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ISBN 10: 1118800485
ISBN 13: 978-1118800485
Author: Ken Black
This text is an unbound, binder-ready edition. Access to WileyPLUS sold separately.
Business Statistics: For Contemporary Decision Making, Binder Ready Version, 8th Edition continues the tradition of presenting and explaining the wonders of business statistics through the use of clear, complete, student-friendly pedagogy. Ken Black’s text equips readers with the quantitative decision-making skills and analysis techniques they need to make smart decisions based on real-world data.
Business Statistics For Contemporary Decision Making 8th Table of contents:
Preface xiv
About the Author xxiii
UNIT 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 09
Interval Level 09
Ratio Level 10
Comparison of the Four Levels of Data 10
Statistical Analysis Using the Computer: Excel and Minitab 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
2.1 Frequency Distributions 20
Class Midpoint 21
Relative Frequency 21
Cumulative Frequency 21
2.2 Quantitative Data Graphs 23
Histograms 24
Using Histograms to Get an Initial Overview of the Data 24
Frequency Polygons 26
Ogives 26
Dot Plots 27
Stem-and-Leaf Plots 27
2.3 Qualitative Data Graphs 31
Pie Charts 31
Bar Graphs 32
Pareto Charts 34
2.4 Charts and Graphs for Two Variables 38
Cross Tabulation 38
Scatter Plot 39
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
Kurtosis 84
Box-and-Whisker Plots and Five-Number Summary 84
3.5 Descriptive Statistics on the Computer 86
Summary 88
Key Terms 89
Formulas 89
Supplementary Problems 90
Analyzing the Databases 94
Case: Coca-Cola Develops the African Market 95
Using the Computer 96
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 114
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 135
Key Terms 135
Formulas 135
Supplementary Problems 136
Analyzing the Databases 140
Case: Colgate-Palmolive Makes a “Total” Effort 140
UNIT II 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 161
Working Poisson Problems by Formula 162
What to Do When the Intervals Are Different 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 175
Supplementary Problems 176
Analyzing the Databases 180
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 186
Solving for the Height and Length of a Uniform Distribution 186
The Mean and Standard Deviation of a Uniform Distribution 187
Determining Probabilities in a Uniform Distribution 188
Using the Computer to Solve for Uniform Distribution Probabilities 191
6.2 Normal Distribution 192
Characteristics of the Normal Distribution 192
History of the Normal Distribution 193
Probability Density Function of the Normal Distribution 193
Standardized Normal Distribution 193
Solving for Probabilities Using the Normal Curve 194
Using Probabilities to Solve for the Mean, the Standard Deviation, or an x Value in a Normal Distribution 198
Using the Computer to Solve for Normal Distribution Probabilities 202
6.3 Using the Normal Curve to Approximate Binomial Distribution Problems 204
Correcting for Continuity 206
6.4 Exponential Distribution 210
Probabilities of the Exponential Distribution 211
Using the Computer to Determine Exponential Distribution Probabilities 213
Summary 215
Key Terms 216
Formulas 216
Supplementary Problems 216
Analyzing the Databases 220
Case: Mercedes Goes After Younger Buyers 220
Using the Computer 221
7 Sampling and Sampling
Distributions 224
Decision Dilemma: What Is the Attitude of Maquiladora Workers? 225
7.1 Sampling 225
Reasons for Sampling 226
Reasons for Taking a Census 226
Frame 227
Random Versus Nonrandom Sampling 228
Random Sampling Techniques 228
Simple Random Sampling 228
Stratified Random Sampling 230
Systematic Sampling 231
Cluster (or Area) Sampling 232
Nonrandom Sampling 234
Convenience Sampling 234
Judgment Sampling 234
Quota Sampling 234
Snowball Sampling 235
Sampling Error 235
Nonsampling Errors 235
7.2 Sampling Distribution of x̄ 237
Sampling from a Finite Population 244
7.3 Sampling Distribution of p̂ 246
Summary 250
Key Terms 251
Formulas 251
Supplementary Problems 251
Analyzing the Databases 254
Case: Shell Attempts to Return to Premiere Status 254
Using the Computer 255
UNIT III MAKING INFERENCES ABOUT POPULATION PARAMETERS
8 Statistical Inference: Estimation for Single Populations 260
Decision Dilemma: Batteries and Bulbs: How Long Do They Last? 261
8.1 Estimating the Population Mean Using the z Statistic (σ Known) 263
Finite Correction Factor 266
Estimating the Population Mean Using the z Statistic When the Sample Size Is Small 267
Using the Computer to Construct z Confidence Intervals for the Mean 267
8.2 Estimating the Population Mean Using the t Statistic (σ Unknown) 270
The t Distribution 271
Robustness 271
Characteristics of the t Distribution 271
Reading the t Distribution Table 271
Confidence Intervals to Estimate the Population Mean Using the t Statistic 272
Using the Computer to Construct t Confidence Intervals for the Mean 274
8.3 Estimating the Population Proportion 277
Using the Computer to Construct Confidence Intervals of the Population Proportion 280
8.4 Estimating the Population Variance 281
8.5 Estimating Sample Size 285
Sample Size When Estimating μ 285
Determining Sample Size when Estimating p 287
Summary 290
Key Terms 291
Formulas 291
Supplementary Problems 291
Analyzing the Databases 294
Case: The Container Store 295
Using the Computer 296
9 Statistical Inference: Hypothesis
Testing for Single Populations 298
Decision Dilemma: Valero: Refining and Retailing 299
9.1 Introduction to Hypothesis Testing 300
Types of Hypotheses 301
Research Hypotheses 301
Statistical Hypotheses 302
Substantive Hypotheses 304
Eight-Step Process for Testing Hypotheses 305
Rejection and Nonrejection Regions 306
Type I and Type II Errors 307
Comparing Type I and Type II Errors 308
9.2 Testing Hypotheses About a Population Mean Using the z Statistic (σ Known) 310
An Example Using the Eight-Step Approach 310
Using the p-Value to Test Hypotheses 312
Testing the Mean with a Finite Population 313
Using the Critical Value Method to Test Hypotheses 314
Using the Computer to Test Hypotheses About a Population Mean Using the z Statistic 317
9.3 Testing Hypotheses About a Population Mean Using the t Statistic (σ Unknown) 319
Using the Computer to Test Hypotheses About a Population Mean Using the t Test 323
9.4 Testing Hypotheses About a Proportion 326
Using the Computer to Test Hypotheses About a Population Proportion 331
9.5 Testing Hypotheses About a Variance 333
9.6 Solving for Type II Errors 336
Some Observations About Type II Errors 341
Operating Characteristic and Power Curves 341
Effect of Increasing Sample Size on the Rejection Limits 343
Summary 347
Key Terms 347
Formulas 347
Supplementary Problems 348
Analyzing the Databases 351
Case: Frito-Lay Targets the Hispanic Market 351
Using the Computer 352
10 Statistical Inferences About Two Populations 354
Decision Dilemma: L. L. Bean 355
10.1 Hypothesis Testing and Confidence Intervals About the Difference in Two Means Using the z Statistic (Population Variances Known) 358
Hypothesis Testing 359
Confidence Intervals 363
Using the Computer to Test Hypotheses About the Difference in Two Population Means Using the z Test 365
10.2 Hypothesis Testing and Confidence Intervals About the Difference in Two Means: Independent Samples and Population Variances Unknown 368
Hypothesis Testing 368
Using the Computer to Test Hypotheses and Construct Confidence Intervals About the Difference in Two Population Means Using the t Test 370
Confidence Intervals 373
10.3 Statistical Inferences for Two Related Populations 378
Hypothesis Testing 378
Using the Computer to Make Statistical Inferences about Two Related Populations 380
Confidence Intervals 383
10.4 Statistical Inferences About Two Population Proportions, p1 _ p2 388
Hypothesis Testing 388
Confidence Intervals 392
Using the Computer to Analyze the Difference in Two Proportions 393
10.5 Testing Hypotheses About Two Population Variances 395
Using the Computer to Test Hypotheses About Two Population Variances 399
Summary 404
Key Terms 404
Formulas 404
Supplementary Problems 405
Analyzing the Databases 410
Case: Seitz Corporation: Producing Quality Gear-Driven and Linear-Motion Products 410
Using the Computer 411
11 Analysis of Variance and Design of Experiments 414
Decision Dilemma: Job and Career Satisfaction of Foreign Self-Initiated Expatriates 415
11.1 Introduction to Design of Experiments 416
11.2 The Completely Randomized Design (One-Way ANOVA) 418
One-Way Analysis of Variance 419
Reading the F Distribution Table 422
Using the Computer for One-Way ANOVA 423
Comparison of F and t Values 425
11.3 Multiple Comparison Tests 430
Tukey’s Honestly Significant Difference (HSD) Test: The Case of Equal Sample Sizes 430
Using the Computer to Do Multiple Comparisons 432
Tukey-Kramer Procedure: The Case of Unequal Sample Sizes 434
11.4 The Randomized Block Design 438
Using the Computer to Analyze Randomized Block Designs 442
11.5 A Factorial Design (Two-Way ANOVA) 448
Advantages of the Factorial Design 448
Factorial Designs with Two Treatments 449
Applications 449
Statistically Testing the Factorial Design 450
Interaction 451
Using a Computer to Do a Two-Way ANOVA 456
Summary 465
Key Terms 465
Formulas 466
Supplementary Problems 467
Analyzing the Databases 470
Case: The Clarkson Company: A Division of Tyco International 471
Using the Computer 472
UNIT IV REGRESSION ANALYSIS AND FORECASTING
12 Simple Regression Analysis and Correlation 476
Decision Dilemma: Predicting International Hourly Wages by the Price of a Big Mac 477
12.1 Correlation 478
12.2 Introduction to Simple Regression Analysis 481
12.3 Determining the Equation of the Regression Line 482
12.4 Residual Analysis 489
Using Residuals to Test the Assumptions of the Regression Model 491
Using the Computer for Residual Analysis 492
12.5 Standard Error of the Estimate 496
12.6 Coefficient of Determination 499
Relationship Between r and r2 501
12.7 Hypothesis Tests for the Slope of the Regression Model and Testing the Overall Model 501
Testing the Slope 501
Testing the Overall Model 505
12.8 Estimation 506
Confidence Intervals to Estimate the Conditional Mean of y:μy|x 506
Prediction Intervals to Estimate a Single Value of y 507
12.9 Using Regression to Develop a Forecasting Trend Line 510
Determining the Equation of the Trend Line 511
Forecasting Using the Equation of the Trend Line 512
Alternate Coding for Time Periods 513
12.10 Interpreting the Output 516
Summary 520
Key Terms 521
Formulas 521
Supplementary Problems 521
Analyzing the Databases 525
Case: Delta Wire Uses Training as a Weapon 525
Using the Computer 527
13 Multiple Regression Analysis 528
Decision Dilemma: Are You Going to Hate Your New Job? 529
13.1 The Multiple Regression Model 530
Multiple Regression Model with Two Independent Variables (First-Order) 531
Determining the Multiple Regression Equation 532
A Multiple Regression Model 532
13.2 Significance Tests of the Regression Model and Its Coefficients 537
Testing the Overall Model 537
Significance Tests of the Regression Coefficients 539
13.3 Residuals, Standard Error of the Estimate, and R2 542
Residuals 542
SSE and Standard Error of the Estimate 543
Coefficient of Multiple Determination (R2) 544
Adjusted R2 545
13.4 Interpreting Multiple Regression Computer Output 547
A Reexamination of the Multiple Regression Output 547
Summary 551
Key Terms 552
Formulas 552
Supplementary Problems 552
Analyzing the Databases 555
Case: Starbucks Introduces Debit Card 555
Using the Computer 556
14 Building Multiple Regression Models 558
Decision Dilemma: Determining Compensation for CEOs 559
14.1 Nonlinear Models: Mathematical Transformation 560
Polynomial Regression 560
Tukey’s Ladder of Transformations 563
Regression Models with Interaction 564
Model Transformation 566
14.2 Indicator (Dummy) Variables 572
14.3 Model-Building: Search Procedures 578
Search Procedures 580
All Possible Regressions 580
Stepwise Regression 580
Forward Selection 584
Backward Elimination 584
14.4 Multicollinearity 588
14.5 Logistic Regression 590
An Example 590
The Logistic Regression Model 592
Interpreting the Output 593
Determining Logistic Regression Model 594
Testing the Overall Model 594
Testing Individual Predictor Variables 595
Summary 599
Key Terms 600
Formulas 600
Supplementary Problems 601
Analyzing the Databases 604
Case: Virginia Semiconductor 604
Using the Computer 606
15 Time-Series Forecasting and Index Numbers 608
Decision Dilemma: Forecasting Air Pollution 609
15.1 Introduction to Forecasting 610
Time-Series Components 610
The Measurement of Forecasting Error 611
Error 611
Mean Absolute Deviation (MAD) 611
Mean Square Error (MSE) 612
15.2 Smoothing Techniques 614
Naïve Forecasting Models 614
Averaging Models 615
Simple Averages 615
Moving Averages 615
Weighted Moving Averages 617
Exponential Smoothing 619
15.3 Trend Analysis 624
Linear Regression Trend Analysis 624
Regression Trend Analysis Using Quadratic Models 626
Holt’s Two-Parameter Exponential Smoothing Method 629
15.4 Seasonal Effects 631
Decomposition 631
Finding Seasonal Effects with the Computer 634
Winters’ Three-Parameter Exponential Smoothing Method 634
15.5 Autocorrelation and Autoregression 636
Autocorrelation 636
Ways to Overcome the Autocorrelation Problem 639
Addition of Independent Variables 639
Transforming Variables 640
Autoregression 640
15.6 Index Numbers 643
Simple Index Numbers 644
Unweighted Aggregate Price Index Numbers 644
Weighted Aggregate Price Index Numbers 645
Laspeyres Price Index 646
Paasche Price Index 647
Summary 652
Key Terms 653
Formulas 653
Supplementary Problems 653
Analyzing the Databases 658
Case: Debourgh Manufacturing Company 659
Using the Computer 660
UNIT V NONPARAMETRIC STATISTICS AND QUALITY
16 Analysis of Categorical Data 664
Decision Dilemma: Selecting Suppliers in the Electronics Industry 665
16.1 Chi-Square Goodness-of-Fit Test 666
16.2 Contingency Analysis: Chi-Square Test of Independence 674
Summary 683
Key Terms 683
Formulas 683
Supplementary Problems 683
Analyzing the Database 685
Case: Foot Locker in the Shoe Mix 685
Using the Computer 686
17 Nonparametric Statistics 688
Decision Dilemma: How Is the Doughnut Business? 689
17.1 Runs Test 691
Small-Sample Runs Test 692
Large-Sample Runs Test 693
17.2 Mann-Whitney U Test 696
Small-Sample Case 696
Large-Sample Case 698
17.3 Wilcoxon Matched-Pairs Signed Rank Test 704
Small-Sample Case (n ≤ 15) 704
Large-Sample Case (n > 15) 705
17.4 Kruskal-Wallis Test 712
17.5 Friedman Test 717
17.6 Spearman’s Rank Correlation 723
Summary 728
Key Terms 729
Formulas 729
Supplementary Problems 729
Analyzing the Databases 734
Case: Schwinn 735
Using the Computer 736
18 Statistical Quality Control 738
Decision Dilemma: Italy’s Piaggio Makes a Comeback 739
18.1 Introduction to Quality Control 740
What Is Quality Control? 740
Total Quality Management 741
Deming’s 14 Points 742
Quality Gurus 743
Six Sigma 743
Design for Six Sigma 745
Lean Manufacturing 745
Some Important Quality Concepts 745
Benchmarking 746
Just-in-Time Inventory Systems 746
Reengineering 747
Failure Mode and Effects Analysis 748
Poka-Yoke 749
Quality Circles and Six Sigma Teams 749
18.2 Process Analysis 751
Flowcharts 751
Pareto Analysis 752
Cause-and-Effect (Fishbone) Diagrams 753
Control Charts 754
Check Sheets or Checklists 755
Histogram 756
Scatter Chart or Scatter Diagram 756
18.3 Control Charts 757
Variation 758
Types of Control Charts 758
x̅ Chart 758
R Charts 762
p Charts 763
c Charts 766
Interpreting Control Charts 768
Summary 774
Key Terms 775
Formulas 776
Supplementary Problems 776
Analyzing the Databases 779
Case: Robotron-elotherm 780
Using the Computer 781
Appendices
A Tables 783
B Answers to Selected Odd-Numbered
Quantitative Problems 823
Glossary 833
Index 843
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