Business Statistics

Bob Donnelly is a professor at Goldey-Beacom College in Wilmington Delaware with more than 25 years of teaching experience. He teaches classes in statistics, operations management, management information systems, and database management at both the undergraduate and graduate level. Bob earned an undergraduate degree in chemical engineering from the University of Delaware, after which he worked for several years as an engineer with the Diamond Shamrock Corporation in a chlorine plant. Despite success in this field, Bob felt drawn to purse a career in education. It was his desire to teach that took him back to school to earn his MBA and Ph.D. in Operations Research, also from the University of Delaware. Bob also teaches in the MBA program at the International School of Management in Paris, France. He thoroughly enjoys discussing research methods and business statistics with both his French and American students.

Preface

Acknowledgments

Dear Students

1. An Introduction to Business Statistics

1.1 Business Statistics and Their Uses

1.2 Data

1.3 Descriptive and Inferential Statistics

1.4 Ethics and Statistics—It’s a Dangerous World of Data Out There

2. Displaying Descriptive Statistics

2.1 The Role Technology Plays in Statistics

2.2 Displaying Quantitative Data

2.3 Displaying Qualitative Data

2.4 Contingency Tables

2.5 Stem and Leaf Display

2.6 Scatter Plots

3. Calculating Descriptive Statistics

3.1 Measures of Central Tendency

3.2 Measures of Variability

3.3 Using the Mean and Standard Deviation Together

3.4 Working with Grouped Data

3.5 Measures of Relative Position

3.6 Measures of Association Between Two Variables

4. Introduction to Probabilities

4.1 An Introduction to Probabilities

4.2 Probability Rules for More Than One Event

4.3 Counting Principles

5. Discrete Probability Distributions

5.1 Introduction to Discrete Probability Distributions

5.2 Binomial Distributions

5.3 Poisson Distributions

5.4 The Hypergeometric Distribution

6. Continuous Probability Distributions

6.1 Continuous Random Variables

6.2 Normal Probability Distributions

6.3 Exponential Probability Distributions

6.4 Uniform Probability Distributions

7. Sampling and Sampling Distributions

7.1 Why Sample?

7.2 Types of Sampling

7.3 Sampling and Nonsampling Errors

7.4 The Central Limit Theorem

7.5 The Sampling Distribution of the Proportion

8. Confidence Intervals

8.1 Point Estimates

8.2 Calculating Confidence Intervals for the Mean when the Standard Deviation (σ) of a Population Is Known

8.3 Calculating Confidence Intervals for the Mean when the Standard Deviation (σ) of a Population Is Unknown

8.4 Calculating Confidence Intervals for Proportions

8.5 Determining the Sample Size

8.6 Calculating Confidence Intervals for Finite Populations

9. Hypothesis Testing for a Single Population

9.1 An Introduction to Hypothesis Testing

9.2 Hypothesis Testing for the Population Mean When σ Is Known

9.3 Hypothesis Testing for the Population Mean when σ Is Unknown

9.4 Hypothesis Testing for the Proportion of a Population

9.5 Type II Errors

10. Hypothesis Tests Comparing Two Populations

10.1 Comparing Two Population Means with

10.2 Comparing Two Population Means with

10.3 Hypothesis Testing With Dependent Samples

10.4 Comparing Two Population Proportions with Independent Samples

11. Analysis of Variance (ANOVA) Procedures

11.1 One-Way ANOVA: Examining the Effect a Single Factor Has on the Means of Populations

11.2 Randomized Block ANOVA: Examining the Effects of a Single Factor by Blocking a Second Factor

11.3 Two-Way ANOVA: Examining the Effects Two Factors Have on the Means of Populations

12. Chi-Square Tests

12.1 Comparing Two or More Population Proportions

12.2 Determining If Observed Frequencies Follow a Known Probability Distribution

12.3 Testing the Independence of Two Variables

13. Hypothesis Tests for the Population Variance

13.1 Testing the Variance of a Single Population

13.2 Comparing the Variances of Two Populations

14. Correlation and Simple Linear Regression

14.1 Dependent and Independent Variables

14.2 Correlation Analysis

14.3 Simple Linear Regression Analysis

14.4 Using a Regression to Make a Prediction

14.5 Testing the Significance of the Slope of the Regression Equation

14.6 Assumptions for Regression Analysis

14.7 A Simple Regression Example with a Negative Correlation

14.8 Some Final (but Very Important) Thoughts

15. Multiple Regression and Model Building

15.1 Developing the Multiple Regression Model

15.2 Explaining the Variation of the Dependent Variable

15.3 Inferences about the Independent Variables

15.4 Using Qualitative Independent Variables

15.5 Model Building

16. Forecasting

16.1 Introduction to Forecasting

16.2 Smoothing Forecasting Methods

16.3 Forecasting with Regression Analysis

16.4 Forecasting with Seasonality

17. Decision Analysis

17.1 Introduction to Decision Analysis

17.2 Constructing a Decision Table

17.3 Decision Making Under Uncertainty

17.4 Decision Making Under Risk

17.5 Decision Making Using Decision Trees

17.6 Using Bayes’ Theorem to Calculate Posterior Probabilities

18. Nonparametric Statistics

18.1 Introduction to Nonparametric Statistics

18.2 The Sign Test

18.3 The Wilcoxon Rank-Sum Test for Two Independent Samples

18.4 The Wilcoxon Signed-Rank Test for Two Dependent Samples

18.5 The Kruskal-Wallis One-Way ANOVA

18.6 The Spearman Rank-Order Correlation Coefficient

Appendix A

Table 1 Binomial Probabilities

Table 2 Poisson Probabilities

Table 3 Cumulative Probabilities for the Standard Normal Distribution

Table 4 Cumulative Probabilities for the Standard Normal Distribution

Table 5 Student’s t-distribution

Table 6 F-distribution

Table 7 Critical Values of the Studentized Range, Q

Table 8 Chi–Square Distribution

Table 9 Critical Values for the Durbin-Watson Statistic

Appendix B: Answers to Selected Even-Numbered Problems

Index of Applications

Index

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