Introduction to Business Statistics 7th Edition by Ronald M. Weiers, ISBN-13: 978-0538452175

Description

Introduction to Business Statistics 7th Edition by Ronald M. Weiers, ISBN-13: 978-0538452175

[PDF eBook eTextbook]

• Publisher: ‎ Cengage Learning; 7th edition (June 7, 2010)
• Language: ‎ English
• 880 pages
• ISBN-10: ‎ 053845217X
• ISBN-13: ‎ 978-0538452175

If you’ve ever felt intimidated or a little overwhelmed by business statistics, or if you simply want to master the power of these critical business skills, this book is for you.

Weiers’ INTRODUCTION TO BUSINESS STATISTICS, 7E speaks to you – today’s student – introducing the fundamentals of business statistics in a conversational language and application setting that you can easily understand. Proven learning aids woven throughout the text, outstanding illustrations, and hundreds of examples build upon familiar, real-life experiences to help you develop a solid understanding of key statistical concepts. You’ll discover how to use the statistical software most often chosen for business today. Also, you’ll learn how to complete hand calculations and Excel applications – and when it’s best to use each. To further your understanding of today’s statistics, a powerful online learning system helps you maximize your study time and efficiently complete homework with tutorials and interactive learning tools designed to focus specifically on the areas you individually need to master for business statistics success.

Table of Contents:

PART 1: BUSINESS STATISTICS: INTRODUCTION AND BACKGROUND

Chapter 1: A Preview of Business Statistics 1

1.1 Introduction 2

1.2 Statistics: Yesterday and Today 3

1.3 Descriptive Versus Inferential Statistics 5

1.4 Types of Variables and Scales of Measurement 8

1.5 Statistics in Business Decisions 11

1.6 Business Statistics: Tools Versus Tricks 11

1.7 Summary 12

Chapter 2: Visual Description of Data 15

2.1 Introduction 16

2.2 The Frequency Distribution and the Histogram 16

2.3 The Stem-and-Leaf Display and the Dotplot 24

2.4 Other Methods for Visual Representation of the Data 28

2.5 The Scatter Diagram 37

2.6 Tabulation, Contingency Tables, and the Excel PivotTable 42

2.7 Summary 48

Integrated Case: Thorndike Sports Equipment (Meet the Thorndikes:

See Video Unit One.) 53

Integrated Case: Springdale Shopping Survey 54

Chapter 3: Statistical Description of Data 57

3.1 Introduction 58

3.2 Statistical Description: Measures of Central Tendency 59

3.3 Statistical Description: Measures of Dispersion 67

3.4 Additional Dispersion Topics 77

3.5 Descriptive Statistics from Grouped Data 83

3.6 Statistical Measures of Association 86

3.7 Summary 90

Integrated Case: Thorndike Sports Equipment 96

Integrated Case: Springdale Shopping Survey 97

Business Case: Baldwin Computer Sales (A) 97

Seeing Statistics Applet 1: Influence of a Single Observation on the Median 99

Seeing Statistics Applet 2: Scatter Diagrams and Correlation 100

Chapter 4: Data Collection and Sampling Methods 101

4.1 Introduction 102

4.2 Research Basics 102

4.3 Survey Research 105

4.4 Experimentation and Observational Research 109

4.5 Secondary Data 112

4.6 The Basics of Sampling 117

4.7 Sampling Methods 119

4.8 Summary 127

Integrated Case: Thorndike Sports Equipment—Video Unit Two 131

Seeing Statistics Applet 3: Sampling 132

PART 2: PROBABILITY

Chapter 5: Probability: Review of Basic Concepts 133

5.1 Introduction 134

5.2 Probability: Terms and Approaches 135

5.3 Unions and Intersections of Events 140

5.4 Addition Rules for Probability 143

5.5 Multiplication Rules for Probability 146

5.6 Bayes’ Theorem and the Revision of Probabilities 150

5.7 Counting: Permutations and Combinations 156

5.8 Summary 160

Integrated Case: Thorndike Sports Equipment 165

Integrated Case: Springdale Shopping Survey 166

Business Case: Baldwin Computer Sales (B) 166

Chapter 6: Discrete Probability Distributions 167

6.1 Introduction 168

6.2 The Binomial Distribution 175

6.3 The Hypergeometric Distribution 183

6.4 The Poisson Distribution 187

6.5 Simulating Observations from a Discrete Probability Distribution 194

6.6 Summary 199

Integrated Case: Thorndike Sports Equipment 203

Chapter 7: Continuous Probability Distributions 205

7.1 Introduction 206

7.2 The Normal Distribution 208

7.3 The Standard Normal Distribution 212

7.4 The Normal Approximation to the Binomial Distribution 223

7.5 The Exponential Distribution 228

7.6 Simulating Observations from a Continuous Probability Distribution 233

7.7 Summary 235

Integrated Case: Thorndike Sports Equipment

(Corresponds to Thorndike Video Unit Three) 240

Integrated Case: Thorndike Golf Products Division 240

Seeing Statistics Applet 4: Size and Shape of Normal Distribution 241

Seeing Statistics Applet 5: Normal Distribution Areas 242

Seeing Statistics Applet 6: Normal Approximation to Binomial Distribution 243

PART 3: SAMPLING DISTRIBUTIONS AND ESTIMATION

Chapter 8: Sampling Distributions 244

8.1 Introduction 245

8.2 A Preview of Sampling Distributions 245

8.3 The Sampling Distribution of the Mean 248

8.4 The Sampling Distribution of the Proportion 254

8.5 Sampling Distributions When the Population Is Finite 257

8.6 Computer Simulation of Sampling Distributions 259

8.7 Summary 262

Integrated Case: Thorndike Sports Equipment 266

Seeing Statistics Applet 7: Distribution of Means: Fair Dice 268

Seeing Statistics Applet 8: Distribution of Means: Loaded Dice 269

Chapter 9: Estimation from Sample Data 270

9.1 Introduction 271

9.2 Point Estimates 272

9.3 A Preview of Interval Estimates 273

9.4 Confidence Interval Estimates for the Mean:  Known 276

9.5 Confidence Interval Estimates for the Mean:  Unknown 281

9.6 Confidence Interval Estimates for the Population Proportion 288

9.7 Sample Size Determination 293

9.8 When the Population Is Finite 298

9.9 Summary 302

Integrated Case: Thorndike Sports Equipment (Thorndike Video Unit Four) 307

Integrated Case: Springdale Shopping Survey 308

Seeing Statistics Applet 9: Confidence Interval Size 309

Seeing Statistics Applet 10: Comparing the Normal and Student t Distributions 310

Seeing Statistics Applet 11: Student t Distribution Areas 310

PART 4: HYPOTHESIS TESTING

Chapter 10: Hypothesis Tests Involving a Sample Mean

or Proportion 311

10.1 Introduction 312

10.2 Hypothesis Testing: Basic Procedures 317

10.3 Testing a Mean, Population Standard Deviation Known 320

10.4 Confidence Intervals and Hypothesis Testing 329

10.5 Testing a Mean, Population Standard Deviation Unknown 330

10.6 Testing a Proportion 338

10.7 The Power of a Hypothesis Test 346

10.8 Summary 354

Integrated Case: Thorndike Sports Equipment 359

Integrated Case: Springdale Shopping Survey 360

Business Case: Pronto Pizza (A) 361

Seeing Statistics Applet 12: z-Interval and Hypothesis Testing 362

Seeing Statistics Applet 13: Statistical Power of a Test 363

Chapter 11: Hypothesis Tests Involving Two Sample

Means or Proportions 364

11.1 Introduction 365

11.2 The Pooled-Variances t-Test for Comparing the

Means of Two Independent Samples 366

11.3 The Unequal-Variances t-Test for Comparing the

Means of Two Independent Samples 374

11.4 The z-Test for Comparing the Means of Two

Independent Samples 380

11.5 Comparing Two Means When the Samples Are Dependent 385

11.6 Comparing Two Sample Proportions 391

11.7 Comparing the Variances of Two Independent Samples 397

11.8 Summary 401

Integrated Case: Thorndike Sports Equipment 407

Integrated Case: Springdale Shopping Survey 407

Business Case: Circuit Systems, Inc. (A) 408

Seeing Statistics Applet 14: Distribution of Difference Between Sample Means 410

Chapter 12: Analysis of Variance Tests 411

12.1 Introduction 412

12.2 Analysis of Variance: Basic Concepts 412

12.3 One-Way Analysis of Variance 416

12.4 The Randomized Block Design 429

12.5 Two-Way Analysis of Variance 441

12.6 Summary 457

Integrated Case: Thorndike Sports Equipment (Video Unit Six) 462

Integrated Case: Springdale Shopping Survey 462

Business Case: Fastest Courier in the West 463

Seeing Statistics Applet 15: F Distribution and ANOVA 464

Seeing Statistics Applet 16: Interaction Graph in Two-Way ANOVA 465

Chapter 13: Chi-Square Applications 467

13.1 Introduction 468

13.2 Basic Concepts in Chi-Square Testing 468

13.3 Tests for Goodness of Fit and Normality 471

13.4 Testing the Independence of Two Variables 479

13.5 Comparing Proportions from k Independent Samples 486

13.6 Estimation and Tests Regarding the Population Variance 489

13.7 Summary 497

Integrated Case: Thorndike Sports Equipment 502

Integrated Case: Springdale Shopping Survey 503

Business Case: Baldwin Computer Sales (C) 503

Seeing Statistics Applet 17: Chi-Square Distribution 504

Chapter 14: Nonparametric Methods 505

14.1 Introduction 506

14.2 Wilcoxon Signed Rank Test for One Sample 508

14.3 Wilcoxon Signed Rank Test for Comparing Paired Samples 513

14.4 Wilcoxon Rank Sum Test for Comparing Two

Independent Samples 517

14.5 Kruskal-Wallis Test for Comparing More Than

Two Independent Samples 521

14.6 Friedman Test for the Randomized Block Design 525

14.7 Other Nonparametric Methods 530

14.8 Summary 545

Integrated Case: Thorndike Sports Equipment 549

Business Case: Circuit Systems, Inc. (B) 550

PART 5: REGRESSION, MODEL BUILDING, AND TIME SERIES

Chapter 15: Simple Linear Regression and Correlation 551

15.1 Introduction 552

15.2 The Simple Linear Regression Model 553

15.3 Interval Estimation Using the Sample Regression Line 561

15.4 Correlation Analysis 567

15.5 Estimation and Tests Regarding the Sample Regression Line 572

15.6 Additional Topics in Regression and Correlation Analysis 578

15.7 Summary 587

Integrated Case: Thorndike Sports Equipment 595

Integrated Case: Springdale Shopping Survey 596

Business Case: Pronto Pizza (B) 596

Seeing Statistics Applet 18: Regression: Point Estimate for y 597

Seeing Statistics Applet 19: Point Insertion Diagram and Correlation 598

Seeing Statistics Applet 20: Regression Error Components 599

Chapter 16: Multiple Regression and Correlation 600

16.1 Introduction 601

16.2 The Multiple Regression Model 602

16.3 Interval Estimation in Multiple Regression 609

16.4 Multiple Correlation Analysis 615

16.5 Significance Tests in Multiple Regression and Correlation 617

16.6 Overview of the Computer Analysis and Interpretation 622

16.7 Additional Topics in Multiple Regression and Correlation 632

16.8 Summary 634

Integrated Case: Thorndike Sports Equipment 639

Integrated Case: Springdale Shopping Survey 640

Business Case: Easton Realty Company (A) 641

Business Case: Circuit Systems, Inc. (C) 643

Chapter 17: Model Building 644

17.1 Introduction 645

17.2 Polynomial Models with One Quantitative

Predictor Variable 645

17.3 Polynomial Models with Two Quantitative

Predictor Variables 653

17.4 Qualitative Variables 658

17.5 Data Transformations 663

17.6 Multicollinearity 667

17.7 Stepwise Regression 670

17.8 Selecting a Model 676

17.9 Summary 677

Integrated Case: Thorndike Sports Equipment 681

Integrated Case: Fast-Growing Companies 681

Business Case: Westmore MBA Program 682

Business Case: Easton Realty Company (B) 685

Chapter 18: Models for Time Series and Forecasting 687

18.1 Introduction 688

18.2 Time Series 688

18.3 Smoothing Techniques 693

18.4 Seasonal Indexes 702

18.5 Forecasting 708

18.6 Evaluating Alternative Models: MAD and MSE 713

18.7 Autocorrelation, The Durbin-Watson Test, and

Autoregressive Forecasting 715

18.8 Index Numbers 724

18.9 Summary 729

Integrated Case: Thorndike Sports Equipment (Video Unit Five) 735

PART 6: SPECIAL TOPICS

Chapter 19: Decision Theory 737

19.1 Introduction 738

19.2 Structuring the Decision Situation 738

19.3 Non-Bayesian Decision Making 742

19.4 Bayesian Decision Making 745

19.5 The Opportunity Loss Approach 749

19.6 Incremental Analysis and Inventory Decisions 751

19.7 Summary 754

Integrated Case: Thorndike Sports Equipment (Video Unit Seven) 757

Online Appendix to Chapter 19: The Expected Value of Imperfect Information

Chapter 20: Total Quality Management 758

20.1 Introduction 759

20.2 A Historical Perspective and Defect Detection 762

20.3 The Emergence of Total Quality Management 763

20.4 Practicing Total Quality Management 765

20.5 Some Statistical Tools for Total Quality Management 770

20.6 Statistical Process Control: The Concepts 774

20.7 Control Charts for Variables 776

20.8 Control Charts for Attributes 786

20.9 Additional Statistical Process Control and

Quality Management Topics 795

20.10 Summary 799

Integrated Case: Thorndike Sports Equipment 803

Integrated Case: Willard Bolt Company 804

Seeing Statistics Applet 21: Mean Control Chart 805

Appendix A: Statistical Tables A-1

Appendix B: Selected Answers B-1

Index/Glossary I-1

Dr. Ron Weiers is an award-winning teacher and textbook author in the fields of business statistics and marketing research. He holds a passion for “making complicated things understandable,” which is evident in the clear, conversational writing style found in his INTRODUCTION TO BUSINESS STATISTICS. Dr. Weiers is a recipient of the Indiana University of Pennsylvania Distinguished Faculty Award for Teaching. He is an adjunct professor at the H. John Heinz III School of Public Policy and Management, Carnegie Mellon University, and is Professor Emeritus at the Eberly College of Business and Information Technology, Indiana University of Pennsylvania. Dr. Weiers has served as a marketing, technical and automotive consultant to organizations such as the Coleman Company, the U.S. Department of Energy, and the Society of Automotive Engineers. He has authored 8 automotive books on topics ranging from repair and maintenance to fuel efficiency and safety. Dr. Weiers has provided research and advisory services to the U.S. Department of Energy, National Highway Traffic Administration, and National Public Services Research Institute. He has developed Public Affairs Programs on Urban Transportation, Fuel Efficiency, Vehicle Safety, and Exhaust Emissions for the U.S. Headquarters of the Society of Automotive Engineers, and has authored an SAE Public Affairs Report on Automotive Noise Pollution. Dr. Weiers earned his B.S. in Industrial Engineering at the University of Pittsburgh and his S.M. in Industrial Management from the Sloan School of Management at the Massachusetts Institute of Technology. He later received his Ph.D. in Marketing Research and Analysis from the University of Pittsburgh. Dr. Weiers is a member of several professional organizations, including the American Marketing Association, the American Statistical Association, the Decision Sciences Institute, and the Society of Automotive Engineers.

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