Basic Business Statistics

(NOTE: Each chapter concludes with Key Terms, Chapter Review Problems, and a Case Study.)

 1. Introduction and Data Collection.


Why a Manager Needs to Know about Statistics. The Growth and Development of Modern Statistics. What Readers of This Textbook Need to Know about Microsoft Excel and PHStat or Minitab. Why Data are Needed. Sources of Data. Types of Data. Design of Survey Research. Types of Sampling Methods. Evaluating Survey Worthiness.



 2. Presenting Data in Tables and Charts.


Organizing Numerical Data. Tables and Charts for Numerical Data. Graphing Bivariate Numerical Data. Tables and Charts for Categorical Data. Tabulating and Graphing Bivariate Categorical Data. Graphical Excellence.



 3. Numerical Descriptive Measures.


Exploring Numerical Data and Their Properties. Measure of Central Tendency, Variation, and Shape. Exploratory Data Analysis. Obtaining Descriptive Summary Measures from a Population. The Coefficient of Correlation. Pitfalls in Numerical Descriptive Measures and Ethical Issues. Obtaining Descriptive Summary Measures from a Frequency Distribution (CD-ROM Topic).



 4. Basic Probability.


Basic Probability Concepts. Conditional Probability. Bayes' Theorem. Ethical Issues and Probability.



 5. Some Important Discrete Probability Distributions.


The Probability Distribution for a Discrete Random Variable. Covariance and Its Application to Finance. Binomial Distribution. Poisson Distribution. Hypergeometric Distribution.



 6. The Normal Distribution and Other Continuous Distributions.


The Normal Distribution. Evaluating the Normality Assumption. The Exponential Distribution.



 7. Sampling Distributions.


Sampling Distributions of the Mean. Sampling Distributions of the Proportions. Sampling from Finite Populations (CD-ROM Topic).



 8. Confidence Interval Estimation.


Confidence Interval Estimation of the Mean (… Known). Confidence Interval Estimation of the Mean (… Unknown). Confidence Interval Estimation for the Proportion. Determining Sample Size. Applications of Confidence Interval Estimation in Auditing. Confidence Interval Estimation and Ethical Issues. Estimation and Sample Size Determination for Finite Populations (CD-ROM Topic).



 9. Fundamentals of Hypothesis Testing: One-Sample Tests.


Hypothesis Testing Methodology. Z Test of Hypothesis for the Mean (… Known). One-Tail Tests. t Test of Hypothesis for the Mean (… Unknown). Z Test of Hypothesis for the Proportion. …2 Test of Hypothesis for the Variance or Standard Deviation. The Power of a Test (CD-ROM Topic). Potential Hypothesis-Testing Pitfalls and Ethical Issues.



10. Two-Sample Tests with Numerical Data.


Comparing Two Independent Samples: t Tests for Differences in Two Means. F Test for Difference in Two Variances. Comparing Two Related Samples: Test for the Mean Difference. Wilcoxon Rank Sum Test for Differences in Two Medians. Wilcoxon Signed-Ranks Test for the Median Difference (CD-ROM Topic).



11. Analysis of Variance.


The Completely Randomized Design: One-Way Analysis of Variance. The Randomized Block Design. The Factorial Design: Two-Way Analysis of Variance. Kruskal-Wallis Rank Test for Differences in c Medians. Friedman Rank Test for Differences in c Medians (CD-ROM Topic).



12. Tests for Two or More Samples with Categorical Data.


Z Test for the Differences Between Two Proportions. …2 Test for the Difference Between Two Proportions. …2 Test for Differences in More Than Two Proportions. …2 Test of Independence.



13. Simple Linear Regression.


Types of Regression Models. Determining the Simple Linear Regression Equation. Measures of Variation. Assumptions. Residual Analysis. Measuring Autocorrelation: The Durbin-Watson Statistic. Inferences about the Slope and Correlation Coefficient. Estimation of Mean Values and Prediction of Individual Values. Pitfalls in Regression and Ethical Issues. Computations in Simple Linear Regression.



14. Introduction to Multiple Regression.


Developing the Multiple Regression Model. Residual Analysis for the Multiple Regression Model. Influence Analysis. Testing for the Significance of the Multiple Regression Model. Inferences Concerning the Population Regression Coefficients. Testing Proportions of the Multiple Regression Model. Logistic Regression.



15. Multiple Regression Model Building.


The Quadratic Regression Model. Dummy-Variable Models. Using Transformations in Regression Models. Collinearity. Model Building. Pitfalls in Multiple Regression and Ethical Issues.



16. Time-Series Analysis.


The Importance of Business Forecasting. Component Factors of the Classical Multiplicative Time-Series Model. Smoothing the Annual Time Series. Least-Squares Trend Fitting and Forecasting. The Holt-Winners Method for Trend Fitting and Forecasting. Autoregressive Modeling for Trend Fitting and Forecasting. Choosing an Appropriate Forecasting Model. Time-Series Forecasting of Monthly or Quarterly Data. Pitfalls Concerning Time-Series Analysis. Index Numbers (CD-ROM Topic).



17. Decision Making.


The Payoff Table and Decision Trees. Criteria for Decision Making. Decision Making with Sample Information. Utility.



18. Statistical Applications in Quality and Productivity Management.


Quality and Productivity: A Historical Perspective. Deming's 14 Points: A Theory of 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 Chart for An Area of Opportunity—The c Chart. Control Charts for the Range and the Mean. Process Capability.