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

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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