Topics In Statistical Methodology
Download Topics In Statistical Methodology PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Topics In Statistical Methodology book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Topics in Statistical Methodology
The Text Book Covers All Traditional As Well As Newly Emerging Topics In Statistical Methodology. A Broad General Description Of The Book Consists Of(I) A Lucid Presentation To The Motivation Of The Modern Axiomatic Approach To Probability.(Ii) Study Of All Major Distributions (Inclusive Of Circular, Log-Normal Singular) With New Interpretations Ofsome Distributions (Ex. Pareto, Logistic Etc.)(Iii) Model Oriented Approach To The Generations Of Normal, Log-Normal, Cauchy, Exponential, Gamma And Other Waiting Distributions And Their Characterizations.(Iv) Techniques Of Truncated And Censored Distributions Vis-À-Vis Parametric, Non-Parametric, Bayesian And Sequential Inference Procedures, The Backgrounds Of Which Have Been Provided.(V) Inclusion Of Classical Topics As Pearsonian Curves, Gram-Charlier Series And Orthogonal Polynomials.Some Of The Distinguishing Features Are As Follows: * Introducing The Concept Of Correlation As A Milestone In The Development Of Regression Theory. * A Large Number Of Solved Examples And A Wide Collection Of Unsolved Problems With Occasional Hints. * A Geometrical Treatment Of Non-Central X2.
An Introduction to Statistical Learning
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
Statistical Research Methods
Author: Roy Sabo
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-10-22
This textbook will help graduate students in non-statistics disciplines, advanced undergraduate researchers, and research faculty in the health sciences to learn, use and communicate results from many commonly used statistical methods. The material covered, and the manner in which it is presented, describe the entire data analysis process from hypothesis generation to writing the results in a manuscript. Chapters cover, among other topics: one and two-sample proportions, multi-category data, one and two-sample means, analysis of variance, and regression. Throughout the text, the authors explain statistical procedures and concepts using a non-statistical language. This accessible approach is complete with real-world examples and sample write-ups for the Methods and Results sections of scholarly papers. The text also allows for the concurrent use of the programming language R, which is an open-source program created, maintained and updated by the statistical community. R is freely available and easy to download.