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Cengage Learning
Statistics for Management and Economics: Systematic Approach (Non-InfoTrac Version) / Edition 6

Statistics for Management and Economics: Systematic Approach (Non-InfoTrac Version) / Edition 6


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Product Details

ISBN-13: 9780534391652
Publisher: Cengage Learning
Publication date: 07/01/2002
Pages: 1050

About the Author

Dr. Gerald Keller is Emeritus Professor of Business at Wilfrid Laurier University, where he taught statistics, management science, and operations management from 1974 to 2011. He also taught at the University of Toronto, the University of Miami, McMaster University, the University of Windsor, and the Beijing Institute of Science and Technology. In addition to consulting with banks on credit scoring and credit card fraud, Dr. Keller has conducted market surveys for the Canadian government on energy conservation. His books include BSTAT, 2e, APPLIED STATISTICS WITH MICROSOFT EXCEL, ESSENTIALS OF BUSINESS STATISTICS (co-authored), AUSTRALIAN BUSINESS STATISTICS (co-authored), and STATISTICS LABORATORY MANUAL EXPERIMENTS USING MINITAB. Dr. Keller also has been published in OMEGA, IIE TRANSACTIONS, DECISION SCIENCES, INFOR, ECONOMICS LETTERS, and ARCHIVES OF SURGERY.

Dr. Brian Warrack: After teaching in the Faculty of Commerce and Business Administration at the University of Alberta, Dr. Warrack joined Wilfrid Laurier University, where he has taught courses in corporate finance, investment management, statistics, and decision analysis. He also has published several articles and cases in these areas. His current research interest lies in financial markets. Dr. Warrack also is co-author of PROXIMITY SPACES.

Table of Contents

1. WHAT IS STATISTICS? Introduction to Statistics. Key Statistical Concepts. Statistical Application in Business. Statistics and the Computer. World Wide Web and Learning Center. Introduction to Microsoft Excel. Introduction to Minitab. 2. GRAPHICAL DESCRIPTIVE TECHNIQUES. Introduction. Types of Data and Information. Graphical Techniques for Interval Data. Graphical Techniques for Nominal Data. Describing the Relationship Between Two Variables. Describing Time-Series Data. Summary. 3. ART AND SCIENCE OF GRAPHICAL PRESENTATIONS. Introduction Graphical Excellence. Graphical Deception. Presenting Statistics. Summary. 4. NUMERICAL DESCRIPTIVE TECHNIQUES. Introduction. Measures of Central Location. Measures of Variability. Measures of Relative Standing and Box Plots. Measures of Linear Relationship. Comparing Graphical and Numerical Techniques. General Guidelines for Exploring Data. Summary. CD Topic: Approximating Means and Variances from Grouped Data.Review of Descriptive Techniques. CD Topic: Descriptive Techniques. Review Exercises. 5. DATA COLLECTION AND SAMPLING. Introduction. Sources of Data. Sampling. Sampling Plans. Sampling and Nonsampling Errors. Summary. 6. PROBABILITY. Introduction. Assigning Probability to Events. Joint, Marginal, and Conditional Probability. Probability Rules and Trees. Bayes'''' Law. Identifying the Correct Method. Summary. 7. RANDOM VARIABLES AND DISCRETE PROBABILITY DISTRIBUTIONS. Introduction. Random Variables and Probability Distributions. Describing the Population/Probability Distribution. Bivariate Distributions (optional). Applications in Finance: Investment Portfolio Diversification and Asset Allocations (optional). Binomial Distribution. Poisson Distribution (optional). Summary. CD Topic: Hypergeometric Distribution. 8. CONTINUOUS PROBABILITY DISTRIBUTIONS. Introduction. Continuous Probability Distributions. Normal Distribution. Exponential Distribution (optional). Other Continuous Distributions. Summary. 9. SAMPLING DISTRIBUTIONS. Introduction. Sampling Distribution of the Mean. Sampling Distribution of a Proportion. Sampling Distribution of the Difference between Two Means. From Here to Inference. Summary. CD Topic: Using the Laws of Expected Value and Variance to Derive the Parameters of Sampling Distributions. 10. INTRODUCTION TO ESTIMATION. Introduction. Concepts of Estimation. Estimating the Population Mean when the Population Standard Deviation is Known. Selecting the Sample Size. Summary. 11. INTRODUCTION TO HYPOTHESIS TESTING. Introduction. Concepts of Hypothesis Testing. Testing the Population Mean when the Population Standard Deviation is Known. Calculating the Probability of a Type II Error. The Road Ahead. Summary. 12. INFERENCE ABOUT ONE POPULATION. Introduction. Inference about a Population Mean when the Standard Deviation is Unknown. Inference about a Population Variance. Inference about a Population Proportion. Applications in Marketing: Market Segmentation. Summary. CD Topic: Probability of a Type II Error in Testing Proportions. 13. INFERENCE ABOUT TWO POPULATIONS. Introduction. Inference about the Difference between Two Means: Independent Samples. Observational and Experimental Data. Inference about the Difference between Two Means: Matched Pairs Experiment. Inference about the Ratio of Two Variances. Inference about the Difference between Two Population Proportions. Summary. Excel Instructions for Stacked and Unstacked Data. MINITAB Instructions for Stacked and Unstacked Data. CD Topic: Probability of a Type II Error in Testing Two Means. CD Topic: Probability of a Type II Error in Testing Two Proportions. 14. REVIEW OF CHAPTERS 12 AND 13. Introduction. Guide to Identifying the Correct Technique: Chapters 12 and 13. 15. ANALYSIS OF VARIANCE. Introduction. One Way Analysis of Variance. Analysis of Variance Experimental Designs. Randomized Blocks (Two-Way) Analysis of Variance. Two-Factor Analysis of Variance. Applications in Operations Management: Finding and Reducing Variation. Multiple Comparisons. Summary. CD Topic: Bartlett'''s Test. 16. CHI-SQUARE TESTS. Introduction. Chi-Square Goodness-of-Fit Test. Chi-Square Test of a Contingency Table. Summary of Tests on Nominal Data. Chi-Square Test for Normality (optional). Summary. CD Topic: Rule of Five. CD Topic: Minitab Instructions. 17. NONPARAMETRIC STATISTICS TECHNIQUES. Introduction. Wilcoxon Rank Sum Test. Sign Test and Wilcoxon Signed Rank Sum Test. Kruskal-Wallis Test. Friedman Test. Summary. CD Topic: Lillifors'''' Test. Mann-Whitney Test. 18. SIMPLE LINEAR REGRESSION. Introduction. Model. Estimating the Coefficients. Error Variable: Required Conditions. Assessing the Model. Applications in Finance: Market Model (optional). Using the Regression Equation. Coefficients of Correlation. Regression Diagnostics I. Summary. CD Topic: Deriving the Formulas that Estimate the Coefficients. 19. MULTIPLE REGRESSION. Introduction. Model and Required Conditions. Estimating the Coefficients and Assessing the Model. Regression Diagnostics II. Regression Diagnostics III (Time Series). Summary. CD Topic: Transformations. 20. MODEL BUILDING. Introduction. Polynomial Models. Nominal Independent Variables. Applications in Human Resource Management: Pay Equity. Stepwise Regression. Model Building. Summary. CD Topic: Regression and the Analysis of Variance. 21. TIME SERIES ANALYSIS AND FORECASTING. Introduction. Time Series Components. Smoothing Techniques. Trend and Seasonal Effects. Introduction to Forecasting. Forecasting Models. Summary. CD Topic: Index Numbers. 22. STATISTICAL PROCESS CONTROL. Introduction. Process Variation. Control Charts. Control Charts for Variables: x-bar and S Charts. Control Charts for Attributes: p Chart. Summary. CD Topic: Control Charts for variables: x-bar and R Charts. 23. DECISION ANALYSIS. Introduction. Decision Problem. Acquiring, Using and Evaluating Additional Information. Summary. CD Topic: Treeplan. 24. STATISTICAL INFERENCE: CONCLUSION. Introduction. Identifying the Correct Technique: Summary of Statistical Inference. The Last Word. Data File Sample Statistics. Tables. Answers To Selected Even-Numbered Exercises.

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