 The Exponential Distribution 276
CHAPTER 6 MINITAB GUIDE 276
MG6.1 Continuous Probability Distributions 276
MG6.2 The Normal Distribution 276
MG6.3 Evaluating Normality 276
MG6.4 The Uniform Distribution 277
MG6.5 The Exponential Distribution 277
7 Sampling Distributions 278
USING STATISTICS: Sampling Oxford Cereals 278
7.1 Sampling Distributions 279
7.2 Sampling Distribution of the Mean 279
The Unbiased Property of the Sample Mean 279
Standard Error of the Mean 281
Sampling from Normally Distributed Populations 282
Sampling from Non-normally Distributed Populations—The
Central Limit Theorem 285
VISUAL EXPLORATIONS: Exploring Sampling Distributions 289
7.3 Sampling Distribution of the Proportion 290
7.4 Sampling from Finite Populations (online) 293
USING STATISTICS: Sampling Oxford Cereals, Revisited 294
SUMMARY 294
REFERENCES 294
KEY EQUATIONS 294
KEY TERMS 295
CHECKING YOUR UNDERSTANDING 295
CHAPTER REVIEW PROBLEMS 295
CASES FOR CHAPTER 7 297
Managing Ashland MultiComm Services 297
Digital Case 297
CHAPTER 7 EXCEL GUIDE 298
EG7.1 Sampling Distributions 298
EG7.2 Sampling Distribution of the Mean 298
EG7.3 Sampling Distribution of the Proportion 298
CHAPTER 7 MINITAB GUIDE 299
MG7.1 Sampling Distributions 299
MG7.2 Sampling Distribution of the Mean 299
MG7.3 Sampling Distribution of the Proportion 299
8 Confidence Interval
Estimation __300
USING STATISTICS: Getting Estimates at Ricknel Home
Centers 300
8.1 Confidence Interval Estimate for the Mean
(cr Known) 301
Can You Ever Know the Population Standard
Deviation? 306
8.2 Confidence Interval Estimate for the Mean
(rr Unknown) 307
Student s t Distribution 307
Properties of the t Distribution 308
The Concept of Degrees of Freedom 309
The Confidence Interval Statement 310
8.3 Confidence Interval Estimate for the Proportion 315
8.4 Determining Sample Size 318
Sample Size Determination for the Mean 318
Sample Size Determination for the Proportion 320
8.5 Confidence Interval Estimation and Ethical Issues 323
8.6 Application of Confidence Interval Estimation in
Auditing (online) 324
8.7 Estimation and Sample Size Estimation for Finite
Populations (online) 324
8.8 Bootstrapping (online) 324
USING STATISTICS: Getting Estimates at Ricknel Home
Centers, Revisited 324
SUMMARY 325
REFERENCES 325
KEY EQUATIONS 325
KEY TERMS 326
CHECKING YOUR UNDERSTANDING 326
CHAPTER REVIEW PROBLEMS 326
CASES FOR CHAPTER 8 329
Managing Ashland MultiComm Services 329
Digital Case 330
Sure Value Convenience Stores 331
CardioGood Fitness 331
More Descriptive Choices Follow-Up 331
Clear Mountain State Student Surveys 331
CHAPTER 8 EXCEL GUIDE 332
EG8.1 Confidence Interval Estimate for the Mean (a Known) 332
EG8.2 Confidence Interval Estimate for the Mean (a Unknown) 332
EG8.3 Confidence Interval Estimate for the Proportion 333
EG8.4 Determining Sample Size 333
CHAPTER 8 MINITAB GUIDE 334
MG8.1 Confidence Interval Estimate for the Mean (cr Known) 334
MG8.2 Confidence Interval Estimate for the Mean (a Unknown) 334
MG8.3 Confidence Interval Estimate for the Proportion 334
MG8.4 Determining Sample Size 335
9 Fundamentals of
Hypothesis Testing:
One-Sample Tests 336
USING STATISTICS: Significant Testing at Oxford Cereals 336
9.1 Fundamentals of Hypothesis-Testing Methodology 337
The Null and Alternative Hypotheses 337
The Critical Value of the Test Statistic 338
Regions of Rejection and Nonrejection 339
Risks in Decision Making Using Hypothesis Testing 339
ZTest for the Mean (a Known) 342
Hypothesis Testing Using the Critical Value Approach 342
Hypothesis Testing Using the p-Value Approach 345
A Connection Between Confidence Interval Estimation and
Hypothesis Testing 347
Can You Ever Know the Population Standard
Deviation? 348
9.2 t Test of Hypothesis for the Mean (a Unknown) 349
The Critical Value Approach 350
The p-Value Approach 352
Checking the Normality Assumption 352
9.3 One-Tail Tests 356
The Critical Value Approach 356
The p-Value Approach 357
9.4 Z Test of Hypothesis for the Proportion 360
The Critical Value Approach 361
The p-Value Approach 362
9.5 Potential Hypothesis-Testing Pitfalls and Ethical
Issues 364
Statistical Significance Versus Practical Significance 364
Statistical Insignificance Versus Importance 365
Reporting of Findings 365
Ethical Issues 365
9.6 Power of a Test (online) 365
USING STATISTICS: Significant Testing at Oxford Cereals,
Revisited 366
SUMMARY 366
REFERENCES 366
KEY EQUATIONS 367
KEY TERMS 367
CHECKING YOUR UNDERSTANDING 367
CHAPTER REVIEW PROBLEMS 367
CASES FOR CHAPTER 9 369
Managing Ashland MultiComm Services 369
Digital Case 370
Sure Value Convenience Stores 370
CHAPTER 9 EXCEL GUIDE 371
EG9.1 Fundamentals of Hypothesis-Testing Methodology 371
EG9.2 t Test of Hypothesis for the Mean (cr Unknown) 371
EG9.3 One-Tail Tests 372
EG9.4 Z Test of Hypothesis for the Proportion 372
CHAPTER 9 MINITAB GUIDE 373
MG9.1 Fundamentals of Hypothesis-Testing Methodology 373
MG9.2 t Test of Hypothesis for the Mean (cr Unknown) 373
MG9.3 One-Tail Tests 373
MG9.4 Z Test of Hypothesis for the Proportion 374
10 Two-Sample Tests 375
USING STATISTICS: For North Fork, Are There Different
Means to the Ends? 375
10.1 Comparing the Means of Two Independent
Populations 376
Pooled-Variance t Test for the Difference Between Two
Means 376
Confidence Interval Estimate for the Difference Between
Two Means 381
t Test for the Difference Between Two Means, Assuming
Unequal Variances 382
Do People Really Do This? 384
10.2 Comparing the Means of Two Related Populations 387
Paired t Test 388
Confidence Interval Estimate for the Mean Difference 393
10.3 Comparing the Proportions of Two Independent
Populations 395
Z Test for the Difference Between Two Proportions 395
Confidence Interval Estimate for the Difference
Between Two Proportions 399
10.4 F Test for the Ratio of Two Variances 401
10.5 Effect Size (online)
USING STATISTICS: For North Fork, Are There Different
Means to the Ends? Revisited 406
SUMMARY 407
REFERENCES 408
KEY EQUATIONS 408
KEY TERMS 409
CHECKING YOUR UNDERSTANDING 409
CHAPTER REVIEW PROBLEMS 409
CASES FOR CHAPTER 10 411
Managing Ashland MultiComm Services 411
Digital Case 412
Sure Value Convenience Stores 412
CardioGood Fitness 412
More Descriptive Choices Follow-Up 413
Clear Mountain State Student Surveys 413
CHAPTER 10 EXCEL GUIDE 414
EG 10.1 Comparing the Means of Two Independent
Populations 414
EG 10.2 Comparing the Means of Two Related Populations 416
EG 10.3 Comparing the Proportions of Two Independent
Populations 417
EG 10.4 F Test for the Ratio of Two Variances 417
CHAPTER 10 MINITAB GUIDE 419
MG10.1 Comparing the Means of Two Independent
Populations 419
MG10.2 Comparing the Means of Two Related Populations 419
MG10.3 Comparing the Proportions of Two Independent
Populations 420
MG10.4 F Test for the Ratio of Two Variances 420
11 Analysis of Variance 422
USING STATISTICS: The Means to Find Differences at
Arlington s 422
11.1 The Completely Randomized Design: One-Way
ANOVA 423
Analyzing Variation in One-Way ANOVA 424
F Test for Differences Among More Than Two Means 426
Multiple Comparisons: The Tukey-Kramer Procedure 430
The Analysis of Means (ANOM) (online) 432
ANOVA Assumptions 433
Levene Test for Homogeneity of Variance 433
11.2 The Randomized Block Design 438
Testing for Factor and Block Effects 438
Multiple Comparisons: The Tukey Procedure 443
11.3 The Factorial Design: Two-Way ANOVA 446
Factor and Interaction Effects 447
Testing for Factor and Interaction Effects 449
Multiple Comparisons: The Tukey Procedure 452
Visualizing Interaction Effects: The Cell Means Plot 454
Interpreting Interaction Effects 454
11.4 Fixed Effects, Random Effects, and Mixed Effects
Models (online) 459
USING STATISTICS: The Means to Find Differences
at Arlington s Revisited 459
SUMMARY 459
REFERENCES 460
KEY EQUATIONS 460
KEY TERMS 461
CHECKING YOUR UNDERSTANDING 462
CHAPTER REVIEW PROBLEMS 462
CASES FOR CHAPTER 11 465
Managing Ashland MultiComm Services 465
Digital Case 465
Sure Value Convenience Stores 466
CardioGood Fitness 466
More Descriptive Choices Follow-Up 466
Clear Mountain State Student Surveys 466
CHAPTER 11 EXCEL GUIDE 468
EG11.1 The Completely Randomized Design: One-Way
ANOVA 468
EG11.2 The Randomized Block Design 470
EG11.3 The Factorial Design: Two-Way ANOVA 471
CHAPTER 11 MINITAB GUIDE 472
MG11.1 The Completely Randomized Design: One-Way
ANOVA 472
MG11.2 The Randomized Block Design 473
MG11.3 The Factorial Design: Two-Way ANOVA 473
12 Chi-Square and
Nonparametric Tests 475
USING STATISTICS: Avoiding Guesswork About Resort
Guests 475
12.1 Chi-Square Test for the Difference Between Two
Proportions 476
12.2 Chi-Square Test for Differences Among More Than Two
Proportions 483
The Marascuilo Procedure 486
The Analysis of Proportions (ANOP) (online) 488
12.3 Chi-Square Test of Independence 489
12.4 Wilcoxon Rank Sum Test: A Nonparametric Method
for Two Independent Populations 495
12.5 Kruskal-Wallis Rank Test: A Nonparametric Method
for the One-Way ANOVA 501
Assumptions 504
12.6 McNemar Test for the Difference Between Two
Proportions (Related Samples) (online) 505
12.7 Chi-Square Test for the Variance or Standard Deviation
(online) 506
12.8 Wilcoxon Signed Ranks Test: A Nonparametric Test for
Two Related Populations (online) 506
12.9 Friedman Rank Test: A Nonparametric Test for the
Randomized Block Design (online) 506
USING STATISTICS: Avoiding Guesswork About Resort
Guests, Revisited 506
SUMMARY 507
REFERENCES 507
KEY EQUATIONS 508
KEY TERMS 508
CHECKING YOUR UNDERSTANDING 508
CHAPTER REVIEW PROBLEMS 508
CASES FOR CHAPTER 12 510
Managing Ashland MultiComm Services 510
Digital Case 511
Sure Value Convenience Stores 511
CardioGood Fitness 512
More Descriptive Choices Follow-Up 512
Clear Mountain State Student Surveys 512
CHAPTER 12 EXCEL GUIDE 514
EG12.1 Chi-Square Test for the Difference Between Two
Proportions 514
EG12.2 Chi-Square Test for Differences Among More Than Two
Proportions 514
EG12.3 Chi-Square Test of Independence 515
EG12.4 Wilcoxon Rank Sum Test: a Nonparametric Method for
Two Independent Populations 515
EG 12.5 Kruskal-Wallis Rank Test: a Nonparametric Method for
the One-Way ANOVA 516
CHAPTER 12 MINITAB GUIDE 517
MG12.1 Chi-Square Test for the Difference Between Two
Proportions 517
MG12.2 Chi-Square Test for Differences Among More Than Two
Proportions 517
MG12.3 Chi-Square Test of Independence 517
MG12.4 Wilcoxon Rank Sum Test: a Nonparametric Method for
Two Independent Populations 517
MC 12.5 Kruskal-Wallis Rank Test: a Nonparametric Method for
the One-Way ANOVA 518
13 Simple Linear Regression 519
USING STATISTICS: Knowing Customers at Sunflowers
Apparel 519
13.1 Types of Regression Models 520
Simple Linear Regression Models 521
13.2 Determining the Simple Linear Regression
Equation 522
The Least-Squares Method 522
Predictions in Regression Analysis: Interpolation Versus
Extrapolation 525
Computing the Y Intercept, è0, and the Slope, b, 525
VISUAL EXPLORATIONS: Exploring Simple Linear Regression
Coefficients 528
13.3 Measures of Variation 530
Computing the Sum of Squares 530
The Coefficient of Determination 531
Standard Error of the Estimate 533
13.4 Assumptions of Regression 535
13.5 Residual Analysis 535
Evaluating the Assumptions 535
13.6 Measuring Autocorrelation: The Durbin-Watson
Statistic 539
Residual Plots to Detect Autocorrelation 539
The Durbin-Watson Statistic 540
13.7 Inferences About the Slope and Correlation
Coefficient 543
t Test for the Slope 544
F Test for the Slope 545
Confidence Interval Estimate for the Slope 547
t Test for the Correlation Coefficient 547
13.8 Estimation of Mean Values and Prediction of Individual
Values 551
The Confidence Interval Estimate for the Mean
Response 551
The Prediction Interval for an Individual Response 552
13.9 Potential Pitfalls in Regression 555
Six Steps for Avoiding the Potential Pitfalls 557
USING STATISTICS: Knowing Customers at Sunflowers
Apparel, Revisited 557
SUMMARY 557
REFERENCES 558
KEY EQUATIONS 559
KEY TERMS 560
CHECKING YOUR UNDERSTANDING 560
CHAPTER REVIEW PROBLEMS 560
CASES FOR CHAPTER 13 564
Managing Ashland MultiComm Services 564
Digital Case 564
Brynne Packaging 564
CHAPTER 13 EXCEL GUIDE 566
EG13.1 Types of Regression Models 566
EG13.2 Determining the Simple Linear Regression Equation 566
EG13.3 Measures of Variation 567
EG13.4 Assumptions of Regression 567
EG13.5 Residual Analysis 567
EG13.6 Measuring Autocorrelation: the Durbin-Watson
Statistic 568
EG13.7 Inferences About the Slope and Correlation Coefficient 568
EG13.8 Estimation of Mean Values and Prediction of Individual
Values 568
CHAPTER 13 MINITAB GUIDE 569
MG13.1 Types of Regression Models 569
MG13.2 Determining the Simple Linear Regression Equation 569
MG13.3 Measures of Variation 569
MG13.4 Assumptions 569
MG13.5 Residual Analysis 569
MG13.6 Measuring Autocorrelation: the Durbin-Watson Statistic 570
MG13.7 Inferences About the Slope and Correlation
Coefficient 570
MG13.8 Estimation of Mean Values and Prediction of Individual
Values 570
14 Introduction to
Multiple Regression 571
USING STATISTICS: The Multiple Effects of OmniPower
Bars 571
14.1 Developing a Multiple Regression Model 572
Interpreting the Regression Coefficients 573
Predicting the Dependent Variable F 575
14.2 r2, Adjusted r2, and the Overall F Test 578
Coefficient of Multiple Determination 578
Adjusted r2 578
Test for the Significance of the Overall Multiple
Regression Model 579
14.3 Residual Analysis for the Multiple Regression
Model 581
14.4 Inferences Concerning the Population Regression
Coefficients 583
Tests of Hypothesis 583
Confidence Interval Estimation 584
14.5 Testing Portions of the Multiple Regression Model 586
Coefficients of Partial Determination 590
14.6 Using Dummy Variables and Interaction Terms
in Regression Models 591
Dummy Variables 592
Interactions 594
14.7 Logistic Regression 601
14.8 Influence Analysis 606
The Hat Matrix Elements, ht 607
The Studentized Deleted Residuals, f; 607
Cook s Distance Statistic, D, 607
Comparison of Statistics 608
USING STATISTICS: The Multiple Effects of Omnipower Bars,
Revisited 609
SUMMARY 609
REFERENCES 611
KEY EQUATIONS 611
KEY TERMS 612
CHECKING YOUR UNDERSTANDING 612
CHAPTER REVIEW PROBLEMS 612
CASES FOR CHAPTER 14 615
Managing Ashland MultiComm Services 615
Digital Case 616
CHAPTER 14 EXCEL GUIDE 617
EG14.1 Developing a Multiple Regression Model 617
EG14.2 r2, Adjusted r2, and the Overall FTest 618
EG14.3 Residual Analysis for the Multiple Regression
Model 618
EG 14,4 Inferences Concerning the Population Regression
Coefficients 619
EG14.5 Testing Portions of the Multiple Regression Model 619
EG 14.6 Using Dummy Variables and Interaction Terms in
Regression Models 619
EG 14.7 Logistic Regression 619
EG 14.8 Influence Analysis 620
CHAPTER 14 MINITAB GUIDE 620
MG14.1 Developing a Multiple Regression Model 620
MG14.2 r2, Adjusted r2, and the Overall FTest 621
MG14.3 Residual Analysis for the Multiple Regression Model 621
MG14.4 Inferences Concerning the Population Regression
Coefficients 621
MG14.5 Testing Portions of the Multiple Regression Model 622
MG14.6 Using Dummy Variables and Interaction Terms in
Regression Models 622
MG14.7 Logistic Regression 622
MG14.8 Influence Analysis 623
15 Multiple Regression
Model Building 624
USING STATISTICS: Valuing Parsimony at WSTA-TV 624
15.1 The Quadratic Regression Model 625
Finding the Regression Coefficients and Predicting Y 625
Testing for the Significance of the Quadratic Model 627
Testing the Quadratic Effect 628
The Coefficient of Multiple Determination 630
15.2 Using Transformations in Regression Models 632
The Square-Root Transformation 633
The Log Transformation 633
15.3 Collinearity 636
15.4 Model Building 637
The Stepwise Regression Approach to Model Building 639
The Best-Subsets Approach to Model Building 640
Model Validation 644
Steps for Successful Model Building 644
15.5 Pitfalls in Multiple Regression and Ethical Issues 646
Pitfalls in Multiple Regression 646
Ethical Issues 646
USING STATISTICS: Valuing Parsimony at WSTA-TV,
Revisited 647
SUMMARY 647
KEY EQUATIONS 647
REFERENCES 649
KEY TERMS 649
CHECKING YOUR UNDERSTANDING 649
CHAPTER REVIEW PROBLEMS 649
CASES FOR CHAPTER 15 651
The Mountain States Potato Company 651
Sure Value Convenience Stores 651
Digital Case 651
The Craybill Instrumentation Company Case 652
More Descriptive Choices Follow-Up 652
CHAPTER 15 EXCEL GUIDE 653
EG15.1 The Quadratic Regression Model 653
EG15.2 Using Transformations in Regression Models 653
EG15.3 Collinearity 653
EG15.4 Model Building 654
CHAPTER 15 MINITAB GUIDE 654
MG15.1 The Quadratic Regression Model 654
MG15.2 Using Transformations in Regression Models 655
MG15.3 Collinearity 655
MG15.4 Model Building 655
16 Time-Series Forecasting 657
USING STATISTICS: Principled Forecasting 657
16.1 The Importance of Business Forecasting 658
16.2 Component Factors of Time-Series Models 658
16.3 Smoothing an Annual Time Series 659
Moving Averages 660
Exponential Smoothing 662
16.4 Least-Squares Trend Fitting and Forecasting 665
The Linear Trend Model 665
The Quadratic Trend Model 667
The Exponential Trend Model 669
Model Selection Using First, Second, and Percentage
Differences 671
16.5 Autoregressive Modeling for Trend Fitting and
Forecasting 675
Selecting an Appropriate Autoregressive Model 676
Determining the Appropriateness of a Selected Model 677
16.6 Choosing an Appropriate Forecasting Model 683
Performing a Residual Analysis 683
Measuring the Magnitude of the Residuals Through Squared
or Absolute Differences 683
Using the Principle of Parsimony 684
A Comparison of Four Forecasting Methods 684
16.7 Time-Series Forecasting of Seasonal Data 686
Least-Squares Forecasting with Monthly or Quarterly
Data 687
16.8 Index Numbers (online) 692
THINK ABOUT THIS: Let the Model User Beware 692
USING STATISTICS: Principled Forecasting, Revisited 692
SUMMARY 693
REFERENCES 693
KEY EQUATIONS 694
KEY TERMS 694
CHECKING YOUR UNDERSTANDING 695
CHAPTER REVIEW PROBLEMS 695
CASES FOR CHAPTER 16 696
Managing Ashland MultiComm Services 696
Digital Case 696
CHAPTER 16 EXCEL GUIDE 697
EG 16.1 The Importance of Business Forecasting 697
EG16.2 Component Factors of Time-Series Models 697
EG 16.3 Smoothing an Annual Time Series 697
EG16.4 Least-Squares Trend Fitting and Forecasting 670
EG16.5 Autoregressive Modeling for Trend Fitting and
Forecasting 698
EG 16.6 Choosing an Appropriate Forecasting Model 699
EG16.7 Time-Series Forecasting of Seasonal Data 699
CHAPTER 16 MINITAB GUIDE 700
MG16.1 The Importance of Business Forecasting 700
MG16.2 Component Factors of Time-Series Models 700
MG16.3 Smoothing an Annual Time Series 700
MG16.4 Least-Squares Trend Fitting and Forecasting 701
MG16.5 Autoregressive Modeling for Trend Fitting and
Forecasting 701
MG16.6 Choosing an Appropriate Forecasting Model 701
MG16.7 Time-Series Forecasting of Seasonal Data 701
17 Business Analytics 702
USING STATISTICS: Finding the Right Lines
atWaldoLands 702
17.1 Descriptive Analytics 703
Dashboards 704
Data Discovery 706
17.2 Predictive Analytics 710
17.3 Classification and Regression Trees 711
Regression Tree Example 713
17.4 Neural Networks 716
Multilayer Perceptrons 716
17.5 Cluster Analysis 719
17.6 Multidimensional Scaling 721
USING STATISTICS: Finding the Right Lines at Waldolands,
Revisited 724
REFERENCES 725
KEY EQUATIONS 725
KEY TERMS 725
CHECKING YOUR UNDERSTANDING 726
CHAPTER REVIEW PROBLEMS 726
CASE FOR CHAPTER 17
The Mountain States Potato Company 727
CHAPTER 17 SOFTWARE GUIDE 728
SGI7.1 Descriptive Analytics 728
SG17.2 Predictive Analytics 732
SG17.3 Classification and Regression Trees 732
SG17.4 Neural Networks 733
SG17.5 Cluster Analysis 734
SG17.6 Multidimensional Scaling 734
18 a Roadmap for
Analyzing Data 735
USING STATISTICS: Mounting Future Analyses 735
18.1 Analyzing Numerical Variables 737
Describing the Characteristics of a Numerical Variable 738
Reaching Conclusions About the Population Mean and/or
Standard Deviation 738
Determining Whether the Mean and/or Standard Deviation
Differs Depending on the Group 738
Determining Which Factors Affect the Value of a
Variable 739
Predicting the Value of a Variable Based on the Values
of Other Variables 739
Determining Whether the Values of a Variable Are Stable
Over Time 739
18.2 Analyzing Categorical Variables 739
Describing the Proportion of Items of Interest in Each
Category 740
Reaching Conclusions About the Proportion of Items
of Interest 740
Determining Whether the Proportion of Items
of Interest Differs Depending on the Group 740
Predicting the Proportion of Items of Interest Based
on the Values of Other Variables 740
Determining Whether the Proportion of Items of Interest
Is Stable Over Time 740
USING STATISTICS: Mounting Future Analyses, Revisited 741
Digital Case 741
CHAPTER REVIEW PROBLEMS 741
19 Statistical Applications
in Quality Management
(online)
USING STATISTICS: Finding Quality at the Beachcomber
19.1 The Theory of Control Charts
19.2 Control Chart for the Proportion: The p Chart
19.3 The Red Bead Experiment: Understanding Process
Variability
19.4 Control Chart for an Area of Opportunity:
The c Chart
19.5 Control Charts for the Range and the Mean
The R Chart
TheV Chart
19.6 Process Capability
Customer Satisfaction and Specification Limits
Capability Indices
CPL, CPU, and Cpk
19.7 Total Quality Management
19.8 Six Sigma
The DMAIC Model
Roles in a Six Sigma Organization
Lean Six Sigma
USING STATISTICS: Finding Quality at the Beachcomber,
Revisited
SUMMARY
REFERENCES
KEY EQUATIONS
KEY TERMS
CHECKING YOUR UNDERSTANDING
CHAPTER REVIEW PROBLEMS
CASES FOR CHAPTER 19
The Harnswell Sewing Machine Company Case
Managing Ashland Multicomm Services
CHAPTER 19 EXCEL GUIDE
EG 19.1 The Theory of Control Charts
EG19.2 Control Chart for the Proportion: The p Chart
EG19.3 The Red Bead Experiment: Understanding Process
Variability
EG 19.4 Control Chart for an Area of Opportunity: The c Chart
EG 19.5 Control Charts for the Range and the Mean
EG19.6 Process Capability
20 Decision Making
(online)
USING STATISTICS: Reliable Decision Making
20.1 Payoff Tables and Decision Trees
20.2 Criteria for Decision Making
Maximax Payoff
Maximin Payoff
Expected Monetary Value
Expected Opportunity Loss
Return-to-Risk Ratio
20.3 Decision Making with Sample Information
20.4 Utility
THINK ABOUT THIS: Risky Business
USING STATISTICS: Reliable Decision-Making, Revisited
SUMMARY
REFERENCES
KEY EQUATIONS
KEY TERMS
CHAPTER REVIEW PROBLEMS
CHAPTER 20 EXCEL GUIDE
EG20.1 Payoff Tables and Decision Trees
EG20.2 Criteria for Decision Making
Appendices 743
A. Basic Math Concepts and Symbols 744
A. 1 Rules for Arithmetic Operations 744
A.2 Rules for Algebra: Exponents and Square Roots 744
A.3 Rules for Logarithms 745
A.4 Summation Notation 746
A.5 Statistical Symbols 749
A.6 Greek Alphabet 749
B. Required Excel Skills 750
B.I Worksheet Entries and References 750
B.2 Absolute and Relative Cell References 751
B.3 Entering Formulas into Worksheets 751
B.4 Pasting with Paste Special 752
B.5 Basic Worksheet Cell Formatting 752
B.6 Chart Formatting 754
B.7 Selecting Cell Ranges for Charts 755
B.8 Deleting the Extra Bar from a Histogram 755
B.9 Creating Histograms for Discrete Probability
Distributions 756
C. Online Resources 757
C.l About the Online Resources for This Book 757
C.2 Accessing the MyStatLab Course Online 757
C.3 Details of Downloadable Files 757
C.4 PHStat 765
D. Configuring Microsoft Excel 766
D.l Getting Microsoft Excel Ready for Use (ALL) 766
D.2 Getting PHStat Ready for Use (ALL) 767
D.3 Configuring Excel Security for Add-In Usage
(WIN) 767
D.4 Opening PHStat (ALL) 768
D.5 Using a Visual Explorations Add-in Workbook
(ALL) 769
D.6 Checking for the Presence of the Analysis Tool Pak
or Solver Add-Ins (ALL) 769
E. Tables 770
E.l Table of Random Numbers 770
E.2 The Cumulative Standardized Normal
Distribution 772
E.3 Critical Values of t IIA
E.4 Critical Values of x2 776
E.5 Critical Values of F 111
E.6 Lower and Upper Critical Values, T,, of the
Wilcoxon Rank Sum Test 781
E.7 Critical Values of the Studentized Range, Q 782
E.8 Critical Values, and dv, of the Durbin-Watson
Statistic, D (Critical Values Are One-Sidcd) 784
E.9 Control Chart Factors 785
E.10 The Standardized Normal Distribution 786
F. Useful Excel Knowledge 787
F.l Useful Keyboard Shortcuts 787
F.2 Verifying Formulas and Worksheets 788
E.3 New Function Names 788
F.4 Understanding the Nonstatistical Functions 790
G. Software FAQs 792
G.l PHStat FAQs 792
G.2 Microsoft Excel FAQs 793
G.3 FAQs for New Users of Microsoft Excel 2013 794
G.4 Minitab FAQs 794
Self-Test Solutions and Answers to Selected
Even-Numbered Problems 795
Index 831
|
| any_adam_object | 1 |
| author | Berenson, Mark L. Levine, David M. 1946- Szabat, Kathryn A. |
| author_GND | (DE-588)1082012130 |
| author_facet | Berenson, Mark L. Levine, David M. 1946- Szabat, Kathryn A. |
| author_role | aut aut aut |
| author_sort | Berenson, Mark L. |
| author_variant | m l b ml mlb d m l dm dml k a s ka kas |
| building | Verbundindex |
| bvnumber | BV041717917 |
| classification_rvk | QH 231 QH 240 |
| ctrlnum | (OCoLC)896869820 (DE-599)BVBBV041717917 |
| discipline | Wirtschaftswissenschaften |
| edition | 13. ed., global ed. |
| format | Book |
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| genre_facet | Einführung Aufgabensammlung |
| id | DE-604.BV041717917 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T01:03:39Z |
| institution | BVB |
| isbn | 1292069023 9781292069029 9780321870025 |
| language | English |
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| publisher | Pearson |
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| spelling | Berenson, Mark L. Verfasser aut Basic business statistics concepts and applications Mark L. Berenson ; David M. Levine ; Kathryn A. Szabat 13. ed., global ed. Boston [u.a.] Pearson 2015 840 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index Commercial statistics Mathematical statistics Wirtschaftsstatistik (DE-588)4066517-3 gnd rswk-swf Entscheidungsprozess (DE-588)4121202-2 gnd rswk-swf Unternehmen (DE-588)4061963-1 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf 1\p (DE-588)4151278-9 Einführung gnd-content 2\p (DE-588)4143389-0 Aufgabensammlung gnd-content Wirtschaftsstatistik (DE-588)4066517-3 s DE-604 Statistik (DE-588)4056995-0 s Unternehmen (DE-588)4061963-1 s Entscheidungsprozess (DE-588)4121202-2 s 3\p DE-604 Levine, David M. 1946- Verfasser (DE-588)1082012130 aut Szabat, Kathryn A. Verfasser aut HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027164965&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 3\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Berenson, Mark L. Levine, David M. 1946- Szabat, Kathryn A. Basic business statistics concepts and applications Commercial statistics Mathematical statistics Wirtschaftsstatistik (DE-588)4066517-3 gnd Entscheidungsprozess (DE-588)4121202-2 gnd Unternehmen (DE-588)4061963-1 gnd Statistik (DE-588)4056995-0 gnd |
| subject_GND | (DE-588)4066517-3 (DE-588)4121202-2 (DE-588)4061963-1 (DE-588)4056995-0 (DE-588)4151278-9 (DE-588)4143389-0 |
| title | Basic business statistics concepts and applications |
| title_auth | Basic business statistics concepts and applications |
| title_exact_search | Basic business statistics concepts and applications |
| title_full | Basic business statistics concepts and applications Mark L. Berenson ; David M. Levine ; Kathryn A. Szabat |
| title_fullStr | Basic business statistics concepts and applications Mark L. Berenson ; David M. Levine ; Kathryn A. Szabat |
| title_full_unstemmed | Basic business statistics concepts and applications Mark L. Berenson ; David M. Levine ; Kathryn A. Szabat |
| title_short | Basic business statistics |
| title_sort | basic business statistics concepts and applications |
| title_sub | concepts and applications |
| topic | Commercial statistics Mathematical statistics Wirtschaftsstatistik (DE-588)4066517-3 gnd Entscheidungsprozess (DE-588)4121202-2 gnd Unternehmen (DE-588)4061963-1 gnd Statistik (DE-588)4056995-0 gnd |
| topic_facet | Commercial statistics Mathematical statistics Wirtschaftsstatistik Entscheidungsprozess Unternehmen Statistik Einführung Aufgabensammlung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027164965&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT berensonmarkl basicbusinessstatisticsconceptsandapplications AT levinedavidm basicbusinessstatisticsconceptsandapplications AT szabatkathryna basicbusinessstatisticsconceptsandapplications |