BSTAT (with Review Cards and Printed Access Card)

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9780538479820: BSTAT (with Review Cards and Printed Access Card)

Created through a "student-tested, faculty-approved" review process with nearly 100 students and faculty, BSTAT is an engaging and accessible solution to accommodate the diverse lifestyles of today's learners. Written by leading statistics expert and best-selling statistics author Gerald Keller, BSTAT emphasizes applications rather than calculations while vividly demonstrating the vital role of statistics for today's business managers. Readers learn the author's hallmark, three-step approach to using statistical tools to solve actual business problems as they learn to identify the correct technique, compute the statistics, and interpret the results within the correct context.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

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.

Review :

1. WHAT IS STATISTICS? Key Statistical Concepts. Large Real Datasets. Statistics and the Computer. 2. GRAPHICAL DESCRIPTIVE TECHNIQUES. Types of Data and Information. Bar and Pie Charts. Histograms and Stem-and-Leaf Displays. Line Charts. Scatter Diagrams. 3. NUMERICAL DESCRIPTIVE TECHNIQUES. Measures of Central Location. Measures of Variability. Measures of Relative Standing and Box Plots. Measures of Linear Relationship. 4. DATA COLLECTION AND SAMPLING. Methods of Collecting Data. Sampling. Sampling Plans. Sampling and Nonsampling Errors. 5. PROBABILITY. Assigning Probability to Events. Joint, Marginal, and Conditional Probability. Probability Rules and Trees. Identifying the Correct Method. 6. RANDOM VARIABLES AND DISCRETE PROBABILITY DISTRIBUTIONS. Random Variables and Probability Distributions. Binomial Distribution. Poisson Distribution. 7. CONTINUOUS PROBABILITY DISTRIBUTIONS. Probability Density Functions. Normal Distribution. Other Continuous Distributions (Student t, Chi-squared, F). 8. SAMPLING DISTRIBUTIONS. Sampling Distribution of the Mean. Sampling Distribution of a Proportion. From Here to Inference. 9. INTRODUCTION TO ESTIMATION. Concepts of Estimation. Estimating the Population Proportion. Selecting the Sample Size to Estimate a Proportion. 10. INTRODUCTION TO HYPOTHESIS TESTING. Concepts of Hypothesis Testing. Testing the Population Proportion. Calculating the Probability of a Type II Error. The Road Ahead. 11. INFERENCE ABOUT ONE POPULATION. Inference about a Population Mean (s Unknown). Inference about a Population Variance. Review of Inference about a Population Proportion. 12. INFERENCE ABOUT TWO POPULATIONS, PART I. Inference about the Difference between Two Means: Independent Samples. Observational and Experimental Data. Inference about the Difference between Two Means: Matched Pairs Experiment. 13. INFERENCE ABOUT TWO POPULATIONS, PART II. Inference about the Ratio of Two Variances. Inference about the Difference between Two Population Proportions. 14. ANALYSIS OF VARIANCE. One Way Analysis of Variance. Multiple Comparisons. Randomized Blocks (Two Way) Analysis of Variance. 15. CHI-SQUARED TESTS. Chi-Squared Goodness-of-Fit Test. Chi-Squared Test of a Contingency Table. 16. MODEL. Simple Linear Regression. Estimating the Coefficients. Error Variable: Required Conditions. Assessing the Model. Using the Regression Equation. Regression Diagnostics - I. 17. MULTIPLE REGRESSION. Model and Required Conditions. Estimating the Coefficients and Assessing the Model Regression Diagnostics - II. Regression Diagnostics- III (Time Series). 18. REVIEW OF STATISTICAL INFERENCE. Appendix A: Data File Sample Statistics. Appendix B: Tables. Appendix C: Answers to Selected Even-Numbered Exercises.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

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Gerald Keller
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