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® 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