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

By

Business Statistics, Fourth Canadian Edition

By Norean Sharpe, Richard De Veaux, Paul Velleman and David Wright

Business Statistics, Fourth Canadian Edition

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

  1. Answer Key A-1
  2. Statistical Tables, Formulas, and Excel/XLStat B-1
  3. Ethical Guidelines for Statistical Practice C-1

Index I-1

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