Primer for Data Analytics and Graduate Study in Statistics

ISBN-13: 9783030474812 / Angielski / Miękka / 2021 / 244 str.

Douglas Wolfe;Grant Schneider

Opis

This book is specially designed to refresh and elevate the level of understanding of the foundational background in probability and distributional theory required to be successful in a graduate-level statistics program. Advanced undergraduate students and introductory graduate students from a variety of quantitative backgrounds will benefit from the transitional bridge that this volume offers, from a more generalized study of undergraduate mathematics and statistics to the career-focused, applied education at the graduate level. In particular, it focuses on growing fields that will be of potential interest to future M.S. and Ph.D. students, as well as advanced undergraduates heading directly into the workplace: data analytics, statistics and biostatistics, and related areas.

Recenzje

"This text looks to bridge the gap between a general undergraduate education in a mathematically oriented field and a career-oriented graduate study in Statistics. In my opinion, the authors achieve this goal. ... this text is a quality candidate for a primary book for an undergraduate course in Probability theory or a graduate course that introduces the basics of Statistical theory." (Grant Innerst, MAA Reviews, July 18, 2021)

"The text provides the necessary framework for an undergraduate course for anyone who is interested in either exploring job opportunities in the data analytics field or in attending a graduate program in statistics or biostatistics." (Bülent Karasözen, zbMATH 1454.62004, 2021)

Spis treści

- Introduction. - Basic Probability. - Random Variables and Probability Distributions. - General Properties of Random Variables. - Joint Probability Distributions for Two Random Variables. - Probability Distribution of a Function of a Single Random Variable. - Sampling Distributions. - Asymptotic (Large-Sample) Properties of Statistics.

O autorze

Douglas A. Wolfe is Chair and Professor Emeritus in the Department of Statistics at Ohio State University. His recent publications include Intuitive Introductory Statistics (Springer 2017) and Nonparametric Statistical Methods, 3rd Edition (2014). He is an elected fellow of the American Statistical Association and of the Institute of Mathematical Statistics.

Grant Schneider is the Head of Upstart Columbus and a Data Scientist at Upstart, the leading AI lending platform. Before returning to Ohio in 2019, he spent the previous five years working on Upstart’s Data Science team in Silicon Valley building AI models used to extend credit to borrowers left behind by traditional models. Dr. Schneider obtained his Bachelors, Masters, and Ph.D. degrees from The Ohio State University and is the co-author of Intuitive Introductory Statistics (Springer 2017). He has also authored the NSM3 and IIS R packages.