Business Statistics · A Decision-Making Approach (Eight Edition) by Groebner, David F. -- Read -- Imperial Library of Trantor

Index

Preface Chapter 1 The Where, Why, and How of Data Collection

What is Business Statistics? Descriptive Statistics Charts and Graphs Inferential Procedures Estimation Hypothesis Testing Procedures for Collecting Data Data Collection Written Questionnaires and Surveys Direct Observation and Personal Interviews Other Data Collection Methods Data Collection Issues Interviewer Bias Nonresponse Bias Selection Bias Observer Bias Measurement Error Internal Validity External Validity Populations, Samples, and Sampling Techniques Populations and Samples Parameters and Statistics Sampling Techniques Statistical Sampling Data Types and Data Measurement Levels Quantitative and Qualitative Data Time-Series Data and Cross-Sectional Data Data Measurement Levels Nominal Data Ordinal Data Interval Data Ratio Data Visual Summary Key Terms Chapter Exercises Video Case 1: Statistical Data Collection @ McDonald’s References

Chapter 2 Graphs, Charts, and Tables—Describing Your Data

Frequency Distributions and Histograms Frequency Distribution Grouped Data Frequency Distributions Steps for Grouping Data into Classes Histograms Issues with Excel Relative Frequency Histograms and Ogives Joint Frequency Distributions Bar Charts, Pie Charts, and Stem and Leaf Diagrams Bar Charts Pie Charts Stem and Leaf Diagrams Line Charts and Scatter Diagrams Line Charts Scatter Diagrams Personal Computers Visual Summary Equations Key Terms Chapter Exercises Video Case 2: Drive-Thru Service Times @ McDonald’s Case 2.1: Server Downtime Case 2.2: Yakima Apples, Inc. Case 2.3: Welco Lumber Company—Part A References

Chapter 3 Describing Data Using Numerical Measures

Measures of Center and Location Parameters and Statistics Population Mean Sample Mean The Impact of Extreme Values on the Mean Median Skewed and Symmetric Distributions Mode Applying the Measures of Central Tendency Issues with Excel Other Measures of Location Weighted Mean Percentiles Quartiles Issues with Excel Box and Whisker Plots Data-Level Issues Measures of Variation Range Interquartile Range Population Variance and Standard Deviation Sample Variance and Standard Deviation Using the Mean and Standard Deviation Together Coefficient of Variation The Empirical Rule Tchebysheff’s Theorem Standardized Data Values Visual Summary Equations Key Terms Chapter Exercises Video Case 3: Drive-Thru Service Times at McDonald’s Case 3.1: WGI — Human Resources Case 3.2: National Call Center Case 3.3: Welco Lumber Company—Part B Case 3.4: AJ’s Fitness Center References

Chapters 1-3 Special Review Section

Chapters 1-3 Exercises Review Case 1: State Department of Insurance Term Project Assignments

Chapter 4 Introduction to Probability

The Basics of Probability Important Probability Terms Events and Sample Space Using Tree Diagrams Mutually Exclusive Events Independent and Dependent Events Methods of Assigning Probability Classical Probability Assessment Relative Frequency Assessment Subjective Probability Assessment The Rules of Probability Measuring Probabilities Possible Values and the Summation of Possible Values Addition Rule for Individual Outcomes Complement Rule Addition Rule for Two Events Addition Rule for Mutuallly Exclusive Events Conditional Probability Tree Diagrams Conditional Probability for Independent Events Multiplication Rule Multiplication Rule for Two Events Using a Tree Diagram Multiplication Rule for Independent Events Bayes’ Theorem Visual Summary Equations Key Terms Chapter Exercises Case 4.1: Great Air Commuter Service Case 4.2: Let’s Make a Deal References

Chapter 5 Discrete Probability Distributions

Introduction to Discrete Probability Distributions Random Variables Displaying Discrete Probability Distributions Graphically Mean and Standard Deviation of Discrete Distributions Calculating the Mean Calculating the Standard Deviation The Binomial Probability Distribution The Binomial Distribution Characteristics of the Binomial Distribution Combinations Binomial Formula Using the Binomial Distribution Table Mean and Standard Deviation of the Binomial Distribution Additional Information about the Binomial Distribution Other Discrete Probability Distributions The Poisson Distribution Characteristics of the Poisson Distribution Poisson Probability Distribution Table The Mean and Standard Deviation of the Poisson Distribution The Hypergeometric Distribution The Hypergeometric Distribution with More Than Two Possible Outcomes per Trial Visual Summary Equations Key Terms Chapter Exercises Case 5.1: SaveMor Pharmacies Case 5.2: Arrowmark Vending Case 5.3: Boise Cascade Corporation References

Chapter 6 Introduction to Continuous Probability Distributions

The Normal Probability Distribution The Normal Distribution The Standard Normal Distribution Using the Standard Normal Table Approximate Areas under the Normal Curve Other Continuous Probability Distributions Uniform Probability Distribution The Exponential Probability Distribution Visual Summary Equations Key Terms Chapter Exercises Case 6.1: State Entitlement Programs Case 6.2: Credit Data, Inc. Case 6.3: American Oil Company References

Chapter 7 Introduction to Sampling Distributions

Sampling Error: What It Is and Why It Happens Calculating Sampling Error The Role of Sample Size in Sampling Error Sampling Distribution of the Mean Simulating the Sampling Distribution for Sampling from Normal Populations The Central Limit Theorem Sampling Distribution of a Proportion Working with Proportions Sampling Distribution of p Visual Summary Equations Key Terms Chapter Exercises Case 7.1: Carpita Bottling Company Case 7.2: Truck Safety Inspection References

Chapter 8 Estimating Single Population Parameters

Point and Confidence Interval Estimates for a Population Mean Point Estimates and Confidence Intervals Confidence Interval Estimate for the Population Mean, σ Known Confidence Interval Calculation Impact of the Confidence Level on the Interval Estimate Impact of the Sample Size on the Interval Estimate Confidence Interval Estimates for the Population Mean, σ Unknown Student’s t -Distribution Estimation with Larger Sample Sizes Determining the Required Sample Size for Estimating a Population Mean Determining the Required Sample Size for Estimating μ , σ Known Determining the Required Sample Size for Estimating μ , σ Unknown Estimating a Population Proportion Confidence Interval Estimate for a Population Proportion Determining the Required Sample Size for Estimating a Population Proportion Visual Summary Equations Key Terms Chapter Exercises Video Case 4: New Product Introductions @ McDonald’s Case 8.1: Management Solutions, Inc. Case 8.2: Federal Aviation Administration Case 8.3: Cell Phone Use References

Chapter 9 Introduction to Hypothesis Testing

Hypothesis Tests for Means Formulating the Hypotheses Null and Alternative Hypotheses Testing the Status Quo Testing a Research Hypothesis Testing a Claim about the Population Types of Statistical Errors Significance Level and Critical Value Hypothesis Test for μ , σ Known Calculating Critical Values Decision Rules and Test Statistics p -Value Approach Types of Hypothesis Tests p -Value for Two-Tailed Tests Hypothesis Test for μ , σ Unknown Hypothesis Tests for Proportions Testing a Hypothesis about a Single Population Proportion Type II Errors Calculating Beta Controlling Alpha and Beta Power of the Test Visual Summary Equations Key Terms Chapter Exercises Video Case 4: New Product Introductions @ McDonald’s Case 9.1: Campbell Brewery, Inc. — Part 1 Case 9.2: Wings of Fire References

Chapter 10 Estimation and Hypothesis Testing for Two Population Parameters

Estimation for Two Population Means Using Independent Samples Estimating the Difference between Two Population Means when σ 1 and σ 2 Are Known, Using Independent Samples Estimating the Difference between Two Means when σ 1 and σ 2 Are Unknown, Using Independent Samples What if the Population Variances Are Not Equal Hypothesis Tests for Two Population Means Using Independent Samples Testing for μ 1 – μ 1 When σ 1 and μ 2 Are Known, Using Independent Samples Using p -Values Testing μ 1 – μ 2 When σ 1 and σ 2 Are Unknown, Using Independent Samples What If the Population Variances are Not Equal? Interval Estimation and Hypothesis Tests for Paired Samples Why Use Paired Samples? Hypothesis Testing for Paired Samples Estimation and Hypothesis Tests for Two Population Proportions Estimating the Difference between Two Population Proportions Hypothesis Tests for the Difference between Two Population Proportions Visual Summary Equations Key Terms Chapter Exercises Case 10.1: Motive Power Company—Part 1 Case 10.2: Hamilton Marketing Services Case 10.3: Green Valley Assembly Company Case 10.4: U-Need-It Rental Agency References

Chapter 11 Hypothesis Tests and Estimation for Population Variances

Hypothesis Tests and Estimation for a Single Population Variance Chi-Square Test for One Population Variance Interval Estimation for a Population Variance Hypothesis Tests for Two Population Variances F -Test for Two Population Variances Additional F -Test Considerations Visual Summary Equations Key Terms Chapter Exercises Case 11.1: Motive Power Company—Part 2 References

Chapter 12 Analysis of Variance

One-Way Analysis of Variance Introduction to One-Way ANOVA Partitioning the Sum of Squares The ANOVA Assumptions Applying One-Way ANOVA The Tukey-Kramer Procedure for Multiple Comparisons Fixed Effects Versus Random Effects in Analysis of Variance Randomized Complete Block Analysis of Variance Randomized Complete Block ANOVA Was Blocking Necessary? Fisher’s Least Significant Difference Test Two-Factor Analysis of Variance with Replication Two-Factor ANOVA with Replications Interaction Explained A Caution about Interaction Visual Summary Equations Key Terms Chapter Exercises Video Case 3: Drive-Thru Service Times @ McDonald’s Case 12.1: Agency for New Americans Case 12.2: McLaughlin Salmon Works Case 12.3: NW Pulp and Paper Case 12.4: Quinn Restoration Business Statistics Capstone Project References

Chapters 8-12 Special Review Section

Chapters 8-12 Using the Flow Diagrams Exercises Term Project Assignments Business Statistics Capstone Project

Chapter 13 Goodness-of-Fit Tests and Contingency Analysis

Introduction to Goodness-of-Fit Tests Chi-Square Goodness-of-Fit Test Introduction to Contingency Analysis 2 × 2 Contingency Tables r × c Contingency Tables Chi-Square Test Limitations Visual Summary Equations Key Term Chapter Exercises Case 13.1: American Oil Company Case 13.2: Bentford Electronics—Part 1 References

Chapter 14 Introduction to Linear Regression and Correlation Analysis

Scatter Plots and Correlation The Correlation Coefficient Significance Test for the Correlation Cause-and-Effect Interpretations Simple Linear Regression Analysis The Regression Model and Assumptions Meaning of the Regression Coefficients Least Squares Regression Properties Significance Tests in Regression Analysis The Coefficient of Determination, R 2 Significance of the Slope Coefficient Uses for Regression Analysis Regression Analysis for Description Regression Analysis for Prediction Confidence Interval for the Average y Given x Prediction Interval for a Particular y Given x Common Problems Using Regression Analysis Visual Summary Equations Key Terms Chapter Exercises Case 14.1: A & A Industrial Products Case 14.2: Sapphire Coffee—Part 1 Case 14.3: Alamar Industries Case 14.4: Continental Trucking References

Chapter 15 Multiple Regression Analysis and Model Building

Introduction to Multiple Regression Analysis Basic Model-Building Concepts Model Specification Model Building Model Diagnosis Computing the Regression Equation The Coefficient of Determination Is the Model Significant? Are the Individual Variables Significant? Is the Standard Deviation of the Regression Model Too Large? Is Multicollinearity a Problem? Confidence Interval Estimation for Regression Coefficients Using Qualitative Independent Variables Possible Improvements to the First City Appraisal Model Working with Nonlinear Relationships The Partial- F Test Stepwise Regression Forward Selection Backward Elimination Standard Stepwise Regression Best Subsets Regression Determining the Aptness of the Model Analysis of Residuals Checking for Linearity Do the Residuals Have Equal Variances at all Levels of Each x Variable? Are the Residuals Independent? Checking for Normally Distributed Error Terms Corrective Actions Visual Summary Equations Key Terms Chapter Exercises Case 15.1: Dynamic Scales, Inc. Case 15.2: Glaser Machine Works Case 15.3: Hawlins Manufacturing Case 15.4: Sapphire Coffee—Part 2 Case 15.5: Wendell Motors References

Chapter 16 Analyzing and Forecasting Time-Series Data

Introduction to Forecasting, Time-Series Data, and Index Numbers General Forecasting Issues Components of a Time Series Trend Component Seasonal Component Cyclical Component Random Component Introduction to Index Numbers Aggregate Price Indexes Weighted Aggregate Price Indexes The Paasche Index The Laspeyres Index Commonly Used Index Numbers Consumer Price Index Producer Price Index Stock Market Indexes Using Index Numbers to Deflate a Time Series Trend-Based Forecasting Techniques Developing a Trend-Based Forecasting Model Comparing the Forecast Values to the Actual Data Autocorrelation True Forecasts Nonlinear Trend Forecasting Some Words of Caution Adjusting for Seasonality Computing Seasonal Indexes The Need to Normalize the Indexes Deseasonalizing Using Dummy Variables to Represent Seasonality Forecasting Using Smoothing Methods Exponential Smoothing Single Exponential Smoothing Double Exponential Smoothing Visual Summary Equations Key Terms Chapter Exercises Video Case 2: Restaurant Location and Re-imaging Decisions @ McDonald’s Case 16.1: Park Falls Chamber of Commerce Case 16.2: The St. Louis Companies Case 16.3: Wagner Machine Works References

Chapter 17 Introduction to Nonparametric Statistics

The Wilcoxon Signed Rank Test for One Population Median The Wilcoxon Signed Rank Test—Single Population Nonparametric Tests for Two Population Medians The Mann-Whitney U -Test Mann-Whitney U -Test—Large Samples The Wilcoxon Matched-Pairs Signed Rank Test Ties in the Data Large-Sample Wilcoxon Test Kruskal-Wallis One-Way Analysis of Variance Limitations and Other Considerations Visual Summary Equations Chapter Exercises Case 17.1: Bentford Electronics—Part 2 References

Chapter 18 Introduction to Quality and Statistical Process Control

Quality Management and Tools for Process Improvement The Tools of Quality for Process Improvement Process Flowcharts Brainstorming Fishbone Diagram Histograms Trend Charts Scatter Plots Statistical Process Control Charts Introduction to Statistical Process Control Charts The Existence of Variation Sources of Variation Types of Variation The Predictability of Variation: Understanding the Normal Distribution The Concept of Stability Introducing Statistical Process Control Charts Chart and R -Chart Using the Control Charts p -Charts Using the p -Chart c -Charts Other Control Charts Visual Summary Equations Key Terms Chapter Exercises Case 18.1: Izbar Precision Casters, Inc. References

APPENDIX A Random Numbers Table APPENDIX B Binomial Distribution Table APPENDIX C Poisson Probability Distribution Table APPENDIX D Standard Normal Distribution Table APPENDIX E Exponential Distribution Table APPENDIX F Values of t for Selected Probabilities APPENDIX G Values of χ 2 for Selected Probabilities APPENDIX H F -Distribution Table APPENDIX I Critical Values of Hartley’s F max Test APPENDIX J Distribution of the Studentized Range ( q -values) APPENDIX K Critical Values of r in the Runs Test APPENDIX L Mann-Whitney U Test Probabilities ( n < 9) APPENDIX M Mann-Whitney U Test Critical Values (9 ≤ n ≤ 20) APPENDIX N Critical Values of T in the Wilcoxon Matched-Pairs Signed-Ranks Test ( n ≤ 25) APPENDIX O Critical Values d L and d U of the Durbin-Watson Statistic D APPENDIX P Lower and Upper Critical Values W of Wilcoxon Signed-Ranks Test APPENDIX Q Control Chart Factors

Appendices Answers to Selected Odd-Numbered Problems Glossary Index

← Prev
Back
Next →

← Prev
Back
Next →