Statistics Inferences

Statistical Causal Inferences and Their Applications in Public Health Research  eBooks & eLearning

Posted by Underaglassmoon at Oct. 31, 2016
Statistical Causal Inferences and Their Applications in Public Health Research

Statistical Causal Inferences and Their Applications in Public Health Research
Springer | Statistics | November 17, 2016 | ISBN-10: 3319412574 | 321 pages | pdf | 3.47 mb

Editors: He, Hua, Wu, Pan, Chen, Ding-Geng (Din) (Eds.)
Includes software and data sets so readers may replicate analyses
Contains much needed coverage of recent developments in causal inference
Begins with an introduction to the concept of potential outcomes as applicable to causal inference concepts, models, and assumptions
Statistical Methods in Molecular Evolution (Statistics for Biology and Health) by Rasmus Nielsen

Statistical Methods in Molecular Evolution (Statistics for Biology and Health) by Rasmus Nielsen
English | Apr. 21, 2005 | ISBN: 0387223339 | 503 Pages | PDF | 9 MB

In the field of molecular evolution, inferences about past evolutionary events are made using molecular data from currently living species. With the availability of genomic data from multiple related species, molecular evolution has become one of the most active and fastest growing fields of study…
Statistics in Science: The Foundations of Statistical Methods in Biology, Physics and Economics by R. Cooke

Statistics in Science: The Foundations of Statistical Methods in Biology, Physics and Economics (Boston Studies in the Philosophy and History of Science) by R. Cooke
English | 1990 | ISBN: 9401067651 | 192 Pages | PDF | 4 MB

An inference may be defined as a passage of thought according to some method. In the theory of knowledge it is customary to distinguish deductive and non-deductive inferences. Deductive inferences are truth preserving, that is, the truth of the premises is preserved in the con­ clusion.
Basic Concepts in Statistics and Epidemiology by Allyson Pollock

Basic Concepts in Statistics and Epidemiology by Allyson Pollock
English | June 2007 | ISBN: 1846191246 | 225 Pages | PDF | 1 MB

This book contains a Foreword by Allyson Pollock, Professor and Head, Centre for International Public Health Policy, University of Edinburgh. Healthcare students, practitioners and researchers need a sound basis for making valid statistical inferences from health data.
A Modern Approach to Regression with R (Springer Texts in Statistics)

A Modern Approach to Regression with R (Springer Texts in Statistics) by Simon Sheather
Springer; 2009 edition | March 11, 2009 | English | ISBN: 0387096078, 0387096086 | 393 pages | PDF | 8 MB

This book focuses on tools and techniques for building regression models using real-world data and assessing their validity. A key theme throughout the book is that it makes sense to base inferences or conclusions only on valid models.
Item Response Theory (Understanding Statistics: Measurement) (repost)

Item Response Theory (Understanding Statistics: Measurement) by Christine DeMars
English | 2010 | ISBN: 0195377036, 0199703841 | 144 pages | PDF | 2,6 MB

This is a title in our Understanding Statistics series, which is designed to provide researchers with authoritative guides to understanding, presenting and critiquing analyses and associated inferences.

Robust Statistics  

Posted by thomas009 at May 4, 2009
Robust Statistics

Peter J. Huber "Robust Statistics"
Wiley-Interscience | 1981-02 | ISBN: 0471418056 | Pages: 320 | PDF | 2.43 MB

Other volumes in the Wiley Series in Probability and Mathematical Statistics Abstract Inference UIf Grenander The traditional setting of statistical inference is when both sample space and parameter space are finite dimensional Euclidean spaces or subjects of such spaces. During the last decades, however, a theory has been developed that allows the sample space to be some abstract space. More recently, mathematical techniques—especially the method of sieves—have been constructed to enable inferences to be made in abstract parameter spaces. This work began with the author's 1950 monograph on inference in stochastic processes (for general sample space) and with the sieve methodology (for general parameter space) that the author and his co-workers at Brown University developed in the 1970s.

Introduction to Bayesian Statistics  eBooks & eLearning

Posted by Sonora at Nov. 22, 2006
Introduction to Bayesian Statistics

William M. Bolstad
Introduction to Bayesian Statistics
Wiley-Interscience | ISBN: 0471270202 | 2004 | 376 pages | PDF | 3.1 MB

There is a strong upsurge in the use of Bayesian methods in applied statistical analysis, yet most introductory statistics texts only present frequentist methods. In Bayesian statistics the rules of probability are used to make inferences about the parameter. Prior information about the parameter and sample information from the data are combined using Bayes theorem. Bayesian statistics has many important advantages that students should learn about if they are going into fields where statistics will be used. This book uniquely covers the topics usually found in a typical introductory statistics book but from a Bayesian perspective.

L. Kazmier - Schaum's Easy Outline of Business Statistics: Crash Course [Repost]  eBooks & eLearning

Posted by rotten comics at Dec. 2, 2016
L. Kazmier - Schaum's Easy Outline of Business Statistics: Crash Course [Repost]

L. Kazmier - Schaum's Easy Outline of Business Statistics: Crash Course
2003 | ISBN: 0071398767 | English | 144 pages | PDF | 1.3 MB

Statistics and Analysis of Scientific Data, Second Edition  eBooks & eLearning

Posted by Underaglassmoon at Dec. 1, 2016
Statistics and Analysis of Scientific Data, Second Edition

Statistics and Analysis of Scientific Data, Second Edition
Springer | Graduate Texts in Physics | Nov 9 2016 | ISBN-10: 1493965700 | 318 pages | pdf | 5.61 mb

Authors: Bonamente, Massimiliano
Introduces the statistical techniques most commonly employed in physical sciences and engineering
Makes clear distinction between material that is strictly mathematical and theoretical, and practical applications of statistical methods
Expanded to cover selected core statistical methods used in business science