Andrew F. Siegel, Michael R. Wagner

Practical Business Statistics (eBook, ePUB)

• Format: ePub

Produktbeschreibung

Practical Business Statistics, Eighth Edition, offers readers a practical, accessible approach to managerial statistics that carefully maintains, but does not overemphasize mathematical correctness. The book fosters deep understanding of both how to learn from data and how to deal with uncertainty, while promoting the use of practical computer applications. This trusted resource teaches present and future managers how to use and understand statistics without an overdose of technical detail, enabling them to better understand the concepts at hand and to interpret results.

The text uses excellent examples with real world data relating to business sector functional areas such as finance, accounting, and marketing. Written in an engaging style, this timely revision is class-tested and designed to help students gain a solid understanding of fundamental statistical principles without bogging them down with excess mathematical details.

• Provides users with a conceptual, realistic, and matter-of-fact approach to managerial statistics
• Offers an accessible approach to teach present and future managers how to use and understand statistics without an overdose of technical detail, enabling them to better understand concepts and to interpret results
• Features updated examples and images to illustrate important applied uses and current business trends
• Includes robust ancillary instructional materials such as an instructor's manual, lecture slides, and data files

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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 560
• Erscheinungstermin: 3. November 2021
• Englisch
• ISBN-13: 9780128200261
• Artikelnr.: 62929068

Autorenporträt

Andrew F. Siegel holds the Grant I. Butterbaugh Professorship in Quantitative Methods and Finance at the Michael G. Foster School of Business, University of Washington, Seattle, and is also Adjunct Professor in the Department of Statistics. His Ph.D. is in statistics from Stanford University (1977). Before settling in Seattle, he held teaching and/ or research positions at Harvard University, the University of Wisconsin, the RAND Corporation, the Smithsonian Institution, and Princeton University. He has taught statistics at both undergraduate and graduate levels, and earned seven teaching awards in 2015 and 2016. The interest-rate model he developed with Charles Nelson (the Nelson-Siegel Model) is in use at central banks around the world. His work has been translated into Chinese and Russian. His articles have appeared in many publications, including the Journal of the American Statistical Association, the Encyclopedia of Statistical Sciences, the American Statistician, Proceedings of the National Academy of Sciences, Nature, the American Mathematical Monthly, the Journal of the Royal Statistical Society, the Annals of Statistics, the Annals of Probability, the Society for Industrial and Applied Mathematics Journal on Scientific and Statistical Computing, Statistics in Medicine, Biometrika, Biometrics, Statistical Applications in Genetics and Molecular Biology, Mathematical Finance, Contemporary Accounting Research, the Journal of Finance, and the Journal of Applied Probability.

Inhaltsangabe

Part I: Introduction and Descriptive Statistics 1. Introduction: Defining
the Role of Statistics in Business 2. Data Structures: Classifying the
Various Types of Data Sets 3. Histograms: Looking at the Distribution of
Data 4. Landmark Summaries: Interpreting Typical Values and Percentiles 5.
Variability: Dealing with Diversity
Part II: Probability 6. Probability: Understanding Random Situations 7.
Random Variables: Working with Uncertain Numbers
Part III: Statistical Inference 8. Random Sampling: Planning Ahead for Data
Gathering 9. Confidence Intervals: Admitting That Estimates Are Not Exact
10. Hypothesis Testing: Deciding Between Reality and Coincidence
Part IV: Regression and Time Series 11. Correlation and Regression:
Measuring and Predicting Relationships 12. Multiple Regression: Predicting
One Variable From Several Others 13. Report Writing: Communicating the
Results of a Multiple Regression 14. Time Series: Understanding Changes
Over Time
Part V: Methods and Applications 15. ANOVA: Testing for Differences Among
Many Samples and Much More 16. Recent Developments 17. Chi-Squared
Analysis: Testing for Patterns in Qualitative Data 18. Quality Control:
Recognizing and Managing Variation
19. Statistical (Machine) Learning: Using Complex Models With Large Data
Sets