Business Statistics

Ken Black is currently professor of quantitative management in the College of Business at the University of Houston–Clear Lake. Born in Cambridge, Massachusetts, and raised in Missouri, he earned a bachelor’s degree in mathematics from Graceland University, a master’s degree in math education from the University of Texas at El Paso, a Ph.D. in business administration (management science), and a Ph.D. in educational research from the University of North Texas. Since joining the faculty of UHCL in 1979, Professor Black has taught all levels of statistics courses, business analytics, forecasting, management science, market research, and production/operations management. He received the 2014 Outstanding Professor Alumni Award from UHCL. In 2005, he was awarded the President’s Distinguished Teaching Award for the university. He has published over 25 journal articles and 30 professional papers, as well as two textbooks: Business Statistics: An Introductory Course and Business Statistics for Contemporary Decision-Making. 

1 Introduction to Statistics and Business Analytics 

1.1 Basic Statistical Concepts

1.2 Data Measurement

1.3 Introduction to Business Analytics

 

2 Visualizing Data with Charts and Graphs   

2.1 Frequency Distributions

2.2 Measures of Variability

2.3 Qualitative Data Graphs

2.4 Charts and Graphs for Two Variables

2.5 Visualizing Time-Series Data

 

3 Descriptive Statistics   

3.1 Measures of Central Tendency

3.2 Measures of Variability

3.3 Measures of Shape

3.4 Business Analytics Using Descriptive Statistics

 

4 Probability   

4.1 Introduction to Probability

4.2 Structure of Probability

4.3 Marginal, Union, Joint, and Conditional Probabilities

4.4 Addition Laws

4.5 Multiplication Laws

4.6 Conditional Probability

 

5 Discrete Random Variables and Their Probability   

5.1 Discrete Random Variables

5.2 Describing a Discrete Distribution

5.3 Binomial Distribution

5.4 Poisson Distribution

5.5 Geometric Distribution

5.6 Hypergeometric Distribution

 

6 Continuous Random Variables and Normal Distributions  

6.1 Continuous Random Variables

6.2 The Uniform Distribution

6.3 Normal Distribution

6.4 Using the Normal Curve to Approximate Binomial Distribution Problems

6.5 Exponential Distribution

 

7 Sampling Data and Decision Making 

7.1 Sampling

7.2 Sampling Distribution of the Sample Mean

7.3 Sampling Distribution of a Sample Proportion

 

8 Statistical Inference: Estimation for Single Populations 

8.1 Estimating the Population Mean Using the z Statistic (σ Known)

8.2 Estimating the Population Mean Using the t Statistic (σ Unknown)

8.3 Estimating the Population Proportion

8.4 Estimating the Population Variance

8.5 Estimating Sample Size

 

9 Statistical Inference: Hypothesis Testing for Single Populations 

9.1 Introduction to Hypothesis Testing

9.2 Testing Hypotheses About a Population Mean Using the z Statistic (σ Known)

9.3 Testing Hypotheses About a Population Mean Using the t Statistic (σ Unknown)

9.4 Testing Hypotheses About a Proportion

9.5 Testing Hypotheses About a Variance

9.6 Solving for Type II Errors

 

10 Statistical Inference About Two Populations   

10.1 Hypothesis Testing and Confidence Intervals About the Difference in Two Means Using the z Statistic (Population Variances Known)

10.2 Hypothesis Testing and Confidence Intervals About the Difference in Two Means: Independent Samples and Population Variances Unknown

10.3 Statistical Inferences for Two Related Populations

10.4 Statistical Inferences About Two Population Proportions, p1 − p2

10.5 Testing Hypotheses About Two Population Variances

 

11 Analysis of Variance and Design of Experiments   

11.1 Introduction to Design of Experiments

11.2 The Completely Randomized Design (One-Way ANOVA)

11.3 Multiple Comparison Tests

11.4 The Randomized Block Design

11.5 A Factorial Design (Two-Way ANOVA)

 

12 Simple Regression Analysis and Correlation   

12.1 Correlation

12.2 Introduction to Simple Regression Analysis

12.3 Determining the Equation of the Regression Line

12.4 Residual Analysis

12.5 Standard Error of the Estimate

12.6 Coefficient of Determination

12.7 Hypothesis Tests for the Slope of the Regression Model and Testing the Overall Model

12.8 Estimation

12.9 Using Regression to Develop a Forecasting Trend Line

12.10 Interpreting the Output

 

13 Multiple Regression Analysis   

13.1 The Multiple Regression Model

13.2 Significance Tests of the Regression Model and Its Coefficients

13.3 Residuals, Standard Error of the Estimate, and R2

13.4 Interpreting Multiple Regression Computer Output

 

14 Building Multiple Regression Models   

14.1 Nonlinear Models: Mathematical Transformation

14.2 Indicator (Dummy) Variables

14.3 Model-Building: Search Procedures

14.4 Multicollinearity

14.5 Logistic Regression

 

15 Time-Series Forecasting and Index Numbers   

15.1 Introduction to Forecasting

15.2 Smoothing Techniques

15.3 Trend Analysis

15.4 Seasonal Effects

15.5 Autocorrelation and Autoregression

15.6 Index Numbers

 

16 Analysis of Categorical Data   

16.1 Chi-Square Goodness-of-Fit Test

16.2 Contingency Analysis: Chi-Square Test of Independence

 

17 Nonparametric Statistics   

17.1 Runs Test

17.2 Mann-Whitney U Test

17.3 Wilcoxon Matched-Pairs Signed Rank Test

17.4 Kruskal-Wallis Test

17.5 Friedman Test

17.6 Spearman’s Rank Correlation

 

18 Statistical Quality Control   

18.1 Introduction to Quality Control

18.2 Process Analysis

18.3 Control Charts

 

19 Decision Analysis    

19.1 Revision of Probabilities: Bayes’ Rule

19.2 The Decision Table and Decision-Making Under Certainty

19.3 Decision-Making Under Uncertainty

19.4 Decision-Making Under Risk

19.5 Revising Probabilities in Light of Sample Information

 

APPENDIX A

APPENDIX B  

GLOSSARY

INDEX