# Business Statistics, 4th Canadian Edition PDF by Norean Sharpe, Richard De Veaux, Paul Velleman and David Wright

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