Business Statistics

1. Introduction and Data Collection
Using Statistics: Good Tunes



Basic Concepts of Statistics. The Growth of Statistics and Information

Technology. How This Text Is Organized The Importance of Collecting Data.

Identifying Sources of Data. Types of Data.


2. Presenting Data in Tables and Charts
Using Statistics: Comparing the Performance of Mutual Funds



Tables and Charts for Categorical Data. Tables and Charts for Numerical Data.

Cross Tabulations. Scatter Diagrams and Time Series Plots. Misusing Graphs

and Ethical Issues


3. Numerical Descriptive Measures
Using Statistics: Comparing the Performance of Mutual Funds



Measures of Central Tendency, Variation, and Shape. Obtaining Descriptive

Summary Measures from a Population. Exploratory Data Analysis. The

Covariance and the Coefficient of Correlation. Pitfalls in Numerical

Descriptive Measures and Ethical Issues.

4. Basic Probability
Using Statistics: The Consumer Electronics Company



Basic Probability Concepts. Conditional Probability. Bayes' Theorem. Counting

Rules. Ethical Issues and Probability.


5. Some Important Discrete Probability Distributions
Using Statistics: The Accounting Information System of the Saxon

Plumbing Company



The Probability Distribution for a Discrete Random Variable. Binomial Distribution.

Poisson Distribution. (CD-ROM Topic) The Hypergeometric Distribution.



6. The Normal Distribution and Other Continuous Distributions
Using Statistics: Download Time for a Web Site Home Page



The Normal Distribution. Evaluating the Normality Assumption. The Uniform

Distribution. The Exponential Distribution.


7. Sampling Distributions
Using Statistics: The Oxford Cereal Company Packaging Process



Sampling Distribution of the Mean. Sampling Distribution of the Proportion.

Types of Survey Sampling Methods. Evaluating Survey Worthiness.





8. Confidence Interval Estimation
Using Statistics: Auditing Invoices at the Saxon Plumbing Company



Confidence Interval Estimation of the Mean (s Known). Confidence Interval

Estimation of the Mean (s Unknown). Confidence Interval Estimation for the

Proportion. Determining Sample Size. Confidence Interval Estimation and

Ethical Issues.


9. Fundamentals of Hypothesis Testing
Using Statistics: The Oxford Cereal Company Packaging Process



Hypothesis-Testing Methodology. Z Test of Hypothesis for the Mean (s Known).

One-Tailed Tests. t Test of Hypothesis for the Mean (s Unknown). Z Test of

Hypothesis for the Proportion. Potential Hypothesis-Testing Pitfalls and Ethical

Issues.


10. Two Sample Tests using the Normaland t Distributions
Using Statistics: Comparing Sales from End-Aisle Displays and Normal

Displays



Comparing Two Independent Samples: Tests for Differences in Two Means.

Comparing Two Related Samples: Tests for the Mean Difference. Z Test for the

Difference between Two Proportions.


11. Chi-Square Tests and F Tests
Using Statistics: Guest satisfaction at T. C. Resort Properties



Chi-Square Test for Differences between Two Proportions. Chi-Square Test for

Differences in Among More than Two Proportions. Chi-Square Test of

Independence. Chi-Square Goodness of Fit Tests. F Test for Differences in Two

Variances. The Analysis of Variance (ANOVA).


12. Simple Linear Regression
Using Statistics: Forecasting Sales at the Sunflowers Clothing Stores



Correlation and Simple Linear Regression. Determining the Simple Linear

Regression Equation. Measures of Variation. Model Assumptions and Residual

Analysis. Measuring Autocorrelation: The Durbin-Watson Statistic. Inferences

Concerning the Slope. Estimation of Mean Values and Prediction of Individual

Values. Pitfalls in Regression and Ethical Issues. Computations in Simple

Linear Regression.



13. Multiple Regression
Using Statistics: Predicting OmniPower Sales



Developing the Multiple Regression Model. R2, adjusted R2, and the Overall F

test. Model Assumptions and Residual Analysis. Inferences Concerning the

Population Regression Coefficients. Using Dummy-Variables and Interaction

Terms in Regression Models. The Quadratic Regression Model.

Collinearity. Model Building. Pitfalls in Multiple Regression and Ethical Issues.



14. Statistical Applications in Quality and Productivity Management

Using Statistics: Service Quality at the Beachcomber Hotel



Total Quality Management. Six Sigma (R) Management. The Theory of Control

Charts. Control Chart for the Proportion of Nonconforming Items-The p Chart.

The Red Bead Experiment: Understanding Process Variability. Control Charts

for the Range and the Mean.



Answers to Self -Test Problems



Answers to Even-Numbered Problems



Appendices



Index



CD-ROM TOPICS



Chapter Sections