Online Statistics Book: A Free Resource for Introductory Statistics Developed by Rice University (Lead Developer), University of Houston Clear Lake, and Tufts University OnlineStatBook Project Home This work is in the public domain. Therefore, it can be copied and reproduced without limitation. However, we would appreciate a citation where possible. If you are an instructor using these materials, I can send you an instructor's manual, PowerPoint Slides, and additional questions that may be helpful to you. Table of Contents Mobile This version uses formatting that works better for mobile devices. Rice Virtual Lab in Statistics This is the original classic with all the simulations and case studies. Version in PDF e-Pub (e-book) Partial support for this work was provided by the National Science Foundation's Division of Undergraduate Education through grants DUE-9751307, DUE-0089435, and DUE-0919818.

LeaRning Path on R - Step by Step Guide to Learn Data Science on R One of the common problems people face in learning R is lack of a structured path. They don’t know, from where to start, how to proceed, which track to choose? Though, there is an overload of good free resources available on the Internet, this could be overwhelming as well as confusing at the same time. To create this R learning path, Analytics Vidhya and DataCamp sat together and selected a comprehensive set of resources to help you learn R from scratch. This learning path is a great introduction for anyone new to data science or R, and if you are a more experienced R user you will be updated on some of the latest advancements. This will help you learn R quickly and efficiently. Step 0: Warming up Before starting your journey, the first question to answer is: Why use R? R is a fast growing open source contestant to commercial software packages like SAS, STATA and SPSS. Watch this 90 seconds video from Revolution Analytics to get an idea of how useful R could be. (Need a GUI? Assignment

Probability and statistics EBook - Socr From Socr SOCR Books: This is a General Statistics Curriculum E-Book, which includes Advanced-Placement (AP) materials. Preface This is an Internet-based probability and statistics E-Book. The materials, tools and demonstrations presented in this E-Book would be very useful for advanced-placement (AP) statistics educational curriculum. There are 4 novel features of this specific Statistics EBook. Format Follow the instructions in this page to expand, revise or improve the materials in this E-Book. Learning and Instructional Usage This section describes the means of traversing, searching, discovering and utilizing the SOCR Statistics EBook resources in both formal and informal learning setting. Copyrights The Probability and Statistics EBook is a freely and openly accessible electronic book developed by SOCR and the general community. Chapter I: Introduction to Statistics The Nature of Data and Variation Uses and Abuses of Statistics Statistics is the science of variation, randomness and chance.

StatPrimer © B. Gerstman 2003 StatPrimer (Version 6.4) B. Burt Gerstman (email) Part A (Introductory) (1) Measurement and sampling [Exercises] (2) Frequency distributions [Exercises] (3) Summary statistics [Exercises] (4) Probability [Exercises Part A] [Exercises Part B] (5) Introduction to estimation [Exercises] (6) Introduction to hypothesis testing [Exercises] (7) Paired samples [Exercises] (8) Comparing Independent means [Exercises] (9) Inference about a proportion [Exercises] (9.5) Comparing two proportion (*.ppt) [Exercises] (10) Cross-tabulated counts [Exercises] Part B (Intermediate) (11) Variances and means [Exercises] (12) ANOVA [Exercises] (13) ANOVA topics (post hoc comparisons, Levene's test, Non-parametric tests) [Exercises] (14) Correlation [Exercises] (15) Regression [Exercises] (16) Risk ratios and prevalence ratios [Exercises] (17) Case-control odds ratios [Exercises] Additional notes Power and sample size [Exercises] How To Know What to Use [Exercises]Approaches Toward Data Analysis Data Files

Big Data, Data Mining, Predictive Analytics, Statistics, StatSoft Electronic Textbook This free ebook has been provided as a public service since 1995. Statistics: Methods and Applications textbook offers training in the understanding and application of statistics and data mining. It covers a wide variety of applications, including laboratory research (biomedical, agricultural, etc.), business statistics, credit scoring, forecasting, social science statistics and survey research, data mining, engineering and quality control applications, and many others. The Textbook begins with an overview of the relevant elementary (pivotal) concepts and continues with a more in depth exploration of specific areas of statistics, organized by "modules", representing classes of analytic techniques. You have filtered out all documents.

Complimentary Exam Copies Please read the terms and conditions carefully before completing the form. To ensure that your request is processed quickly, please complete the required fields in the form below. eInspections – Save Time & The Environment Alternatively we can now offer many of our titles for inspection online. If you would like to inspect our books via this innovative, student-friendly format, simply select this option in the following form, give your full course details and, upon authorization, you will then be able to view the book from any PC with Internet access. Terms and Conditions Selected textbooks are available as a complimentary exam copy to qualified lecturers for consideration as a course textbook.

Mass Shooting Tracker Free Statistics Book Writing Better Statistical Programs in R A while back a friend asked me for advice about speeding up some R code that they’d written. Because they were running an extensive Monte Carlo simulation of a model they’d been developing, the poor performance of their code had become an impediment to their work. After I looked through their code, it was clear that the performance hurdles they were stumbling upon could be overcome by adopting a few best practices for statistical programming. This post tries to describe some of the simplest best practices for statistical programming in R. Following these principles should make it easier for you to write statistical programs that are both highly performant and correct. Write Out a DAG Whenever you’re running a simulation study, you should appreciate the fact that you are working with a probabilistic model. Almost certainly the most important concept in probabilistic modeling when you want to write efficient code is the notion of conditional independence. Let’s go through an example. Speed

Estadística para todos 10 Stats Terms Explained in “Plain English” (#10: Standard Deviation) Four years is a reasonable amount of time between blog posts, right? Help me decide what to blog about. What topics do you want to hear about? Read More Statistical Soup: ANOVA, ANCOVA, MANOVA, & MANCOVA The distinctions between ANOVA, ANCOVA, MANOVA, and MANCOVA can be difficult to keep straight. Difference Between Within-Subject and Between-Subject Effects: The Answer to Ice-Cream is Always Yes Within-person (or within-subject) effects represent the variability of a particular value for individuals in a sample. How to make SPSS produce all tables in APA format automatically! Formatting a graph that was exported from SPSS to Microsoft Word can be an absolute pain. Introduction to R Programming The following is one of the best introductions to R programming that I've found online. Literate Statistical Programming with knitr - Creating Reproducible Analysis in R How to Plot Interaction Effects in SPSS Using Predicted Values Using the lavaan package (in R) for latent variable modeling (SEM)

Griffith Feeney Consulting ModernDive Getting Started - For Students This book was written using the bookdown R package from Yihui Xie (Xie 2016). In order to follow along and run the code in this book on your own, you’ll need to have access to R and RStudio. You can find more information on both of these with a simple Google search for “R” and for “RStudio.” We will keep a running list of R packages you will need to have installed to complete the analysis as well here in the needed_pkgs character vector. You can run the library function on them to load them into your current analysis. Colophon The source of the book is available here and was built with versions of R packages (and their dependent packages) given below.

Bayes' Theorem An Intuitive Explanation of Bayes' Theorem Bayes' Theorem for the curious and bewildered; an excruciatingly gentle introduction. Your friends and colleagues are talking about something called "Bayes' Theorem" or "Bayes' Rule", or something called Bayesian reasoning. They sound really enthusiastic about it, too, so you google and find a webpage about Bayes' Theorem and... It's this equation. So you came here. Why does a mathematical concept generate this strange enthusiasm in its students? Soon you will know. While there are a few existing online explanations of Bayes' Theorem, my experience with trying to introduce people to Bayesian reasoning is that the existing online explanations are too abstract. Or so they claim. And let's begin. Here's a story problem about a situation that doctors often encounter: What do you think the answer is? Next, suppose I told you that most doctors get the same wrong answer on this problem - usually, only around 15% of doctors get it right. No, it does not!

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