Statistics for managers using Microsoft Excel / David M. Levine, Timothy C. Krehbiel, David Stephan and Mark L. Berenson

"Prentice Hall just-in-time program."
LC copy lacks CD-ROM

Includes index

1. Introduction and Data Collection. What is Statistics. The Growth of Statistics and Information Technology. Microsoft Excel: The Solution to a Problem or a Problem Itself. Learning about Business Statistics. Learning Statistics with Microsoft Excel. Using Microsoft Excel in the Best Way Possible. Learning Business Statistics using this Text. How this Text is Organized. The Importance of Collecting Data. Identifying Sources of Data. Types of Survey Sampling Methods. Types of Data. 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. Measures of Central Tendency, Variation, and Shape. Obtaining Descriptive Summary Measures from a Population. Exploratory Data Analysis. The Coefficient of Correlation. Pitfalls in Numerical Descriptive Measures and Ethical Issues. CD-ROM Topic Obtaining Descriptive Summary Measures from a Frequency Distribution. 4 Basic Probability. Basic Probability Concepts. Conditional Probability. Bayes' Theorem. Ethical Issues and Probability. CD-ROM Topic: Counting Rules. 5. Some Important Discrete Probability Distributions. The Probability Distribution for a Discrete Random Variable. Covariance and Its Application in Finance. Binomial Distribution. Hypergeometric Distribution. Poisson Distribution. CD ROM Topic Using the Poisson Distribution to Approximate the Binomial Distribution. 6. The Normal Distribution and Other Continuous Distributions and Sampling Distributions. The Normal Distribution. Evaluating the Normality Assumption. The Uniform Distribution. The Exponential Distribution. Introduction to Sampling Distributions. Sampling Distribution of the Mean. Sampling Distribution of the Proportion. CD ROM Topic The Normal Approximation to the Binomial Distribution. CD ROM Topic Sampling from Finite Populations. 7. Confidence Interval Estimation. 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. Applications of Confidence Interval Estimation in Auditing. Confidence Interval Estimation and Ethical Issues. CD ROM Topic Estimation and Sample Size Determination for Finite Populations. 8. Fundamentals of Hypothesis Testing. 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. CD ROM Topic The Power of a Test. 9. Two Sample Tests. 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. F Test for Differences in Two Variances. 10. Analysis of Variance. The Completely Randomized Design: One-Way Analysis of Variance. The Factorial Design: Two-Way Analysis of Variance. CD ROM Topic The Randomized Block Design. 11. Chi-Square Tests and Nonparametric Tests. Chi-Square Test for Differences between Two Proportions. Chi-Square Test for Differences Among More than Two Proportions. Chi-Square Test of Independence. Wilcoxon Rank Sum Test: Nonparametric Analysis for Two Independent Populations. Kruskal-Wallis Rank Test: Nonparametric Analysis for the One-Way Design. CD ROM Topic Chi-Square Test for a Variance. CD ROM Topic Chi-Square Goodness of Fit Tests. 12. 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 Predicted Values. Pitfalls in Regression and Ethical Issues. Computations in Simple Linear Regression. 13. Introduction to Multiple Regression. Developing the Multiple Regression Model. Residual Analysis for the Multiple Regression Model. Testing for the Significance of the Multiple Regression Model. Inferences Concerning the Population Regression Coefficients. Testing Portions of the Multiple Regression Model. Using Dummy-Variables and Interaction Terms in Regression Models. 14. Multiple Regression Model Building. The Quadratic Regression Model. Using Transformations in Regression Models. Collinearity. Model Building. Pitfalls in Multiple Regression and Ethical Issues. 15. Time-Series Forecasting and Index Numbers. 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. Autoregressive Modeling for Trend Fitting and Forecasting. Choosing an Appropriate Forecasting Model. Time-Series Forecasting of Monthly or Quarterly Data. Index Numbers. Pitfalls Concerning Time-Series Analysis. 16. Decision Making. Payoff Tables and Decision Trees. Criteria for Decision Making. Decision Making with Sample Information. Utility. 17. Statistical Applications in Quality and Productivity Management. 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. Process Capability.

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