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OpenIntro

Personality Project: An introduction to psychometric theory This page is devoted to teaching others about psychometric theory as well as R. It consists of chapters of an in progress text as well as various short courses on R. The e-book is a work in progress. Chapters will appear sporadically. Parts of it are from the draft of a book being prepared for the Springer series on using R, other parts are just interesting tid-bits that would not be appropriate as chapters. It is written in the hope that I can instill in a new generation of psychologists the love for quantitative methodology imparted to me by reading the popular and then later the scientific texts of Ray Cattell [Cattell, 1966b] and Hans Eysenck [Eysenck, 1964, Eysenck, 1953, Eysenck, 1965]. My course in psychometric theory, on which much of this book is based, was inspired by a course of the same name by Warren Norman. This book would not be possible without the amazing contributions of the R-Core Team and the many contributers to R and the R-Help listserve.

Workload Characterization and Modeling Book by Dror G. Feitelson This is the final version of a book I am working on, entitled Workload Modeling for Computer Systems Performance Evaluation. It will be published by Cambridge University Press, hopefully towards the end of 2014. It will continue to be available here in pdf format also after it is published. Your are welcome to make one copy for your personal use, but further unauthorized distribution is not allowed. ©2014 All rights reserved. Download current version (pdf format, x+591 pages, 235 figures, 18 tables, 51 boxes, 756 references, 15.3 MB) Table of Contents Introduction Workload Data Statistical Distributions Fitting Distributions to Data Heavy Tails Correlations in Workloads Self Similarity and Long-Range Dependence Hierarchical Generative Models Case Studies Summary and Outlook The bibliography is available as a BibTeX file. Links to data sources. Please let me know if you find any typos or other problems (email feit@cs.huji.ac.il).

Networks, Crowds, and Markets: A Book by David Easley and Jon Kleinberg In recent years there has been a growing public fascination with the complex "connectedness" of modern society. This connectedness is found in many incarnations: in the rapid growth of the Internet and the Web, in the ease with which global communication now takes place, and in the ability of news and information as well as epidemics and financial crises to spread around the world with surprising speed and intensity. These are phenomena that involve networks, incentives, and the aggregate behavior of groups of people; they are based on the links that connect us and the ways in which each of our decisions can have subtle consequences for the outcomes of everyone else. Networks, Crowds, and Markets combines different scientific perspectives in its approach to understanding networks and behavior. The book is based on an inter-disciplinary course that we teach at Cornell. You can download a complete pre-publication draft of Networks, Crowds, and Markets here.

Natural Language Processing with Python Natural Language Processing with Python – Analyzing Text with the Natural Language Toolkit Steven Bird, Ewan Klein, and Edward Loper This version of the NLTK book is updated for Python 3 and NLTK 3. The first edition of the book, published by O'Reilly, is available at (There are currently no plans for a second edition of the book.) 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. Bibliography Term Index This book is made available under the terms of the Creative Commons Attribution Noncommercial No-Derivative-Works 3.0 US License.

Modern Computer Arithmetic (book) Modern Computer Arithmetic R. P. Brent, P. Zimmermann, Modern Computer Arithmetic, Cambridge Monographs on Computational and Applied Mathematics (No. 18), Cambridge University Press, November 2010, 236 pages. Publisher's web page. To cite this document, please use the following: Modern Computer Arithmetic, Richard Brent and Paul Zimmermann, Cambridge University Press, 2010. Preliminary versions of the book are available here: Version 0.5.9 (October 2010), xvi+223 pages; this version corresponds quite closely to the version published by Cambridge University Press (the only differences being that pages i-viii slightly differ, and the three typos reported by Torbjrn Granlund on version 0.5.7 are fixed in 0.5.9). Abstract This is a book about algorithms for performing arithmetic, and their implementation on modern computers. Summary Chapter 1 describes integer arithmetic (representation, addition, subtraction, multiplication, division, roots, gcd, base conversion). Exercises Errata Software Reviews

Mining of Massive Datasets maxima Go to Maxima by Example. Computational Physics with R and Maxima Chapter 1, Numerical Differentiation, Quadrature, and Roots A brief introduction to both R and Maxima is followed by examination of built-in and package tools for these three areas, with examples of use, and the design of homemade functions designed to employ the simplest numerical methods for these three areas of numerical work. --cp1.pdf : Ch. 1, Mar. 4, 2014, Maxima 5.28.0, R 3.0.1, 70 pages --cp1.tex : Ch. 1 Latex code file --cp1code.R : R code for Ch. 1, Mar. 4, 2014, R 3.0.1 --cp1code.mac : Maxima code for Ch. 1 , Mar. 4, 2014, Maxima 5.28.0 --cpnewton.mac : Maxima code for Ch. 1, Mar. 4, 2014, Maxima 5.28.0 Chapter 1 Topics Example 1: Semiclassical Quantization of Molecular Vibrations Examples of "bottom-up" programming in both R and Maxima for a physics problem which makes use of both quadrature and root finding. Project 1: Classical Scattering in a Central Potential Maxima by Example What is Maxima?

ManyBooks.net Learning Statistics with R | Computational Cognitive Science Lab In late 2013 I gave a one-day workshop out at CSIRO that aimed to provide a brief introduction to R for an audience who knew statistics but not R. The workshop consisted of two distinct parts, an introduction to the basic mechanics of R, followed by a fairly rapid coverage of a lot of core statistical tools in R. (There's also a bonus "Part 3" that covers a few additional topics that I'm fond of). I also had the presence of mind to record screencasts of my practice talk, so there's about 5 hours of me talking about statistics linked to below! Part 1: Introducing R Getting Started. Part 2: Introductory Statistics in R Descriptive Statistics. Part 3: Extras Additional Statistical Tools.

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