Practical Business Statistics

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. Michael R. Wagner is an Associate Professor of Operations Management and a Neal and Jan Dempsey Endowed Faculty Fellow at the Michael G. Foster School of Business, University of Washington, Seattle. His Ph.D. is in Operations Research from MIT (2006). He has taught data analytics at both undergraduate and graduate levels, and earned the Ron Crockett Award for Innovation in Education (2014). His articles have appeared in many publications, including the journals Management Science, Operations Research, Manufacturing & Service Operations Management, Mathematics of Operations Research, Mathematical Programming, IIE Transactions, European Journal of Operational Research, Operations Research Letters, Networks, Computers & Operations Research, and Transportation Science.

• Part I: Introduction and Descriptive Statistics1. Introduction: Defining the Role of Statistics in Business2. Data Structures: Classifying the Various Types of Data Sets3. Histograms: Looking at the Distribution of Data4. Landmark Summaries: Interpreting Typical Values and Percentiles5. Variability: Dealing with DiversityPart II: Probability6. Probability: Understanding Random Situations7. Random Variables: Working with Uncertain NumbersPart III: Statistical Inference8. Random Sampling: Planning Ahead for Data Gathering9. Confidence Intervals: Admitting That Estimates Are Not Exact10. Hypothesis Testing: Deciding Between Reality and CoincidencePart IV: Regression and Time Series11. Correlation and Regression: Measuring and Predicting Relationships12. Multiple Regression: Predicting One Variable From Several Others13. Report Writing: Communicating the Results of a Multiple Regression14. Time Series: Understanding Changes Over TimePart V: Methods and Applications15. ANOVA: Testing for Differences Among Many Samples and Much More16. Recent Developments17. Chi-Squared Analysis: Testing for Patterns in Qualitative Data18. Quality Control: Recognizing and Managing Variation19. Statistical (Machine) Learning: Using Complex Models With Large Data Sets