BOOK
Business Statistics, Global Edition
David F. Groebner | Patrick W. Shannon | Phillip C. Fry
(2017)
Additional Information
Book Details
Abstract
For 2-semester courses in Introductory Business Statistics.
Gain an edge in today’s workplace by applying statistical analysis skills to real-world decision-making.
Business Statistics: A Decision Making Approach provides students with an introduction to business statistics and to the analysis skills and techniques needed to make successful real-world business decisions. Written for students of all mathematical skill levels, the authors present concepts in a systematic and ordered way, drawing from their own experience as educators and consultants. Rooted in the theme that data are the starting point, Business Statistics champions the need to use and understand different types of data and data sources to be effective decision makers. This new edition integrates Microsoft Excel throughout as a way to work with statistical concepts and give students a resource that can be used in both their academic and professional careers.
Pearson MyLabTM Statistics not included. Students, if MyLab is a recommended/mandatory component of the course, please ask your instructor for the correct ISBN and course ID. MyLab should only be purchased when required by an instructor. Instructors, contact your Pearson rep for more information.
MyLab is an online homework, tutorial, and assessment product designed to personalize learning and improve results. With a wide range of interactive, engaging, and assignable activities, students are encouraged to actively learn and retain tough course concepts.
Table of Contents
| Section Title | Page | Action | Price |
|---|---|---|---|
| Cover | Cover | ||
| Title Page | 3 | ||
| Copyright Page | 4 | ||
| About the Authors | 7 | ||
| Brief Contents | 9 | ||
| Contents | 11 | ||
| Preface | 19 | ||
| Acknowledgments | 24 | ||
| Chapter 1: The Where, Why, and How of Data Collection | 25 | ||
| 1.1. What Is Business Statistics? | 26 | ||
| Descriptive Statistics | 27 | ||
| Inferential Procedures | 28 | ||
| 1.2. Procedures for Collecting Data | 29 | ||
| Primary Data Collection Methods | 29 | ||
| Other Data Collection Methods | 34 | ||
| Data Collection Issues | 35 | ||
| 1.3. Populations, Samples, and Sampling Techniques | 37 | ||
| Populations and Samples | 37 | ||
| Sampling Techniques | 38 | ||
| 1.4. Data Types and Data Measurement Levels | 43 | ||
| Quantitative and Qualitative Data | 43 | ||
| Time-Series Data and Cross-Sectional Data | 44 | ||
| Data Measurement Levels | 44 | ||
| 1.5. A Brief Introduction to Data Mining | 47 | ||
| Data Mining—Finding the Important, Hidden Relationships in Data | 47 | ||
| Summary | 49 | ||
| Key Terms | 50 | ||
| Chapter Exercises | 51 | ||
| Chapter 2: Graphs, Charts, and Tables—Describing Your Data | 52 | ||
| 2.1. Frequency Distributions and Histograms | 53 | ||
| Frequency Distributions | 53 | ||
| Grouped Data Frequency Distributions | 57 | ||
| Histograms | 62 | ||
| Relative Frequency Histograms and Ogives | 65 | ||
| Joint Frequency Distributions | 67 | ||
| 2.2. Bar Charts, Pie Charts, and Stem and Leaf Diagrams | 74 | ||
| Bar Charts | 74 | ||
| Pie Charts | 77 | ||
| Stem and Leaf Diagrams | 78 | ||
| 2.3. Line Charts, Scatter Diagrams, and Pareto Charts | 83 | ||
| Line Charts | 83 | ||
| Scatter Diagrams | 86 | ||
| Pareto Charts | 88 | ||
| Summary | 92 | ||
| Equations | 93 | ||
| Key Terms | 93 | ||
| Chapter Exercises | 93 | ||
| Case 2.1: Server Downtime | 95 | ||
| Case 2.2: Hudson Valley Apples, Inc. | 96 | ||
| Case 2.3: Pine River Lumber Company—Part 1 | 96 | ||
| Chapter 3: Describing Data Using Numerical Measures | 97 | ||
| 3.1. Measures of Center and Location | 98 | ||
| Parameters and Statistics | 98 | ||
| Population Mean | 98 | ||
| Sample Mean | 101 | ||
| The Impact of Extreme Values on the Mean | 102 | ||
| Median | 103 | ||
| Skewed and Symmetric Distributions | 104 | ||
| Mode | 105 | ||
| Applying the Measures of Central Tendency | 107 | ||
| Other Measures of Location | 108 | ||
| Box and Whisker Plots | 111 | ||
| Developing a Box and Whisker Plot in Excel 2016 | 113 | ||
| Data-Level Issues | 113 | ||
| 3.2. Measures of Variation | 119 | ||
| Range | 119 | ||
| Interquartile Range | 120 | ||
| Population Variance and Standard Deviation | 121 | ||
| Sample Variance and Standard Deviation | 124 | ||
| 3.3. Using the Mean and Standard Deviation Together | 130 | ||
| Coefficient of Variation | 130 | ||
| Tchebysheff’s Theorem | 133 | ||
| Standardized Data Values | 133 | ||
| Summary | 138 | ||
| Equations | 139 | ||
| Key Terms | 140 | ||
| Chapter Exercises | 140 | ||
| Case 3.1: SDW—Human Resources | 144 | ||
| Case 3.2: National Call Center | 144 | ||
| Case 3.3: Pine River Lumber Company—Part 2 | 145 | ||
| Case 3.4: AJ’s Fitness Center | 145 | ||
| Chapters 1–3: Special Review Section | 146 | ||
| Chapters 1–3 | 146 | ||
| Exercises | 149 | ||
| Review Case 1: State Department of Insurance | 150 | ||
| Term Project Assignments | 151 | ||
| Chapter 4: Introduction to Probability | 152 | ||
| 4.1. The Basics of Probability | 153 | ||
| Important Probability Terms | 153 | ||
| Methods of Assigning Probability | 158 | ||
| 4.2. The Rules of Probability | 165 | ||
| Measuring Probabilities | 165 | ||
| Conditional Probability | 173 | ||
| Multiplication Rule | 177 | ||
| Bayes’ Theorem | 180 | ||
| Summary | 189 | ||
| Equations | 189 | ||
| Key Terms | 190 | ||
| Chapter Exercises | 190 | ||
| Case 4.1: Great Air Commuter Service | 193 | ||
| Case 4.2: Pittsburg Lighting | 194 | ||
| Chapter 5: Discrete Probability Distributions | 196 | ||
| 5.1. Introduction to Discrete Probability Distributions | 197 | ||
| Random Variables | 197 | ||
| Mean and Standard Deviation of Discrete Distributions | 199 | ||
| 5.2. The Binomial Probability Distribution | 204 | ||
| The Binomial Distribution | 205 | ||
| Characteristics of the Binomial Distribution | 205 | ||
| 5.3. Other Probability Distributions | 217 | ||
| The Poisson Distribution | 217 | ||
| The Hypergeometric Distribution | 221 | ||
| Summary | 229 | ||
| Equations | 229 | ||
| Key Terms | 230 | ||
| Chapter Exercises | 230 | ||
| Case 5.1: SaveMor Pharmacies | 233 | ||
| Case 5.2: Arrowmark Vending | 234 | ||
| Case 5.3: Boise Cascade Corporation | 235 | ||
| Chapter 6: Introduction to Continuous Probability Distributions | 236 | ||
| 6.1. The Normal Distribution | 237 | ||
| The Normal Distribution | 237 | ||
| The Standard Normal Distribution | 238 | ||
| Using the Standard Normal Table | 240 | ||
| 6.2. Other Continuous Probability Distributions | 250 | ||
| The Uniform Distribution | 250 | ||
| The Exponential Distribution | 252 | ||
| Summary | 257 | ||
| Equations | 258 | ||
| Key Terms | 258 | ||
| Chapter Exercises | 258 | ||
| Case 6.1: State Entitlement Programs | 261 | ||
| Case 6.2: Credit Data, Inc. | 262 | ||
| Case 6.3: National Oil Company—Part 1 | 262 | ||
| Chapter 7: Introduction to Sampling Distributions | 263 | ||
| 7.1. Sampling Error: What It Is and Why It Happens | 264 | ||
| Calculating Sampling Error | 264 | ||
| 7.2. Sampling Distribution of the Mean | 272 | ||
| Simulating the Sampling Distribution for x | 273 | ||
| The Central Limit Theorem | 279 | ||
| 7.3. Sampling Distribution of a Proportion | 286 | ||
| Working with Proportions | 286 | ||
| Sampling Distribution of p | 288 | ||
| Summary | 295 | ||
| Equations | 296 | ||
| Key Terms | 296 | ||
| Chapter Exercises | 296 | ||
| Case 7.1: Carpita Bottling Company—Part 1 | 299 | ||
| Case 7.2: Truck Safety Inspection | 300 | ||
| Chapter 8: Estimating Single Population Parameters | 301 | ||
| 8.1. Point and Confidence Interval Estimates for a Population Mean | 302 | ||
| Point Estimates and Confidence Intervals | 302 | ||
| Confidence Interval Estimate for the Population Mean, S Known | 303 | ||
| Confidence Interval Estimates for the Population Mean, S Unknown | 310 | ||
| Student’s t-Distribution | 310 | ||
| 8.2. Determining the Required Sample Size for Estimating a Population Mean | 319 | ||
| Determining the Required Sample Size for Estimating M, S Known | 320 | ||
| Determining the Required Sample Size for Estimating M, S Unknown | 321 | ||
| 8.3. Estimating a Population Proportion | 325 | ||
| Confidence Interval Estimate for a Population Proportion | 326 | ||
| Determining the Required Sample Size for Estimating a Population Proportion | 328 | ||
| Summary | 334 | ||
| Equations | 335 | ||
| Key Terms | 335 | ||
| Chapter Exercises | 335 | ||
| Case 8.1: Management Solutions, Inc. | 338 | ||
| Case 8.2: Federal Aviation Administration | 339 | ||
| Case 8.3: Cell Phone Use | 339 | ||
| Chapter 9: Introduction to Hypothesis Testing | 340 | ||
| 9.1. Hypothesis Tests for Means | 341 | ||
| Formulating the Hypotheses | 341 | ||
| Significance Level and Critical Value | 345 | ||
| Hypothesis Test for M, S Known | 346 | ||
| Types of Hypothesis Tests | 352 | ||
| p-Value for Two-Tailed Tests | 353 | ||
| Hypothesis Test for M, S Unknown | 355 | ||
| 9.2. Hypothesis Tests for a Proportion | 362 | ||
| Testing a Hypothesis about a Single Population Proportion | 362 | ||
| 9.3. Type II Errors | 368 | ||
| Calculating Beta | 368 | ||
| Controlling Alpha and Beta | 370 | ||
| Power of the Test | 374 | ||
| Summary | 379 | ||
| Equations | 381 | ||
| Key Terms | 381 | ||
| Chapter Exercises | 381 | ||
| Case 9.1: Carpita Bottling Company—Part 2 | 385 | ||
| Case 9.2: Wings of Fire | 385 | ||
| Chapter 10: Estimation and Hypothesis Testing for Two Population Parameters | 387 | ||
| 10.1. Estimation for Two Population Means Using Independent Samples | 388 | ||
| Estimating the Difference between Two Population Means When S1 and S2 Are Known, Using Independent Samples | 388 | ||
| Estimating the Difference between Two Population Means When S1 and S2 Are Unknown, Using Independent Samples | 390 | ||
| 10.2. Hypothesis Tests for Two Population Means Using Independent Samples | 398 | ||
| Testing for M1 M2 When S1 and S2 Are Known, Using Independent Samples | 398 | ||
| Testing for M1 M2 When S1 and S2 Are Unknown, Using Independent Samples | 401 | ||
| 10.3. Interval Estimation and Hypothesis Tests for Paired Samples | 410 | ||
| Why Use Paired Samples? | 411 | ||
| Hypothesis Testing for Paired Samples | 414 | ||
| 10.4. Estimation and Hypothesis Tests for Two Population Proportions | 419 | ||
| Estimating the Difference between Two Population Proportions | 419 | ||
| Hypothesis Tests for the Difference between Two Population Proportions | 420 | ||
| Summary | 426 | ||
| Equations | 427 | ||
| Key Terms | 428 | ||
| Chapter Exercises | 428 | ||
| Case 10.1: Larabee Engineering—Part 1 | 431 | ||
| Case 10.2: Hamilton Marketing Services | 431 | ||
| Case 10.3: Green Valley Assembly Company | 432 | ||
| Case 10.4: U-Need-It Rental Agency | 432 | ||
| Chapter 11: Hypothesis Tests and Estimation for Population Variances | 434 | ||
| 11.1. Hypothesis Tests and Estimation for a Single Population Variance | 435 | ||
| Chi-Square Test for One Population Variance | 435 | ||
| Interval Estimation for a Population Variance | 440 | ||
| 11.2. Hypothesis Tests for Two Population Variances | 444 | ||
| F-Test for Two Population Variances | 444 | ||
| Summary | 454 | ||
| Equations | 454 | ||
| Key Term | 454 | ||
| Chapter Exercises | 454 | ||
| Case 11.1: Larabee Engineering—Part 2 | 456 | ||
| Chapter 12: Analysis of Variance | 458 | ||
| 12.1. One-Way Analysis of Variance | 459 | ||
| Introduction to One-Way ANOVA | 459 | ||
| Partitioning the Sum of Squares | 460 | ||
| The ANOVA Assumptions | 461 | ||
| Applying One-Way ANOVA | 463 | ||
| The Tukey-Kramer Procedure for Multiple Comparisons | 470 | ||
| Fixed Effects Versus Random Effects in Analysis of Variance | 473 | ||
| 12.2. Randomized Complete Block Analysis of Variance | 477 | ||
| Randomized Complete Block ANOVA | 478 | ||
| Fisher’s Least Significant Difference Test | 484 | ||
| 12.3. Two-Factor Analysis of Variance with Replication | 488 | ||
| Two-Factor ANOVA with Replications | 488 | ||
| A Caution about Interaction | 494 | ||
| Summary | 498 | ||
| Equations | 499 | ||
| Key Terms | 499 | ||
| Chapter Exercises | 499 | ||
| Case 12.1: Agency for New Americans | 502 | ||
| Case 12.2: McLaughlin Salmon Works | 503 | ||
| Case 12.3: NW Pulp and Paper | 503 | ||
| Case 12.4: Quinn Restoration | 503 | ||
| Business Statistics Capstone Project | 504 | ||
| Chapters 8–12: Special Review Section | 505 | ||
| Chapters 8–12 | 505 | ||
| Using the Flow Diagrams | 517 | ||
| Exercises | 518 | ||
| Chapter 13: Goodness-of-Fit Tests and Contingency Analysis | 521 | ||
| 13.1. Introduction to Goodness-of-Fit Tests | 522 | ||
| Chi-Square Goodness-of-Fit Test | 522 | ||
| 13.2. Introduction to Contingency Analysis | 534 | ||
| 2 x 2 Contingency Tables | 535 | ||
| r x c Contingency Tables | 539 | ||
| Chi-Square Test Limitations | 541 | ||
| Summary | 545 | ||
| Equations | 545 | ||
| Key Term | 545 | ||
| Chapter Exercises | 546 | ||
| Case 13.1: National Oil Company—Part 2 | 548 | ||
| Case 13.2: Bentford Electronics—Part 1 | 548 | ||
| Chapter 14: Introduction to Linear Regression and Correlation Analysis | 550 | ||
| 14.1. Scatter Plots and Correlation | 551 | ||
| The Correlation Coefficient | 551 | ||
| 14.2. Simple Linear Regression Analysis | 560 | ||
| The Regression Model Assumptions | 560 | ||
| Meaning of the Regression Coefficients | 561 | ||
| Least Squares Regression Properties | 566 | ||
| Significance Tests in Regression Analysis | 568 | ||
| 14.3. Uses for Regression Analysis | 578 | ||
| Regression Analysis for Description | 578 | ||
| Regression Analysis for Prediction | 580 | ||
| Common Problems Using Regression Analysis | 582 | ||
| Summary | 589 | ||
| Equations | 590 | ||
| Key Terms | 591 | ||
| Chapter Exercises | 591 | ||
| Case 14.1: A & A Industrial Products | 594 | ||
| Case 14.2: Sapphire Coffee—Part 1 | 595 | ||
| Case 14.3: Alamar Industries | 595 | ||
| Case 14.4: Continental Trucking | 596 | ||
| Chapter 15: Multiple Regression Analysis and Model Building | 597 | ||
| 15.1. Introduction to Multiple Regression Analysis | 598 | ||
| Basic Model-Building Concepts | 600 | ||
| 15.2. Using Qualitative Independent Variables | 614 | ||
| 15.3. Working with Nonlinear Relationships | 621 | ||
| Analyzing Interaction Effects | 625 | ||
| Partial F-Test | 629 | ||
| 15.4. Stepwise Regression | 635 | ||
| Forward Selection | 635 | ||
| Backward Elimination | 635 | ||
| Standard Stepwise Regression | 637 | ||
| Best Subsets Regression | 638 | ||
| 15.5. Determining the Aptness of the Model | 642 | ||
| Analysis of Residuals | 643 | ||
| Corrective Actions | 648 | ||
| Summary | 652 | ||
| Equations | 653 | ||
| Key Terms | 654 | ||
| Chapter Exercises | 654 | ||
| Case 15.1: Dynamic Weighing, Inc. | 656 | ||
| Case 15.2: Glaser Machine Works | 658 | ||
| Case 15.3: Hawlins Manufacturing | 658 | ||
| Case 15.4: Sapphire Coffee—Part 2 | 659 | ||
| Case 15.5: Wendell Motors | 659 | ||
| Chapter 16: Analyzing and Forecasting Time-Series Data | 660 | ||
| 16.1. Introduction to Forecasting and Time-Series Data | 661 | ||
| General Forecasting Issues | 661 | ||
| Components of a Time Series | 662 | ||
| Introduction to Index Numbers | 665 | ||
| Using Index Numbers to Deflate a Time Series | 666 | ||
| 16.2. Trend-Based Forecasting Techniques | 668 | ||
| Developing a Trend-Based Forecasting Model | 668 | ||
| Comparing the Forecast Values to the Actual Data | 670 | ||
| Nonlinear Trend Forecasting | 677 | ||
| Adjusting for Seasonality | 681 | ||
| 16.3. Forecasting Using Smoothing Methods | 691 | ||
| Exponential Smoothing | 691 | ||
| Forecasting with Excel 2016 | 698 | ||
| Summary | 705 | ||
| Equations | 706 | ||
| Key Terms | 706 | ||
| Chapter Exercises | 706 | ||
| Case 16.1: Park Falls Chamber of Commerce | 709 | ||
| Case 16.2: The St. Louis Companies | 710 | ||
| Case 16.3: Wagner Machine Works | 710 | ||
| Chapter 17: Introduction to Nonparametric Statistics | 711 | ||
| 17.1. The Wilcoxon Signed Rank Test for One Population Median | 712 | ||
| The Wilcoxon Signed Rank Test—Single Population | 712 | ||
| 17.2. Nonparametric Tests for Two Population Medians | 717 | ||
| The Mann–Whitney U-Test | 717 | ||
| Mann–Whitney U-Test—Large Samples | 720 | ||
| 17.3. Kruskal–Wallis One-Way Analysis of Variance | 729 | ||
| Limitations and Other Considerations | 733 | ||
| Summary | 736 | ||
| Equations | 737 | ||
| Chapter Exercises | 738 | ||
| Case 17.1: Bentford Electronics—Part 2 | 741 | ||
| Chapter 18: Introducing Business Analytics | 742 | ||
| 18.1. What Is Business Analytics? | 743 | ||
| Descriptive Analytics | 744 | ||
| Predictive Analytics | 747 | ||
| 18.2. Data Visualization Using Microsoft Power BI Desktop | 749 | ||
| Using Microsoft Power BI Desktop | 753 | ||
| Summary | 765 | ||
| Key Terms | 765 | ||
| Case 18.1: New York City Taxi Trips | 765 | ||
| Appendices | 767 | ||
| A: Random Numbers Table | 768 | ||
| B: Cumulative Binomial Distribution Table | 769 | ||
| C: Cumulative Poisson Probability Distribution Table | 783 | ||
| D: Standard Normal Distribution Table | 788 | ||
| E: Exponential Distribution Table | 789 | ||
| F: Values of t for Selected Probabilities | 790 | ||
| G: Values of x2 for Selected Probabilities | 791 | ||
| H: F-Distribution Table: Upper 5% Probability (or 5% Area) under F-Distribution Curve | 792 | ||
| I: Distribution of the Studentized Range (q-values) | 798 | ||
| J: Critical Values of r in the Runs Test | 800 | ||
| K: Mann–Whitney U Test Probabilities (n * 9) | 801 | ||
| L: Mann–Whitney U Test Critical Values (9 \" n \" 20) | 803 | ||
| M: Critical Values of T in the Wilcoxon Matched-Pairs Signed-Ranks Test (n \" 25) | 805 | ||
| N: Critical Values dL and du of the Durbin-Watson Statistic D (Critical Values Are One-Sided) | 806 | ||
| O: Lower and Upper Critical Values W of Wilcoxon Signed-Ranks Test 808 | 808 | ||
| P: Control Chart Factors | 809 | ||
| Answers to Selected Odd-Numbered Problems | 811 | ||
| References | 839 | ||
| Glossary | 843 | ||
| Index | 849 | ||
| Credits | 859 | ||
| Back Cover | Back Cover |