### MULTIVARIATE STATISTICS OLD SCHOOL MATHEMATICAL AND METHODOLOGICAL INTRODUCTION TO MULTIVARIATE STATISTICAL ANALYTICS INCLUDING LINEAR MODELS MACHINE LEARNING AND BIG DATA STUDY WITH R

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## Multivariate Statistics Old School

Author : John I. Marden
ISBN : 1456538837
Genre :
File Size : 42.26 MB
Format : PDF, Docs

Multivariate Statistics: Old School is amathematical and methodological introduction to multivariate statistical analysis. It presents the basic mathematical grounding that graduate statistics students need for future research, andimportant multivariate techniques useful to statisticians in general. The material provides support forfurther study in big data and machine learning. Topics include The multivariate normal and Wishart distributions Linear models, including multivariate regression and analysis of variance, andboth-sides models (GMANOVA, repeated measures, growth curves) Linear algebra useful for multivariate statistics Covariance structures, including principal components, factor analysis, independence and conditional independence, and symmetry models Classification (linear and quadratic discrimination, trees, logistic regression) Clustering (K-means, model-based, hierarchical) Other techniques, including biplots, canonical correlations, and multidimensional scaling Most of the analyses in the book use the statistical computing environment R, for which there is an available package (msos)of multivariate routines and data sets. This text was developed over many years by the author, John Marden, while teaching in the Department of Statistics, University of Illinoisat Urbana-Champaign.
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## Mathematical Statistics

Author : John I. Marden
ISBN : 1542439604
Genre :
File Size : 50.58 MB
Format : PDF, Mobi

Mathematical Statistics: Old School covers three main areas: The mathematics needed as a basisfor work in statistics; the mathematical methods for carrying out statistical inference; and the theoretical approaches for analyzing the efficacy of various procedures. The author, John Marden,developed this material over the last thirty years teaching various configurations of mathematical statisticsand decision theory courses. It is intended as a graduate-level text. Topics include distribution theory, asymptotic convergence, frequentist and Bayesian inference (estimation, hypothesis testing, confidence intervals, model selection), exponential families, linear regression, likelihood methods, bootstrap and randomization methods, and statistical decision theory.
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## The R Book

Author : Michael J. Crawley
ISBN : 9781118448960
Genre : Mathematics
File Size : 62.86 MB
Format : PDF

Hugely successful and popular text presenting an extensive and comprehensive guide for all R users The R language is recognized as one of the most powerful and flexible statistical software packages, enabling users to apply many statistical techniques that would be impossible without such software to help implement such large data sets. R has become an essential tool for understanding and carrying out research. This edition: Features full colour text and extensive graphics throughout. Introduces a clear structure with numbered section headings to help readers locate information more efficiently. Looks at the evolution of R over the past five years. Features a new chapter on Bayesian Analysis and Meta-Analysis. Presents a fully revised and updated bibliography and reference section. Is supported by an accompanying website allowing examples from the text to be run by the user. Praise for the first edition: ‘…if you are an R user or wannabe R user, this text is the one that should be on your shelf. The breadth of topics covered is unsurpassed when it comes to texts on data analysis in R.’ (The American Statistician, August 2008) ‘The High-level software language of R is setting standards in quantitative analysis. And now anybody can get to grips with it thanks to The R Book…’ (Professional Pensions, July 2007)
Category: Mathematics

## An Introduction To Statistical Learning

Author : Gareth James
ISBN : 9781461471387
Genre : Mathematics
File Size : 59.73 MB
Format : PDF, Kindle

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 to marketing to 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, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
Category: Mathematics

## Harness Oil And Gas Big Data With Analytics

Author : Keith Holdaway
ISBN : 9781118910894
File Size : 41.31 MB
Format : PDF, Docs

Use big data analytics to efficiently drive oil and gas exploration and production Harness Oil and Gas Big Data with Analytics provides a complete view of big data and analytics techniques as they are applied to the oil and gas industry. Including a compendium of specific case studies, the book underscores the acute need for optimization in the oil and gas exploration and production stages and shows how data analytics can provide such optimization. This spans exploration, development, production and rejuvenation of oil and gas assets. The book serves as a guide for fully leveraging data, statistical, and quantitative analysis, exploratory and predictive modeling, and fact-based management to drive decision making in oil and gas operations. This comprehensive resource delves into the three major issues that face the oil and gas industry during the exploration and production stages: Data management, including storing massive quantities of data in a manner conducive to analysis and effectively retrieving, backing up, and purging data Quantification of uncertainty, including a look at the statistical and data analytics methods for making predictions and determining the certainty of those predictions Risk assessment, including predictive analysis of the likelihood that known risks are realized and how to properly deal with unknown risks Covering the major issues facing the oil and gas industry in the exploration and production stages, Harness Big Data with Analytics reveals how to model big data to realize efficiencies and business benefits.

## Multivariate Analysis Of Ecological Data

Author : Michael Greenacre
ISBN : 9788492937509
Genre : Ecology
File Size : 22.43 MB
Format : PDF, Mobi

Category: Ecology

## Data Analysis And Graphics Using R

Author : John Maindonald
ISBN : 9781139486675
Genre : Computers
File Size : 65.24 MB
Format : PDF, Mobi

Discover what you can do with R! Introducing the R system, covering standard regression methods, then tackling more advanced topics, this book guides users through the practical, powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display, and interpretation of data. The many worked examples, from real-world research, are accompanied by commentary on what is done and why. The companion website has code and datasets, allowing readers to reproduce all analyses, along with solutions to selected exercises and updates. Assuming basic statistical knowledge and some experience with data analysis (but not R), the book is ideal for research scientists, final-year undergraduate or graduate-level students of applied statistics, and practising statisticians. It is both for learning and for reference. This third edition expands upon topics such as Bayesian inference for regression, errors in variables, generalized linear mixed models, and random forests.
Category: Computers

## Machine Learning Algorithms

Author : Giuseppe Bonaccorso
ISBN : 9781785884511
Genre : Computers
File Size : 54.32 MB
Format : PDF, ePub

Category: Computers

## Modern Multivariate Statistical Techniques

Author : Alan J. Izenman
ISBN : 0387781897
Genre : Mathematics
File Size : 42.84 MB
Format : PDF, Kindle

This is the first book on multivariate analysis to look at large data sets which describes the state of the art in analyzing such data. Material such as database management systems is included that has never appeared in statistics books before.
Category: Mathematics

## Linear Models With R Second Edition

Author : Julian J. Faraway
ISBN : 9781439887332
Genre : Mathematics
File Size : 36.92 MB
Format : PDF, ePub, Mobi