Synopsis:
Traditional statistical inference is integrated with the more modern idea of data analysis in this introductory text for non-technical students. Material begins with simple data sets and proceeds to those with more structure. Examples are plentiful and have been chosen from diverse fields, making the subject accessible to students of any academic field. The book contains many pictures as well as detailed calculations with step-by-step instructions and a formula to indicate in mathematical notation exactly what is being done. At the end of each chapter is a brief summary which reviews the material and explains key terms. Following this are questions which help readers review main new concepts and ideas, and practice problems (many with real data sets). This text requires limited background in mathematics.
About the Author:
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.
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