Business Statistics, Fourth Canadian Edition
By Norean Sharpe, Richard De Veaux, Paul Velleman and David Wright
Contents:
Preface xvii
Acknowledgements xxiii
Part I EXPLORING AND COLLECTING DATA
Chapter 1 An Introduction to Statistics 1
1.1 So What Is Statistics? 2 • 1.2 How Is Statistics Used in Management? 5
1.3 How Can I Learn Statistics? 6
Mini Case Studies 7
Chapter 2 Data 8
2.1 What Are Data? 9 • 2.2 Variable Types 12 • 2.3 Where, How, and When 18
Ethics in Action 19
Mini Case Studies 21
Technology Help: Computer-Based Statistics Packages 22
Chapter 3 Surveys and Sampling 27
3.1 Three Principles of Sampling 28 • 3.2 A Census—Does It Make Sense? 31
3.3 Populations and Parameters 32 • 3.4 Simple Random Sampling (SRS) 33
3.5 Other Random Sample Designs 34 • 3.6 Practicalities 39
3.7 The Valid Survey 40 • 3.8 How to Sample Badly 42
Ethics in Action 45
Mini Case Studies 47
Technology Help: Random Sampling 48
Chapter 4 Displaying and Describing Categorical Data 56
4.1 The Three Rules of Data Analysis 57 • 4.2 Frequency Tables 57 • 4.3 Charts 59
4.4 Exploring Two Categorical Variables: Contingency Tables 62 • 4.5 Simpson’s Paradox 69
Ethics in Action 72
Mini Case Studies 73
Technology Help: Displaying Categorical Data on the Computer 74
Chapter 5 Displaying and Describing Quantitative Data 88
5.1 Displaying Data Distributions 89 • 5.2 Shape 93 • 5.3 Centre 95
5.4 Spread 98 • 5.5 Reporting the Shape, Centre, and Spread 102
5.6 Adding Measures of Centre and Spread 103 • 5.7 Grouped Data 103
5.8 Five-Number Summary and Boxplots 105 • 5.9 Percentiles 108
5.10 Comparing Groups 109 • 5.11 Dealing With Outliers 111
5.12 Standardizing 113 • 5.13 Time Series Plots 115
5.14 Transforming Skewed Data 118
Ethics in Action 122
Mini Case Studies 125
Technology Help: Displaying and Summarizing Quantitative Variables 127
Chapter 6 Scatterplots, Association, and Correlation 143
6.1 Looking at Scatterplots 144 • 6.2 Assigning Roles to Variables in Scatterplots 146
6.3 Understanding Correlation 147 • 6.4 Straightening Scatterplots 153
6.5 Lurking Variables and Causation 155
Ethics in Action 159
Mini Case Studies 161
Technology Help: Scatterplots and Correlation 162
Chapter 7 Introduction to Linear Regression 172
7.1 The Linear Model 173 • 7.2 Correlation and the Line 175 • 7.3 Regression to
the Mean 179 • 7.4 Checking the Model 180 • 7.5 Learning More From the Residuals 181
7.6 Variation in the Model and R 2 183 • 7.7 Reality Check: Is the Regression Reasonable? 184
7.8 Nonlinear Relationships 187
Ethics in Action 189
Mini Case Studies 191
Technology Help: Regression 193
Part 2 UNDERSTANDING PROBABILITY DISTRIBUTIONS
AND STATISTICAL INFERENCE
Chapter 8 Randomness and Probability 205
8.1 Random Phenomena and Empirical Probability 206 • 8.2 The Nonexistent Law of
Averages 208 • 8.3 Two More Types of Probability 209 • 8.4 Probability Rules 211
8.5 Joint Probability and Contingency Tables 216 • 8.6 Conditional Probability and
Independence 218 • 8.7 Constructing Contingency Tables 220 • 8.8 Probability
Trees 221 • 8.9 Reversing the Conditioning: Bayes’s Rule 224
Ethics in Action 228
Mini Case Studies 231
Chapter 9 Random Variables and Probability Distributions 245
9.1 Expected Value of a Random Variable 246 • 9.2 Standard Deviation and Variance of
a Random Variable 248 • 9.3 Adding and Subtracting Random Variables 251 • 9.4 Introduction
to Discrete Probability Distributions 258 • 9.5 The Geometric Distribution 259 • 9.6 The Binomial
Distribution 261 • 9.7 The Poisson Distribution 267 • 9.8 Continuous Random Variables 270
9.9 The Uniform Distribution 271 • 9.10 The Normal Distribution 272 • 9.11 The Normal
Approximation to the Binomial 285 • 9.12 The Exponential Distribution 288
Ethics in Action 291
Mini Case Studies 294
Technology Help: Probability Distributions 296
Chapter 10 Sampling Distributions 309
10.1 Modelling Sample Proportions 310 • 10.2 The Sampling Distribution for Proportions
312 • 10.3 The Central Limit Theorem—The Fundamental Theorem of Statistics 317
10.4 The Sampling Distribution of the Mean 319 • 10.5 Standard Error 321
Ethics in Action 323
Mini Case Studies 325
Chapter 11 Confidence Intervals for Proportions 336
11.1 A Confidence Interval 338 • 11.2 Margin of Error: Certainty vs. Precision 341
11.3 Critical Values 342 • 11.4 Assumptions and Conditions 344 • 11.5 Choosing
the Sample Size 346 • 11.6 Confidence Interval for the Difference Between Two Proportions 349
Ethics in Action 352
Mini Case Studies 354
Technology Help: Confidence Intervals for Proportions 355
Chapter 12 Testing Hypotheses About Proportions 363
12.1 Hypotheses 364 • 12.2 A Trial as a Hypothesis Test 367 • 12.3 P-Values 369
12.4 Alpha Levels and Significance 372 • 12.5 The Reasoning of Hypothesis Testing 374
12.6 Critical Values 380 • 12.7 Confidence Intervals and Hypothesis Tests 381
12.8 Comparing Two Proportions 385 • 12.9 Two Types of Error 388 • 12.10 Power 390
Ethics in Action 396
Mini Case Studies 398
Technology Help: Testing Hypotheses About Proportions 399
Chapter 13 Confidence Intervals and Hypothesis Tests for Means 411
13.1 The Sampling Distribution for the Mean 412 • 13.2 A Confidence Interval for Means
414 • 13.3 Assumptions and Conditions 415 • 13.4 Cautions About Interpreting Confidence
Intervals 419 • 13.5 Hypothesis Test for Means 420 • 13.6 Sample Size 424
Ethics in Action 427
Mini Case Studies 429
Technology Help: Inference for Means 431
Chapter 14 Comparing Two Means 443
14.1 Comparing Two Means 444 • 14.2 The Two-Sample t-Test 446 • 14.3 Assumptions
and Conditions 447 • 14.4 A Confidence Interval for the Difference Between Two Means 452
14.5 The Pooled t-Test 454 • 14.6 Paired Data 460 • 14.7 The Paired t-Test 461
Ethics in Action 466
Mini Case Studies 468
Technology Help: Comparing Two Means 469
Chapter 15 Design of Experiments and Analysis of Variance (ANOVA) 487
15.1 Observational Studies 488 • 15.2 Randomized, Comparative Experiments 490
15.3 The Four Principles of Experimental Design 491 • 15.4 Experimental Designs 493
15.5 Blinding and Placebos 497 • 15.6 Confounding and Lurking Variables 498
15.7 Analyzing a Completely Randomized Design: The One-Way Analysis of Variance 499
15.8 Assumptions and Conditions for ANOVA 503 • 15.9 ANOVA on Observational Data 507
15.10 Analyzing a Randomized Block Design 508 • 15.11 Analyzing a Factorial Design—
Two-Way Analysis of Variance 511
Ethics in Action 519
Mini Case Studies 523
Technology Help: ANOVA 523
Chapter 16 Inference for Counts: Chi-Square Tests 537
16.1 Goodness-of-Fit Tests 539 • 16.2 Interpreting Chi-Square Values 543 • 16.3 Examining
the Residuals 544 • 16.4 The Chi-Square Test of Homogeneity (Independence) 545
Ethics in Action 551
Mini Case Studies 553
Technology Help: Chi-Square 555
Chapter 17 Nonparametric Methods 566
17.1 Data Types for Nonparametric Tests 567 • 17.2 The Wilcoxon Signed-Rank Test 569
17.3 Friedman Test for a Randomized Block Design 575 • 17.4 The Wilcoxon Rank-Sum Test
(or, the Mann-Whitney Test) 577 • 17.5 Tukey’s Quick Test 581 • 17.6 Kruskal-Wallis Test 583
17.7 Kendall’s Tau 586 • 17.8 Spearman’s Rank Correlation 588 • 17.9 When Should You
Use Nonparametric Methods? 591
Ethics in Action 592
Mini Case Studies 594
Part 3 EXPLORING RELATIONSHIPS AMONG VARIABLES
Chapter 18 Inference for Regression 602
18.1 The Population and the Sample 604 • 18.2 Assumptions and Conditions 605
18.3 The Standard Error of the Slope 610 • 18.4 A Test for the Regression Slope 612
18.5 A Hypothesis Test for Correlation 617 • 18.6 Predicted Values 618
Ethics in Action 623
Mini Case Studies 626
Technology Help: Regression Analysis 628
Chapter 19 Understanding Regression Residuals 643
19.1 Examining Residuals for Groups 644 • 19.2 Extrapolation and Prediction 647
19.3 Unusual and Extraordinary Observations 649 • 19.4 Working with Summary Values 653
19.5 Autocorrelation 655 • 19.6 Linearity 658 • 19.7 Transforming (Re-expressing) Data
659 • 19.8 The Ladder of Powers 664
Ethics in Action 670
Mini Case Studies 672
Technology Help: Regression Residuals 673
Chapter 20 Multiple Regression 688
20.1 The Linear Multiple Regression Model 691 • 20.2 Interpreting Multiple Regression
Coefficients 693 • 20.3 Assumptions and Conditions for the Multiple Regression Model 695
20.4 Testing the Multiple Regression Model 703 • 20.5 The F-Statistic and ANOVA 705
20.6 R 2 and Adjusted R 2 707
Ethics in Action 710
Mini Case Studies 712
Technology Help: Regression Analysis 714
Chapter 21 Building Multiple Regression Models 726
21.1 Indicator (or Dummy) Variables 728 • 21.2 Adjusting for Different Slopes—Interaction
Terms 733 • 21.3 Multiple Regression Diagnostics 735 • 21.4 Building Regression Models 742
21.5 Collinearity 750
Ethics in Action 754
Mini Case Studies 757
Technology Help: Multiple Regression Analysis 758
Part 4 USING STATISTICS FOR DECISION MAKING
Chapter 22 Time Series Analysis 772
22.1 Time Series and Index Numbers 774 • 22.2 Components of a Time Series 776
22.3 Smoothing Methods 780 • 22.4 Summarizing Forecast Error 786 • 22.5 Autoregressive
Models 788 • 22.6 Multiple Regression–Based Models 795 • 22.7 Additive and Multiplicative
Models 799 • 22.8 Cyclical and Irregular Components 801 • 22.9 Forecasting with Regression-
Based Models 802 • 22.10 Choosing a Time Series Forecasting Method 805 • 22.11 Interpreting
Time Series Models: The Whole Foods Data Revisited 806
Ethics in Action 807
Mini Case Studies 810
Technology Help: Time Series Analysis 812
Chapter 23 Decision Making and Risk 824
23.1 Actions, States of Nature, and Outcomes 825 • 23.2 Payoff Tables and Decision Trees
826 • 23.3 Minimizing Loss and Maximizing Gain 827 • 23.4 The Expected Value of an Action
828 • 23.5 Expected Value with Perfect Information 829 • 23.6 Decisions Made with Sample
Information 830 • 23.7 Estimating Variation 832 • 23.8 Sensitivity 834
23.9 Simulation 835 • 23.10 More Complex Decisions 837
Ethics in Action 838
Mini Case Studies 840
Chapter 24 Quality Control 848
24.1 A Short History of Quality Control 849 • 24.2 Control Charts for Individual Observations
(Run Charts) 853 • 24.3 Control Charts for Sample Measurements: x, R, and S Charts 857
24.4 Actions for Out-of-Control Processes 864 • 24.5 Control Charts for Attributes: p Charts
and c Charts 869 • 24.6 Quality Control in Industry 873
Ethics in Action 874
Mini Case Studies 876
Technology Help: Quality Control Charts 877
Chapter 25 (Online) Introduction to Data Mining 886
25.1 Big Data W3 • 25.2 The Goals of Data Mining W4 • 25.3 Data Mining Myths W5
25.4 Successful Data Mining W6 • 25.5 Data Mining Problems W7
25.6 Data Mining Algorithms W8 • 25.7 The Data Mining Process W12
25.8 Summary W13
Ethics in Action W14
Appendixes
- Answer Key A-1
- Statistical Tables, Formulas, and Excel/XLStat B-1
- Ethical Guidelines for Statistical Practice C-1
Index I-1