Statistic: A Concise Mathematical Introduction for Students and Scientists offers a one academic term text that prepares the student to broaden their skills in statistics, probability and inference, prior to selecting their follow-on courses in their chosen fields, whether it be engineering, computer science, programming, data sciences, business or economics. The book places focus early on continuous measurements, as well as discrete random variables. By invoking simple and intuitive models and geometric probability, discrete and continuous experiments and probabilities are discussed throughout the book in a natural way. Classical probability, random variables, and inference are discussed, as well as material on understanding data and topics of special interest. Topics discussed include: • Classical equally likely outcomes • Variety of models of discrete and continuous probability laws • Likelihood function and ratio • Inference • Bayesian statistics With the growth in the volume of data generated in many disciplines that is enabling the growth in data science, companies now demand statistically literate scientists and this textbook is the answer, suited for undergraduates studying science or engineering, be it computer science, economics, life sciences, environmental, business, amongst many others. Basic knowledge of bivariate calculus, R language, Matematica and JMP is useful, however there is an accompanying website including sample R and Mathematica code to help instructors and students.
This text assumes students have been exposed to intermediate algebra, and it focuses on the applications of statistical knowledge rather than the theory behind it.
This highly-regarded text serves as a quick reference book which offers clear, concise instructions on how and when to use the most popular nonparametric procedures.
Now even more indispensable in our data-driven world than it was when first published, How to Lie with Statistics is the book that generations of readers have relied on to keep from being fooled.
Convergence rates of posterior distributions. The Annals of Statistics 28 500–531. GILKs, W. R., RICHARDSON, S. and SPIEGELHALTER, D. J. (1998). Markov Chain Monte Carlo in Practice. Chapman & Hall. GRIMMETT, G. and STIRZAKER, D.
As best-selling author Charles Wheelan shows us in Naked Statistics, the right data and a few well-chosen statistical tools can help us answer these questions and more. For those who slept through Stats 101, this book is a lifesaver.
Optimal asymptotic results have been obtained and special behaviour of estimators and predictors in con tinuous time has been pointed out. This book is devoted to these questions.
In The Art of Statistics, David Spiegelhalter guides the reader through the essential principles we need in order to derive knowledge from data.
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This resource features interactive demonstrations and simulations, case studies, and an analysis lab.This print edition of the public domain textbook gives the student an opportunity to own a physical copy to help enhance their educational ...
This book presents a detailed account of recently developed approaches, including the Lasso and versions of it for various models, boosting methods, undirected graphical modeling, and procedures controlling false positive selections.