Statistics for Business and Economics

Dr. Jim McClave is currently President and CEO of Info Tech, Inc., a statistical consulting and software development firm with an international clientele. He is an Adjunct Professor of Statistics at the University of Florida, where he was a full-time member of the faculty for 20 years.   P. George Benson is the 21st president of the College of Charleston. Prior to his appointment, he was Dean at the University of Georgia’s C. Herman and Mary Virginia Terry College of Business. His research interests include quality management, strategic management, belief formation, and judgmental forecasting. He consults nationally in the areas of applied statistics, quality management, and employment discrimination.   Terry Sincich obtained his PhD in statistics from the University of Florida in 1980. He is an Associate Professor in the Information Systems & Decision Sciences Department at the University of South Florida in Tampa. Dr. Sincich is responsible for teaching basic statistics to all undergraduates in the College of Business, as well as advanced statistics to all business doctoral candidates. He has published articles in such journals as the Journal of the American Statistical Association, International Journal of Forecasting, Academy of Management Journal, and Auditing: A Journal of Practice & Theory. Dr. Sincich is a co-author of the texts Statistics, A First Course in Statistics, Statistics for Engineering & the Sciences, and A Second Course in Statistics: Regression Analysis.

1. Statistics, Data, and Statistical Thinking

1.1 The Science of Statistics

1.2 Types of Statistical Applications in Business

1.3 Fundamental Elements of Statistics

1.4 Processes (Optional)

1.5 Types of Data

1.6 Collecting Data: Sampling and Related Issues

1.7 Critical Thinking with Statistics

   Statistics in Action: A 20/20 View of Surveys: Fact or Fiction?

   Activity 1.1: Keep the Change: Collecting Data

   Activity 2.2: Identifying Misleading Statistics

   Using Technology: Accessing and Listing Data; Random Sampling

 

2. Methods for Describing Sets of Data

2.1 Describing Qualitative Data

2.2 Graphical Methods for Describing Quantitative Data

2.3 Numerical Measures of Central Tendency

2.4 Numerical Measures of Variability

2.5 Using the Mean and Standard Deviation to Describe Data

2.6 Numerical Measures of Relative Standing

2.7 Methods for Detecting Outliers: Box Plots and z-Scores

2.8 Graphing Bivariate Relationships (Optional)

2.9 The Time Series Plot (Optional)

2.10 Distorting the Truth with Descriptive Techniques

   Statistics in Action: Can Money Buy Love?

   Activity 2.1: Real Estate Sales

   Activity 2.2: Keep the Change: Measures of Central Tendency and Variability

   Using Technology: Describing Data

   Making Business Decisions: The Kentucky Milk CasePart 1 (Covers Chapters 1 and 2)

 

3. Probability

3.1 Events, Sample Spaces, and Probability

3.2 Unions and Intersections

3.3 Complementary Events

3.4 The Additive Rule and Mutually Exclusive Events

3.5 Conditional Probability

3.6 The Multiplicative Rule and Independent Events

3.7 Bayes’s Rule

   Statistics in Action: Lotto Buster!

   Activity 3.1: Exit Polls: Conditional Probability

   Activity 3.2: Keep the Change: Independent Events

   Using Technology: Combinations and Permutations

 

4. Random Variables and Probability Distributions

4.1 Two Types of Random Variables

PART I: Discrete Random Variables

4.2 Probability Distributions for Discrete Random Variables

4.3 The Binomial Distribution

4.4 Other Discrete Distributions: Poisson and Hypergeometric

PART II: Continuous Random Variables

4.5 Probability Distributions for Continuous Random Variables

4.6 The Normal Distribution

4.7 Descriptive Methods for Assessing Normality

4.8 Other Continuous Distributions: Uniform and Exponential

   Statistics in Action: Probability in a Reverse Cocaine Sting: Was Cocaine Really Sold?

   Activity 4.1: Warehouse Club Memberships: Exploring a Binomial Random Variable

   Activity 4.2: Identifying the Type of Probability Distribution

   Using Technology: Discrete Probabilities, Continuous Probabilities, and Normal Probability Plots

 

5. Sampling Distributions

5.1 The Concept of a Sampling Distribution

5.2 Properties of Sampling Distributions: Unbiasedness and Minimum Variance

5.3 The Sampling Distribution of the Sample Mean and the Central Limit Theorem

5.4 The Sampling Distribution of the Sample Proportion

   Statistics in Action: The Insomnia Pill: Is It Effective?

   Activity 5.1: Simulating a Sampling DistributionCell Phone Usage

   Using Technology: Simulating a Sampling Distribution

   Making Business Decisions: The Furniture Fire Case (Covers Chapters 3–5)

 

6. Inferences Based on a Single Sample: Estimation with Confidence Intervals

6.1 Identifying and Estimating the Target Parameter

6.2 Confidence Interval for a Population Mean: Normal (z) Statistic

6.3 Confidence Interval for a Population Mean: Student’s t-Statistic

6.4 Large-Sample Confidence Interval for a Population Proportion

6.5 Determining the Sample Size

6.6 Finite Population Correction for Simple Random Sampling (Optional)

6.7 Confidence Interval for a Population Variance (Optional)

   Inferences Based on a Single Sample: Estimation with Confidence Intervals

   Statistics in Action: Medicare Fraud Investigations

   Activity 6.1: Conducting a Pilot Study

   Using Technology: Confidence Intervals

 

7. Inferences Based on a Single Sample: Tests of Hypotheses

7.1 The Elements of a Test of Hypothesis

7.2 Formulating Hypotheses and Setting Up the Rejection Region

7.3 Observed Significance Levels: p-Values

7.4 Test of Hypothesis about a Population Mean: Normal (z) Statistic

7.5 Test of Hypothesis about a Population Mean: Student’s t-Statistic

7.6 Large-Sample Test of Hypothesis about a Population Proportion

7.7 Test of Hypothesis about a Population Variance

7.8 Calculating Type II Error Probabilities: More about b (Optional)

   Statistics in Action: Diary of a Kleenex® User—How Many Tissues in a Box?

   Activity 7.1: Challenging a Company's Claim: Tests of Hypotheses

   Activity 7.2: Keep the Change: Tests of Hypotheses

   Using Technology: Tests of Hypotheses

 

8. Inferences Based on Two Samples: Confidence Intervals and Tests of Hypotheses

8.1 Identifying the Target Parameter

8.2 Comparing Two Population Means: Independent Sampling

8.3 Comparing Two Population Means: Paired Difference Experiments

8.4 Comparing Two Population Proportions: Independent Sampling

8.5 Determining the Required Sample Size

8.6 Comparing Two Population Variances: Independent Sampling

   Statistics in Action: ZixIt Corp. v. Visa USA Inc.—A Libel Case

   Activity 8.1: Box Office Receipts: Comparing Population Means

   Activity 8.2: Keep the Change: Inferences Based on Two Samples

   Using Technology: Two-Sample Inferences

   Making Business Decisions: The Kentucky Milk Case—Part II (Covers Chapters 6–8)

 

9. Design of Experiments and Analysis of Variance

9.1 Elements of a Designed Experiment

9.2 The Completely Randomized Design: Single Factor

9.3 Multiple Comparisons of Means

9.4 The Randomized Block Design

9.5 Factorial Experiments: Two Factors

   Statistics in Action: Pollutants at a Housing Development—A Case of Mishandling Small Samples

   Activity 9.1: Designed vs. Observational Experiments

   Using Technology: Analysis of Variance

 

10. Categorical Data Analysis

10.1 Categorical Data and the Multinomial Experiment

10.2 Testing Category Probabilities: One-Way Table

10.3 Testing Category Probabilities: Two-Way (Contingency) Table

10.4 A Word of Caution about Chi-Square Tests

   Statistics in Action: The Case of the Ghoulish Transplant Tissue—Who Is Responsible for Paying Damages?

   Activity 10.1: Binomial vs. Multinomial Experiments

   Activity 10.2: Contingency Tables

   Using Technology: Chi-Square Analyses

   Making Business Decisions: Discrimination in the Workplace (Covers Chapters 9 and 10)

 

11. Simple Linear Regression

11.1 Probabilistic Models

11.2 Fitting the Model: The Least Squares Approach

11.3 Model Assumptions

11.4 Assessing the Utility of the Model: Making Inferences about the Slope b1

11.5 The Coefficients of Correlation and Determination

11.6 Using the Model for Estimation and Prediction

11.7 A Complete Example

   Statistics in Action: Legal Advertising—Does It Pay?

   Activity 11.1: Apply Simple Linear Regression to Your Favorite Data

   Using Technology: Simple Linear Regression

 

12. Multiple Regression and Model Building

12.1 Multiple Regression Models

PART I: First-Order Models with Quantitative Independent Variables

12.2 Estimating and Making Inferences about the b Parameters

12.3 Evaluating Overall Model Utility

12.4 Using the Model for Estimation and Prediction

PART II: Model Building in Multiple Regression

12.5 Interaction Models

12.6 Quadratic and Other Higher-Order Models

12.7 Qualitative (Dummy) Variable Models

12.8 Models with Both Quantitative and Qualitative Variables

12.9 Comparing Nested Models

12.10 Stepwise Regression

PART III: Multiple Regression Diagnostics

12.11 Residual Analysis: Checking the Regression Assumptions

12.12 Some Pitfalls: Estimability, Multicollinearity, and Extrapolation

   Statistics in Action: Bid Rigging in the Highway Construction Industry

   Activity 12.1: Insurance Premiums: Collecting Data for Several Variables

   Activity 12.2: Collecting Data and Fitting a Multiple Regression Model

   Using Technology: Multiple Regression

   Making Business Decisions: The Condo Sales Case (Covers Chapters 11 and 12)

 

13. Methods for Quality Improvement: Statistical Process Control (Available on CD)

13.1 Quality, Processes, and Systems

13.2 Statistical Control

13.3 The Logic of Control Charts

13.4 A Control Chart for Monitoring the Mean of a Process: The [x-bar]-Chart

13.5 A Control Chart for Monitoring the Variation of a Process: The R-Chart

13.6 A Control Chart for Monitoring the Proportion of Defectives Generated by a Process: The p-Chart

13.7 Diagnosing the Causes of Variation

13.8 Capability Analysis

   Statistics in Action: Testing Jet Fuel Additive for Safety

   Activity 13.1: Quality Control: Consistency

   Using Technology: Control Charts

   MAKING BUSINESS DECISIONS: The Gasket Manufacturing Case (Covers Chapter 13)

 

14. Time Series: Descriptive Analyses, Models, and Forecasting (Available on CD)

14.1 Descriptive Analysis: Index Numbers

14.2 Descriptive Analysis: Exponential Smoothing

14.3 Time Series Components

14.4 Forecasting: Exponential Smoothing

14.5 Forecasting Trends: Holt’s Method

14.6 Measuring Forecast Accuracy: MAD and RMSE

14.7 Forecasting Trends: Simple Linear Regression

14.8 Seasonal Regression Models

14.9 Autocorrelation and the Durbin-Watson Test

   Statistics in Action: Forecasting the Monthly Sales of a New Cold Medicine

   Activity 14.1: Time Series

   Using Technology: Forecasting

 

15. Nonparametric Statistics (Available on CD)

15.1 Introduction: Distribution-Free Tests

15.2 Single Population Inferences

15.3 Comparing Two Populations: Independent Samples

15.4 Comparing Two Populations: Paired Difference Experiment

15.5 Comparing Three or More Populations: Completely Randomized Design

15.6 Comparing Three or More Populations: Randomized Block Design

15.7 Rank Correlation

   Statistics in Action: How Vulnerable Are New Hampshire Wells to Groundwater Contamination?

   Activity 15.1: Keep the Change: Nonparametric Statistics

   Using Technology: Nonparametric Tests

   Making Business Decisions: Detecting “Sales Chasing” (Covers Chapters 10 and 15)

 

 

Appendix A: Summation Notation

Appendix B: Basic Counting Rules

Appendix C: Calculation Formulas for Analysis of Variance

   C.1 Formulas for the Calculations in the Completely Randomized Design

   C.2 Formulas for the Calculations in the Randomized Block Design

   C.3 Formulas for the Calculations for a Two-Factor Factorial Experiment

   C.4 Tukey's Multiple Comparisons Procedure (Equal Sample Sizes)

   C.5 Bonferroni Multiple Comparisons Procedure (Pairwise Comparisons)

   C.6 Scheffé's Multiple Comparisons Procedure (Pairwise Comparisons)

Appendix D: Tables

   Table I. Binomial Probabilities

   Table II. Normal Curve Areas

   Table III. Critical Values of t

   Table IV. Critical Values of x2

   Table V. Percentage Points of the F-Distribution, α = .10

   Table VI. Percentage Points of the F-Distribution, α = .05

   Table VII. Percentage Points of the F-Distribution, α = .025

   Table VIII. Percentage Points of the F-Distribution, α = .01

   Table IX. Control Chart Constants

   Table X. Critical Values for the Durbin-Watson d-Statistic, α = .05

   Table XI. Critical Values for the Durbin-Watson d-Statistic, α = .01

   Table XII. Critical Values of TL and Tu for the Wilcoxon Rank Sum Test: Independent Samples

   Table XIII. Critical Values of T0 in the Wilcoxon Paired Difference Signed Rank Test

   Table XIV. Critical Values of Spearman's Rank Correlation Coefficient

   Table XV. Critical Values of the Studentized Range, α = .05

 

 

Answers to Selected Exercises

Index

Credits