# Statistics

# The theory of distributions

## by Peter K. Dunn

An introduction to mathematical statistics, and the theory of distributions. […] This book is an introduction to the theory of statistical probability and distributions. This book can be read without relying on any specific statistical software, though sometimes R code1 is included to demonstrate ideas, and to discuss simulation. The callouts used in this book have meanings; for example: These chunks introduce the objectives for the chapters of the book. These chunks highlight common mistakes or warnings, about a particular concept or about using a formula. These chunks offer helpful … Read more →

# Introductory predictive analytics and machine learning in education and healthcare

## by Anshul Kumar

This textbook accompanies the course HE-930 in the PhD in HPEd program at MGH Institute of Health Professions. This book introduces students to basic predictive analytics and machine learning, with examples and applications related to education and healthcare. […] This textbook accompanies the course HE-930—Statistics/Predictive Analytics for Health Professions Education—in the PhD in HPEd program at MGH Institute of Health Professions. HE-930 is a data analytics course that introduces students to basic predictive analytics (PA) and machine learning (ML), with examples and applications … Read more →

# Statistics and Probability for Economics and Finance - 2022/23

## by Michela Cameletti

Notes for the R labs of the SPEF 22/23 course @UniBg […] You are reading the lecture notes of the R labs for the Statistics and Probability for Economics and Finance (SPEF) course at University of Bergamo (academic year 2022/23) taught by Prof. Raffaele Argiento, Prof. Michela Cameletti and Prof. Tommaso Lando. R is a great programming language especially designed for statistical analysis and data visualisation. The SPEF R labs are designed for those who don’t have any programming background. It will be a step-by-step path; at the end you will have the basic R knowledge for analysing … Read more →

# Introduction to Data Science

## by Hansjörg Neth

This book provides a gentle introduction to data science for students of any discipline with little or no background in data analysis or computer programming. Based on notions of representation, measurement, and modeling, we examine key data types (e.g., logicals, numbers, text) and learn to clean, summarize, transform, and visualize (rectangular) data. By reflecting on the relations between representations, tasks, and tools, the course promotes data literacy and cultivates reproducible research practices that precede and enable practical uses of programming or statistics. This book is still being written and revised. It currently serves as a scaffold for a curriculum that will be filled with content as we go along. Read more →

# Data Science for Psychologists

## by Hansjörg Neth

This book provides an introduction to data science that is tailored to the needs of students in psychology, but is also suitable for students of the humanities and other biological or social sciences. This audience typically has some knowledge of statistics, but rarely an idea how data is prepared for statistical testing. By using various data types and working with many examples, we teach strategies and tools for reshaping, summarizing, and visualizing data. By keeping our eyes open for the perils of misleading representations, the book fosters fundamental skills of data literacy and cultivates reproducible research practices that enable and precede any practical use of statistics. Read more →

# Introduction to Statistics

## by Lauren Cappiello

Introduction to Statistics. […] There are a lot of ways to approach an introductory statistics class. Historically, the topics found in this text have been taught in a way that emphasizes hand calculations and the use of tables full of numbers. My philosophy is a little different. This class is designed for students who will need to read statistical results and may need to produce basic statistics using a computer. If you go on to be a scientist and need more statistical know how, this course will give you enough background knowledge to take the inevitable next course in statistics. There … Read more →

# Regression Modelling for Biostatistics 1

## by Schlub T, Heritier S, Teixeira-Pinto A

Schlub T, Heritier S, Teixeira-Pinto A The following chapters include notes, videos, R and Stata code, required readings, and exercises for the BCA unit RM1 (Regression Modelling for Biostatistics 1). These pages were generated with Quarto https://quarto.org/. On the left menu you have the topics that correspond roughly to the weekly modules. On the right side you should see the subtopics in the current topic. Make sure that you have access to Regression Methods in Biostatistics book by Vittinghoff et al. You should be able to obtain a digital copy of the book from the library of your … Read more →

# Introduction to Econometrics with R

## by Christoph Hanck, Martin Arnold, Alexander Gerber, and Martin Schmelzer

Beginners with little background in statistics and econometrics often have a hard time understanding the benefits of having programming skills for learning and applying Econometrics. ‘Introduction to Econometrics with R’ is an interactive companion to the well-received textbook ‘Introduction to Econometrics’ by James H. Stock and Mark W. Watson (2015). It gives a gentle introduction to the essentials of R programming and guides students in implementing the empirical applications presented throughout the textbook using the newly aquired skills. This is supported by interactive programming exercises generated with DataCamp Light and integration of interactive visualizations of central concepts which are based on the flexible JavaScript library D3.js. Read more →

# Quantitative Methods Using R

## by Subash Parajuli

This book covers practical worked out examples which you can easily apply to your data set and also includes a discussion on how the example is working. We will cover descriptive and basic inferential statistics, including graphs, frequency distributions, central tendency, dispersion, probability, hypothesis testing, tests of mean differences, correlation, simple regression, and chi-square tests. This book is designed to facilitate graduate students of Educational Psychology to develop their knowledge and understanding of various statistical concepts and procedures in R programming as a … Read more →

# A First Course on Statistical Inference

## by Isabel Molina Peralta and Eduardo García-Portugués

Notes for Statistical Inference. MSc in Statistics for Data Science. Carlos III University of Madrid. [...] Welcome to the notes for Statistical Inference. The course is part of the MSc in Statistics for Data Science from Carlos III University of Madrid. The course is designed to have, roughly, one session per each main topic in the syllabus. The schedule is tight due to time constraints, which will inevitably make the exposition of certain methods somehow superficial. Nevertheless, the course and exercises will hopefully give you a respectable panoramic view of the fundamentals of ... Read more →

# STA 253 Notes (Murray State)

## by Christopher Mecklin

This are notes for STA 253 at Murray State University for students in Dr. Christopher Mecklin’s class. […] These notes are meant to supplement, not replace your textbook. I will occasionally cover topics not in your textbook, and I will stress those topics I feel are most important. My definition of statistics: Statistics is the attempt to use qualitative and quantitative data in order to: When we collect data, we will often organize or structure the data into a table (called a data matrix in your book and often referred to as a data frame) where the rows represent cases or units, which are … Read more →

# Notes for Nonparametric Statistics

## by Eduardo García-Portugués

Notes for Nonparametric Statistics. MSc in Statistics for Data Science. Carlos III University of Madrid. [...] Welcome to the notes for Nonparametric Statistics. The course is part of the MSc in Statistics for Data Science from Carlos III University of Madrid. The course is designed to have, roughly, one session per each main topic in the syllabus. The schedule is tight due to time constraints, which will inevitably make the treatment of certain methods somehow superficial. Nevertheless, the course will hopefully give you a respectable panoramic view of different available topics on ... Read more →

# Responsible applied statistics in R for behavioral and health data (working title)

## by Anshul Kumar

This textbook accompanies the course HE-902 in the PhD in HPEd program at MGHIHP (http://mghihp.edu/phdhped). HE-902 is a statistics course that equips students to analyze healthcare and/or behavioral data in R. […] Welcome to HE-902! Please watch the following welcome video: The video above can also be viewed externally at https://youtu.be/DmWQ51aX-ao. Please additionally read the following items: This textbook accompanies the course HE-902—Advanced Statistical Modeling for Health Professions Education—in the PhD in HPEd program at MGH Institute of Health Professions. HE-902 is a … Read more →

# Surrogates

## by Robert B. Gramacy

Surrogates: a new graduate level textbook on topics lying at the interface between machine learning, spatial statistics, computer simulation, meta-modeling (i.e., emulation), and design of experiments. Gaussian process emphasis facilitates flexible nonparametric and nonlinear modeling, with applications to uncertainty quantification, sensitivity analysis, calibration of computer models to field data, sequential design and (blackbox) optimization under uncertainty. Presentation targets numerically competent scientists in the engineering, physical, and biological sciences. Treatment includes historical perspective and canonical examples, but primarily concentrates on modern statistical methods, computation and implementation in R at modern scale. Rmarkdown facilitates a fully reproducible tour complete with motivation from, application to, and illustration with, compelling real-data examples. Read more →

# STA 135 Notes (Murray State)

## by Christopher Mecklin

This are notes for STA 135 at Murray State University for students in Dr. Christopher Mecklin’s class. […] These notes are meant to supplement, not replace your textbook. I will occasionally cover topics not in your textbook, and I will stress those topics I feel are most important. My definition of statistics: Statistics is the attempt to use qualitative and quantitative data in order to: When we collect data, we will often organize the data into a table where the rows represent cases or units, which are called subjects or respondents when they are humans, and the columns represent … Read more →

# Introduction to Regression Methods for Public Health Using R

## by Ramzi W. Nahhas

An introduction to regression methods using R with examples from public health datasets and accessible to students without a background in mathematical statistics. […] This is a SECOND DRAFT but is still awaiting peer review. The goal is publication as a printed version (through CRC Press) with the online version remaining freely available. Status If you have any comments or suggestions, feel free to contact me at ramzi.nahhas@wright.edu. Thank you! This text is suitable as a second biostatistics course for Master of Public Health students or public health professionals. Almost all public … Read more →

# Introductory Statistics for Economics

## by Brian Krauth

A textbook for an introductory (first-year or second-year undergraduate) course in statistics for economics majors. […] As its name suggests, Introductory Statistics for Economics is a textbook intended for use in an introductory (first or second year) statistics course for economics majors. It was written for use as a textbook for ECON 233, the introductory statistics course for economics majors at Simon Fraser University. The content is similar to most other introductory statistics courses for business and economics students, but with a few important differences. When I was assigned to … Read more →

# Business Analytics: Metoder og anvendelser

## by Udarbejdet af Mads Stenbo Nielsen

Dansksprogede noter, der opsummerer væsentlige dele af pensum i HD Business Intelligence. […] Nærværende notesæt er udarbejdet som et dansksproget supplement til fagets lærebog: Agresti, Franklin og Klingenberg: “Statistics: The Art and Science of Learning from Data,” Pearson, 5. udgave … Read more →

# Introductory statistics skills pack

## by Glenna Nightingale and Michael Allerhand

This book provides basic material for students seeking to learn statistics in an R environment, […] This skills pack introduces statistical concepts to beginners within the framework of R. Examples of analyses and R code are provided as well. Dr. Glenna Nightingale PhD Statistics research scientist (public health, epidemiology, spatial ecology, computational social science). Dr. Mike Allerhand, PhD Statistics -since 2009 Statistician at the Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh. Since 2018 Statistical Consultant in the Statistical Consultancy Unit … Read more →

# STM1001 Topic 1: Introduction to statistics and presenting data

## by Amanda Shaker

STM1001 Topic 1 […] Where are we headed in this subject? In this subject, we will be learning how to Make Sense of Data. One of the most important tools we can use to do so is Statistics. What is Statistics? Statistics allows us to make sense of data. It involves collecting, describing, and analysing data, and sometimes drawing conclusions from data. In a nutshell, the above definition describes exactly what we will be doing throughout this subject. We will be learning about how to collect data. Once we have a data set, how can we then make sense of it? It is always a good idea to begin by … Read more →

# STM1001: Introduction to Machine Learning in R

STM1001 Machine Learning (Data Science Stream) […] Welcome to the final content supplement for the Data Science stream of STM1001. Throughout the semester, as we cover different aspects of statistics and data science, supplementary documents such as this one will be used to enhance your learning experience. This document contains material to support your learning as you complete Computer Labs 9B, 10B and 11B of the Data Science stream. We recommend that you take a few minutes to browse the different sections in this document before Computer Lab 9B. We suggest that you aim to read through … Read more →

# STM1001 Topic 2 - Descriptive Statistics

## by Amanda Shaker

STM1001 Topic 2 […] Having learnt how to present data in the last topic, in this topic our focus will be on describing data, or, descriptive statistics. We will be learning how to describe data using both numerical and graphical measures. Throughout this topic’s content, we will be considering some examples based on free material from Gapminder.org (Gapminder.org … Read more →

# Statistics Taster Session

## by Prof Peter Neal

Lecture Notes for Statistics Taster Session […] Welcome to the free Statistics Taster … Read more →

# Advanced Statistics I 2021 Edition

The official textbook of PSY 207 for the Fall 2021 Semester. […] This book is a compilation of the readings developed for the Fall 2021 Semester offering of Psychology 207: Advanced Statistics. Please don’t try to sell this book because there are about a million copyright violations in it. … Read more →

# Statistics 240 Course Notes

## by Bret Larget

This book contains case studies and course notes for STAT 240, Introduction to Data Modeling, at the University of Wisconsin, including instruction for many tidyverse packages […] Statistics 240 is a first course in data science and statistical modeling at the University of Wisconsin - Madison. The course aims to enable you, the student in the course, to gain insight into real-world problems from messy data using methods of data science. These notes chart an initial path for you to gain the knowledge and skills needed to become a data scientist. The structure of the course is to present a series … Read more →

# Design and Analysis of Experiments and Observational Studies using R

## by Nathan Taback

Online version of Design and Analysis of Experiments and Observational Studies using R […] This is the free website for Design and Analysis of Experiments and Observational Studies using R. A hardcopy of the book can be purchased from Routledge. This book grew out of course notes for a twelve-week course (one term) on the Design of Experiments and Observational Studies in the Department of Statistical Sciences at the University of Toronto. Students are senior undergraduates and applied Masters students who have completed courses in probability, mathematical statistics, and regression … Read more →

# A Guide on Data Analysis

## by Mike Nguyen

This is a guide on how to conduct data analysis in the field of data science, statistics, or machine learning. […] More books by the author can be found … Read more →

# ECON 381: Statistics and Probability for Econometrics

## by Pierangelo DePace and Augusto Gonzalez Bonorino

Textbook for ECON 381: Statistics and Probability for Econometrics […] Here the introduction to the class and … Read more →

# Geostatystyka w R

## by Jakub Nowosad

Introduction to geostatistics with R (in Polish). Skrypt ma na celu wprowadzenie do analiz przestrzennych (GIS) używająć języka programowania R, a następnie zastosowanie uzyskanej wiedzy do wykonania estymacji (interpolacji) oraz symulacji geostatystycznych. […] Masz przed sobą skrypt zawierający materiały do ćwiczeń z geostatystyki. Składa się ona z kilkunastu rozdziałów pokazujących jak: wygląda geostatystyczna analiza danych (rozdział 1), dodawać i wizualizować dane przestrzenne w R (rozdział 2), wykonywać wstępną eksplorację danych nieprzestrzennych (rozdział 3), wstępnie analizować dane … Read more →

# STM1001: Foundational Biology for Analyses of Biological Data

STM1001 Biology (Science/Health/Data Science Streams) […] Welcome to another content supplement for the Science, Health and Data Science streams of STM1001. Throughout the semester, as we cover different aspects of statistics and data science, supplementary documents such as this one will be used to enhance your learning experience. This document contains material to support your learning as you complete Computer Lab 8B of the Science, Health or Data Science streams. We recommend that you take a few minutes to browse the different sections in this document (just click on the sections in the … Read more →

# Prediction and Feature Assessment

## by Nicolas Städler

Script for Analysis of High-Dimensional Data […] Prediction and Feature Assessment This script was written for the course on Analysis of High-Dimensional Data held at the University of Bern and the ETH Zurich. Much of the content is based on the book from Hastie, Tibshirani, and Friedman (2001). The course has a focus on applications using R (R Core Team 2022). All data sets used throughout the script can be downloaded from github. What are high-dimensional data and what is high-dimensional statistics? The Statistics Department of the University of California, Berkeley summarizes it as … Read more →

# A primer for biostatistics in R

## by cjlortie

A brief introduction to statistical thinking in biostatistics supported by the R programming language. […] Welcome to a primer for biostatistics in R. Mathematical! Adventure time! Well, the mathematical part is up to you, but this is an adventure. This set of learning materials is a guide developed to support you in better developing critical thinking using statistics. Critical thinking very generally is a mode of thinking that is self-directed and evidence based (Facionie 2017). Statistical thinking is thus an ideal opportunity and partner in honing literacy adventure skills in this … Read more →

# Statistics 1 - exercises

## by Błażej Kochański

Statistics 1 - exercises 2022 […] This book is being prepared for the students of Statistics course at Gdańsk University of Technology. Dear students. For the purposes of our class I am testing bookdown (http://bookdown.org). We will see how it … Read more →

# Modern R with the tidyverse

## by Bruno Rodrigues

This book will teach you how to use R to solve your statistical, data science and machine learning problems. Importing data, computing descriptive statistics, running regressions (or more complex machine learning models) and generating reports are some of the topics covered. No previous experience with R is needed. […] I have been working on this on and off for the past 4 years or so. In 2022, I have updated the contents of the book to reflect updates introduced with R 4.1 and in several packages (especially those from the {tidyverse}). I have also cut some content that I think is not that … Read more →

# STAT101 Tutorials

## by speedyjiang

STAT101 Tutorials […] Weekly tutorial exercises (starting in Week 1) are assigned from the required course textbook, Lock, Lock, Lock Morgan, Lock and Lock, Statistics: Unlocking the Power of Data, 2nd Edition, (2017) Wiley. You can find details on where to source the textbook in the Course Information section. You do not need to get the textbook exercises finished or correct during the tutorial, we just want you to give them your best attempt. However, mastering these concepts is strongly recommended in preparation for the final exam. Your attempts at the textbook exercises will not be … Read more →

# Machine Learning for Biostatistics

## by Armando Teixeira-Pinto & Jaroslaw Harezlak

Machine Learning for Biostatistics […] So far, most of the methods that we have seen (with the exception of KNN) assume an additive effect of the predictors. We will now study non-parametric methods to estimate (f(\mathbf x)). By the end of this module you should be able to: The dataset triceps is available in the MultiKink package. You may install.packages(“MultiKink”), load the library (library(MultiKink)) and then run data(“triceps”). The data are derived from an anthropometric study of 892 females under 50 years in three Gambian villages in West Africa. There are 892 observations on the … Read more →

# Foundations of Statistics

## by Prof Peter Neal and Dr Daniel Cavey

Lecture Notes for Foundations of Statistics […] In this course the fundamental principles and techniques underlying modern statistical and data analysis will be introduced. The course will cover the core foundations of statistical theory consisting of: The course highlights the importance of computers, and in particular, statistical packages, in performing modern statistical analysis. Students will be introduced to the statistical package R as a statistical and programming tool and will gain experience in interpreting and communicating its output. Learning Outcomes A student who completes … Read more →

# Machine Learning for Biostatistics

## by Armando Teixeira-Pinto

Machine Learning for Biostatistics […] In this module we will talk about model selection and regularisation methods (also called penalisation methods), namely, ridge and lasso. We will start with classical algorithms for model selection, such as the best subset selection and stepwise (backward and forward) selection. Then we introduce the idea of bias-variance trade-off and the motivation for ridge regression. Finally, we will talk about Lasso regression and some of its extensions. By the end of this module you should be able to: The dataset fat is available in the library(faraway). You have to … Read more →

# GSB 518 Handouts

## by Kevin Ross

Kevin Ross This is a selection of Handouts for Cal Poly GSB 518, Essential Statistics for Business Analytics, covering topics in Probability and … Read more →

# Machine Learning for Biostatistics

## by Jaroslaw Harezlak & Armando Teixeira-Pinto

Machine Learning for Biostatistics […] This module will cover methods to explore non-linear effects of numerical predictors on the outcome. By the end of this module you should be able to: The dataset triceps is available in the MultiKink package. You may install.packages(“MultiKink”), load the library (library(MultiKink)) and then run data(“triceps”). The data are derived from an anthropometric study of 892 females under 50 years in three Gambian villages in West Africa. There are 892 observations on the following 3 variables: The data SA_heart.csv is retrospective sample of males in a … Read more →

# BASIC STATISTICS

## by Nannan Wang

BASIC STATISTICS […] … Read more →

# 10 Fundamental Theorems for Econometrics

## by Thomas S. Robinson (

This book walks through the ten most important statistical theorems as highlighted by Jeffrey Wooldridge, presenting intuiitions, proofs, and applications. […] A list of 10 econometric theorems was circulated on Twitter citing what Jeffrey Wooldridge claims you need to apply repeatedly in order to do econometrics. As a political scientist with applied statistics training, this list caught my attention because it contains many of the theorems I see used in (methods) papers, but which I typically glaze over for lack of understanding. The complete list (slightly paraphrased) is: As an exercise … Read more →

# Computer Intensive Statistics: APTS 2021–22 Supporting Notes

## by Paul A. Jenkins & Richard Everitt

Computer Intensive Statistics: APTS 2021–22 Supporting Notes […] These notes were intended to supplement the Computer Intensive Statistics lectures and laboratory sessions rather than to replace or directly accompany them. As such, material is presented here in an order which is logical for reference purposes after the week and not precisely the order in which it was to be discussed during the week. There is much more information in these notes concerning some topics than would have been covered during the week itself. One of their main functions is to provide pointers to the relevant … Read more →

# ISTA 321 - Data Mining

## by Nicholas DiRienzo

Course content for ISTA 321 - Last updated for Summer 2022 […] Welcome to ISTA 321 - Data Mining! The goal of this class is to teach you how to use R to make informed inferences and predictions from large datasets using a variety of methods. This requires a mixture of many skills including programming, data exploration and visualizations, statistics, algorithms, machine learning, model validation, and general data wrangling. We don’t do these things in isolation, but instead do them with a goal of answering a question, thus being able to apply this knowledge to make a data-driven decision … Read more →

# Useful Terms in Statistics

## by Nannan Wang

Useful Terms in Statistics […] This book is created as a final project advised by Dr. Olmanson for TEAC-889. TLTE 889: Working with a faculty mentor on either an individual or small-group basis, the student plans, conducts, and reports a summative work … Read more →

# Advanced Statistical Computing

## by Roger D. Peng

The book covers material taught in the Johns Hopkins Biostatistics Advanced Statistical Computing course. I taught this course off and on from 2003–2016 to upper level PhD students in Biostatistics. The course ran for 8 weeks each year, which is a fairly compressed schedule for material of this nature. Because of the short time frame, I felt the need to present material in a manner that assumed that students would often be using others’ software to implement these algorithms but that they would need to know what was going on underneath. In particular, should something go wrong with one of … Read more →

# Modern Statistical Methods for Psychology

## by Mine Çetinkaya-Rundel and Johanna Hardin, tuned by Gregory Cox

This is the website for Modern Statistical Methods for Psychology, a modified version of Introduction to Modern Statistics, First Edition by Mine Çetinkaya-Rundel and Johanna Hardin, as modified by Gregory Cox. The original Introduction to Modern Statistics is a textbook from the OpenIntro project. — Version date of this modification: May 24, 2022. The original version of the Introduction to Modern Statistics textbook and its supplements, including slides, labs, and interactive tutorials, may be downloaded for free atopenintro.org/book/ims. This textbook is itself a derivative of OpenIntro … Read more →

# Quick R for Statistics

## by Chia-Ching Wu

A book created with bookdown. […] R語言是免費的軟體，是進行統計分析很好的工具。 … Read more →

# The Data Files

## by Peter K. Dunn and Margaret Marshman

Supporting information for The Data Files. […] These web pages are designed to help teachers with teaching statistics in schools. These pages are based on a series of short articles (called The Data Files) published in the Australian Mathematics Education Journal, by Dr Peter K. Dunn and Dr Margaret … Read more →

# R you Ready for R?

## by Wade Roberts, Colorado College

This e-book offers generic scripts for conducting core statistical analyses. They should be considered a starting point, not an end point, in your exploration of R. […] This e-book offers generic scripts for conducting core statistical analyses, from calculating statistics to producing tables or graphing results. These scripts should be considered a starting point, not an end point, in your exploration of R. The following chapters … Read more →

# 20IMCAL204 STATISTICS LAB- Laboratory Manual

## by Department of Mathematics

This manual is generated using Bookdown for internal use only […] This course is designed as a Computational Statistics Laboratory (CSL) comprised of 29 experiments selected from the Statistical Courses in INMCA Programme. Details of experiments and the instructions regarding creation & submission of laboratory reports are explained in this introductory chapter. Familiarization of environments in R. Perform simple arithmetics using R. Perform basic R functions. Use various graphical techniques in EDA. Create different charts for visualization of given set of data. Draw a Pareto chart to … Read more →

# jamoviguiden

## by Jonas Rafi

Lär dig göra independent samples t-test, paired samples t-test, one sample t-test, ANOVA, repeated measures ANOVA, factorial ANOVA, mixed ANOVA, linear regression, och logistic regression i jamovi. jamoviguiden innehåller även avsnitt om csv-filer och skalnivåer. […] Syftet med jamoviguiden är att tillhandahålla snabbstartsguider över vanliga procedurer i jamovi. För dig som söker en grundläggade introduktion till både statistik och jamovi rekommenderar jag gratisboken Learning statistics with jamovi av Danielle J. Navarro och David R. Foxcroft. jonas.rafi psychology.su.se. Detta verk … Read more →

# Biostatistics for Clinical Research

## by Sean Collins

This is an open educational resource (OER) book for Biostatistics for Clinical Research: Theory & Applications in R using the bookdown package in RStudio. The output format for this example is bookdown::gitbook. […] This version has been written in Scrivener, exported as plain text as a single index.Rmd file and then used to build the book. If this worked, then it’s an entirely new workflow for writing and self publishing books that takes advantage of the organizing and writing features of Scrivener, and the computational analysis and publishing features of R, Rmarkdown and Bookdown. At … Read more →

# Primer on Mathematical Statistics

## by Peter K. Dunn

A primer of mathematical statistics, before reading generalized linear models […] This book is a primer on basic mathematical statistics and matrices. This book was originally prepared for students about to undertake MTH301 Reading in Advanced Mathematics at USC, and reading Generalized Linear Models with Examples in R (by Dunn & … Read more →

# Statistik Vorlesung

## by Lisa Lechner

Dies sind Begleitnotizten für die Vorlesung Statistik. […] “Statistics is the grammar of science” (Karl Pearson) Ziel der Vorlesung Statistik ist, die Einführung in die zentralen Grundlagen und Begriffe der deskriptiven (Datenmatrix, Häufigkeitsverteilungen, Lagemaße, Streuungsmaße, Verteilungskenngrößen, Zusammenhang zwischen Variablen) und der induktiven Statistik zu geben. Gegenstand der induktiven Statistik ist es, durch geeignete Verfahren von der Stichprobe auf die Grundgesamtheit zu schließen und die Sicherheit der Schlussfolgerung abzuschätzen, d.h. Wahrscheinlichkeiten für die … Read more →

# An Introduction to Bayesian Reasoning and Methods

## by Kevin Ross

This textbook presents an introduction to Bayesian reasoning and methods […] Statistics is the science of learning from data. Statistics involves We will assume some familiarity with many of these aspects, and we will focus on the items in italics. That is, we will focus on statistical inference, the process of using data analysis to draw conclusions about a population or process beyond the existing data. “Traditional” hypothesis tests and confidence intervals that you are familiar with are components of “frequestist” statistics. This book will introduce aspects of “Bayesian” statistics. We … Read more →

# Introduction to Computational Finance and Financial Econometrics with R

## by Eric Zivot

Add description […] Outline of preface (preliminary and incomplete). June 21, 2016. I started teaching the course Introduction to Financial Econometrics at UW in 1998. Motivation was to teach more statistics and quantitative methods to economics majors. I found that combining statistics topics with finance applications was very effective and popular. Early classes used Microsoft Excel as the main software tool (R was not around then). Experience with Excel was, and still is, in high demand by employers in the finance industry. However, Excel is not a good tool for doing statistics. In early … Read more →

# Lab notes for Statistics for Social Sciences II: Multivariate Techniques

## by Eduardo García-Portugués

Lab notes for Statistics for Social Sciences II: Multivariate Techniques […] Welcome to the lab notes for Statistics for Social Sciences II: Multivariate Techniques. Along these notes we will see how to effectively implement the statistical methods presented in the lectures. The exposition we will follow is based on learning by analyzing datasets and real-case studies, always with the help of statistical software. While doing so, we will illustrate the key insights of some multivariate techniques and the adequate use of advanced statistical software. Be advised that these notes are neither … Read more →

# Probability

## by Michael Foley

Notes cobbled together from books, online classes, etc. to be used as quick reference for common work projects. […] These are notes from books, classes, tutorials, vignettes, etc. They contain mistakes, are poorly organized, and are sloppy on fundamentals. They should improve over time, but that’s all I can say for it. Use at your own risk. The focus of this handbook is probability, including random variables and probability distributions. Not included here: statistics, machine learning, text mining, survey analysis, or survival analysis. These subjects frequently arise at work, but are … Read more →

# STM1001: Introduction to Bioinformatics in R

STM1001 Bioinformatics (Science/Health Science/Data Science Modules) […] Welcome to another content supplement for the STM1001 Science, Health Science and Data Science modules. Throughout the semester, as we cover different aspects of statistics and data science, supplementary documents such as this one will be used to enhance your learning experience. This document contains material to support your learning as you complete Computer Labs 7B and 8B of the Science, Health Science or Data Science modules. We recommend that you take a few minutes to browse the different sections in this … Read more →

# Geostatistics Final Summary

## by Yan Ren

This is the final report summary of spatial statistics analysis. January 31, 2022. […] Spatial data is considered as a random process ({Z(s),s\in D}) in this part. Set coalash dataset as an example (Figure 1.1). (D) is the region with values. and (s) indicates percent of coalash in this location. Many kinds of exploratory statistics can be applied here to test stationarity, local stationarity and so on. The key idea in this chapter is to model the above random process ({Z(s),s\in D}) with values on known locations. Then inference of unknown locations can be made. Variogram is … Read more →

# Probability and Statistics for Business and Finance - 2021/22

## by Michela Cameletti and Raffaele Argiento

Notes for the R labs of the PSBF course @Unibg […] You are reading the lecture notes of the R labs for the Probability and Statistics for Business and Finance (PBSF) course at University of Bergamo (academic year 2021/22). R is a great programming language especially designed for statistical analysis and data visualisation. The PSBF R labs are designed for those who don’t have any programming background. It will be a step-by-step path; at the end you will have the basic R knowledge for analysing financial time series. Enjoy the journey! In the following lecture notes, this font (with grey … Read more →

# Practical Data Skills

## by Introduction To Data Science

Practical Data Skills […] The purpose of this book is to provide practical data science skills to managers and business analysts. The focus is helping the reader develop pragmatic skills they can apply within their organizations to extract value from data. This book will not provide a complete and rigorous overview of data science, statistics, or computer programming, but it will help the reader quickly learn how to process and analyze data in the R programming language. The book assumes nothing more than a high school level background in mathematics - it requires no prior knowledge of … Read more →

# Statistics for Data Science Notes

## by Andrew Sage - Stat 255: Lawrence University

This is a minimal example of using the bookdown package to write a book. The HTML output format for this example is bookdown::gitbook, set in the _output.yml file. [...] We consider a dataset with prices (in $ US) and other information on 53,940 round cut diamonds. The first 6 rows are shown below. The dataset incudes both: What do we notice about the relationship between price and cut? Is this surprising? Next, we examine a histogram, displaying price, cut, and carat size. How does the information in this plot help explain the surprising result we saw in the boxplot? Next, we use a ... Read more →

# Survey data in the field of economy and finance

## by Guillaume Osier

This is the full book written in preparation for my course on “Survey data in the field of economy and finance” given at the University of Luxembourg (Master 2 ‘Economy and Finance’). […] Counting a population through censuses has been a long established practice which dates back to thousand years BC. Babylonians, Egyptians, Romans etc. used to resort to population censuses to support important economic decisions in terms of taxation, labour force scaling, food distribution etc. Censuses are usually regarded as error-free data sources leading to statistics with highest accuracy. On the … Read more →

# Statistics for Data Science R Code Guide

## by Andrew Sage - Stat 255, Lawrence University

This is a minimal example of using the bookdown package to write a book. The HTML output format for this example is bookdown::gitbook, set in the _output.yml file. […] This guide provides details and examples on using R to perform the kinds of statistical analyses that we’ll use in STAT 255. You may use it as a template, as you write code for your assignments. If you want to work with R from your own computer, you can install it for free using the directions below. This will allow you to work on your assignments whenever and wherever you would like. Mac: Windows: The following chapters walk … Read more →

# Statistics with R - Practical Sessions

## by By Seda Erdem | University of Stirling

This is the companion book for University of Stirling Statisitcs with R module […] Welcome to the Practical Sessions of the “Statistics with R.” This space will provide you with sufficient information to practise concepts we have learnt in lectures and prepare you for your assessed R sessions. There will be exercises and challenge questions for you to code, and opportunities for you to elaborate on the results of these activities. After these warm-up exercises, please go back to your lecture platform and review the assessed R-practical tests. Let’s get … Read more →

# Statistics for Data Science R Code Guide

## by Andrew Sage - Stat 255, Lawrence University

This is a minimal example of using the bookdown package to write a book. The HTML output format for this example is bookdown::gitbook, set in the _output.yml file. [...] This guide provides details and examples on using R to perform the kinds of statistical analyses that we’ll use in STAT 255. You may use it as a template, as you write code for your assignments. If you want to work with R from your own computer, you can install it for free using the directions below. This will allow you to work on your assignments whenever and wherever you would like. Mac: Windows: The following ... Read more →

# Catálogo de datos

## by DCR Infosel

En este bookdown se hace un registro de los datos a los cuáles se tiene acceso y documentación […] En proceso En proceso This API offers real-time quotes for equities trading on U.S. and international exchanges. In addition to last price and stock quote (bid/ask) data, the API also provides intraday tick data, volume and time weighted average prices and other market statistics including open, high, low, close, opening/closing auction prices and other data for active equities, depository receipts and ETFs. Haz el request aquí This API offers real time stock quotes for securities trading on … Read more →

# PSY317L & PSY120R Guidebook

## by James P. Curley & Tyler M. Milewski

PSY317L & PSY120R Guidebook […] This book is written to help students enrolled in the University of Texas at Austin Introduction to Statistics for the Behavioral Sciences (PSY317L) course or R Programming for Behavioral Sciences (PSY120R) led by Professor James Curley. We hope that the book will be a useful resource to help you learn both R and statistics. If you have any suggestions for improvements, please get in touch with Professor Curley. This is in between a textbook and a study guide. We’re trying to build materials that will enable students to quickly find what they’re looking for … Read more →

# ADVANCED REGRESSION AND PREDICTION: MACHINE LEARNING TOOLS

## by Ilán F. Carretero Juchnowicz

This is a bookdown in which the second part of the project of the subject advanced regression and prediction of the Master’s Degree in Statistics for Data Science has been carried out […] Currently Machine Learning (ML) techniques are applied in an infinity of fields to obtain knowledge from data. Among these fields today we can highlight the appearance and effect of the coronavirus disease (COVID-19) in all aspects of society. That is why, by completing this second part of the advanced regression and prediction course, it is intended to use the techniques learned during the practical and … Read more →

# Business Statistics

## by Josip Arnerić & Anita Čeh Časni ©jarneric@net.efzg.hr, aceh@net.efzg.hr

Business Statistics […] The course purpose is to introduce a formal framework for analyzing real life business problems with actual data, so that students can improve their understanding of the circumstances in which statistical techniques should be used and how to apply statistics to practical business situations. The entire course is supported with many case studies and worked-out examples. In particular, statistical techniques are grouped in sections covering applications in the field of decision making, business forecasting, quality control, and commonly used descriptive and inferential … Read more →

# Introduction to R

## by Jena University Hospital, Institute of Medical Statistics, Computer and Data Sciences, Julia Palm (julia.palm@med.uni-jena.de)

Accompanying IMSID course […] This instruction manual belongs to the course Introduction to R which is taught at the Institute for Medical Statistics, Computer and Data Sciences at Jena University hospital. Each chapter belongs to one of the five course dates. It is written in a way that should allow you to reproduce the entire course by yourself on your personal computer. There are a lot of code examples in this instruction manual. You can generally recognize a piece of R code in this document by the grey highlighting. If the code returns a result, the result is displayed directly below … Read more →

# (Mostly Clinical) Epidemiology with R

## by James Brophy

This is an intermediate epidemiology book that focuses on clinical epidmeiology and its quantification using R. It stems from my belief that the learning of epidmeiologic principles is consolidated through hands on coding examples. […] James (Jay) Brophy is a full professor with a joint appointment in the Departments of Medicine and Epidemiology and Biostatistics at McGill University where he works as a clinical cardiologist at the McGill University Health Center and does research in cardiovascular epidemiology. His research interests are eclectic and include clinical and outcomes research, … Read more →

# Simulation and Modelling to Understand Change

## by Manuele Leonelli

These are lecture notes for the module Simulation and Modelling to Understand Change given in the School of Human Sciences and Technology at IE University, Madrid, Spain. The module is given in the 2nd semester of the 1st year of the bachelor in Data and Business Analytics. Knowledge of basic elements of R programming as well as probability and statistics is assumed. […] These are lecture notes for the module Simulation and Modelling to Understand Change given in the School of Human Sciences and Technology at IE University, Madrid, Spain. The module is given in the 2nd semester of the 1st … Read more →

# How does R fit a linear model?

## by Matthew Whalen

What happens when you run lm(y ~ x1 + x2) […] This book is the product of my goal to answer the question ‘How does R fit a linear model using the lm function?’ A seemingly simple question based on one of the most humble formulas in all of statistics led me down a rabbit hole of math and programming I never expected. The goal of writing this is for my own etification, but also to provide a thorough walkthrough so that R-users at all levels of math and programming can have a better grasp on one of the most ubiquitous tools in a statisticians toolbelt. The book wil be structured largely … Read more →

# STA 444/5 - Introductory Data Science using R

## by Derek L. Sonderegger

STA 444/5 - Introductory Data Science using R […] This book is intended to provide students with a resource for learning R while using it during an introductory statistics course. The Introduction section covers common issues that students in a typical statistics course will encounter and provides a simple examples and does not attempt to be exhaustive. The Deeper Details section addresses issues that commonly arise in many data wrangling situations and is intended to give students a deep enough understanding of R that they will be able to use it as their primary computing resource … Read more →

# MGHIHP HE-802, Spring 2021

## by Anshul Kumar

This e-book accompanies the course HE-802 in the MS in HPEd program at MGHIHP (http://mghihp.edu/mshped). HE-802 is a statistics course that equips students to analyze healthcare and/or behavioral data in R. […] This online e-book is the main resource to guide you through the course HE-802 in the MS in HPEd program at MGHIHP in the Spring 2021 semester. Each chapter contains reading (or links to reading) that you should do as well as an assignment that you should complete and submit by the deadline in the course calendar. My name is Anshul Kumar and I am the author/preparer of this e-book. … Read more →

# Introduction to Statistics with R

## by content

Introduction to Statistics with R […] Let’s start with a simple question: In the US, are adult men taller, on average, than women? We know the answer to this question already (yes), but attempting to answer it with data will allow us to illustrate many important concepts of statistics. The first step is to collect some … Read more →

# Efficient R programming

## by Colin Gillespie, Robin Lovelace

Efficient R Programming is about increasing the amount of work you can do with R in a given amount of time. It’s about both computational and programmer efficiency. […] This is the online version of the O’Reilly book: Efficient R programming. Pull requests and general comments are welcome. Get a hard copy from: Amazon (UK), Amazon (USA), O’Reilly Colin Gillespie is Senior Lecturer (Associate Professor) at Newcastle University, UK. He is an Executive Editor of the R Journal, with research interests including high performance statistical computing and Bayesian statistics. Colin founded the … Read more →

# Conceitos e análises estatísticas com R e JASP

## by Luis Anunciação (PUC-Rio), PhD

This book was written for undergraduate level students on Quantitative Methods at the Pontifical Catholic University of Rio de Janeiro (PUC-Rio). The primary goal of this book is to provide a short and to-the-point exposition on the essentials of statistics. To a lesser degree, the mathematical modeling of statistical questions will be addressed. I expect this book can also help students who enroll for laboratory-based statistics and anyone who wants to learn R. Read more →

# Data Science for Human-Centered Product Design

## by Travis Kassab

Data Science for Human-Centered Product Design […] This is a data science tutorial with seven open-source projects that show how statistics and machine learning can be applied to user survey data. The purpose is not to prescribe techniques, but to demonstrate the use of data science in the context of product design. I’ve compiled what I know on the topic, and hope readers adopt some of these techniques and use them in concert with qualitative research and entrepreneurial thinking to build better products. Let’s quickly preview the seven different use-cases. First, we must develop an … Read more →

# Notes for Basic Statistics I

## by Joao M. Souto-Maior, PhD student at NYU

Notes for Basic Statistics I […] These are my notes for the Basic Stats I course at NYU (taught by Prof. Hassad). They will guide our lab sessions. The notes provide: Questions Note: Short-cut to the attendance file, … Read more →

# Cracking The First Year Exam

## by 红领巾

UCSC Ph.D. in Statistics First Year Class Practice Problems. […] People feel anxious about unknown things. In exams, that anxiety may diminish ones abaility of solving problems. When I was in my first year, I cannot stop thinking about what would the First Year Exam looks like, and that curiosity reached its peak at the night just before the exam. It was a long night. After the exam took place, when I traced back the whole preparing process for FYE, it reminded me a famous book for Chinese college entrance exam, Five Years’ Real Exams, Three Years’ Practice Problems (五年高考，三年模拟). … Read more →

# Introduction to Statistical Methodology, Second Edition

## by Derek L. Sonderegger & Robert Buscaglia

Introduction to Statistical Methodology, Second Edition […] The problem with most introductory statistics courses is that they don’t prepare the student for the use of advanced statistics. Rote hand calculation is easy to test, easy to grade, and easy for students to learn to do, but is useless for actually understanding how to apply statistics. Since students pursuing a Ph.D. will likely be using statistics for the rest of their professional careers, we feel that this sort of course should attempt to steer away from a “cookbook” undergraduate pedagogy, and give the student enough … Read more →

# 1014SCG Statistics - Lecture Notes

## by James McBroom

These are the complete set of lecture notes in online bookform for the course 1014SCG Statistics at Griffith University, 2020. […] ©Griffith University 2019. Subject to the provisions of the Copyright Act, no part of this publication may be reproduced in any form or by any means (whether mechanical, electronic, microcopying, photocopying, recording, or otherwise), stored in a retrieval system or transmitted without prior … Read more →

# Introduction to Data Science

## by Ron Sarafian

Class notes for the BGU course - Introduction to Data Science. […] This book accompanies the course I give at Ben-Gurion University, named “Introduction to Data Science”. This is an introductory-level, hands-on focused course, designed for students with basic background in statistics and econometrics, and without programming experience. It introduces students to different tools needed for building a data science pipeline, including data processing, analysis, visualization and modeling. The course is taught in R environment. Many of the contents in this book are taken from BGU’s “R” course, … Read more →

# R Software Handbook

## by Evaluation, Statistics, and Methodology - University of Tennessee, Knoxville

This is a handbook to help UTK Evaluation, Statistics, and Methodology students learn important R skills. […] Welcome to the R Handbook for ESM Students. This handbook is a hands-on guide to help you learn R. It will take you from installation and set up, to data cleaning, analysis, visualization, and reporting. This guide uses real data to help you practice with R. Specifically, it uses survey data from the RStudio Learning R Survey. It also includes data from built-in R data sets and simulated data. R is a free, open-source programming language for statistics and data visualization. It is … Read more →

# R Practices for Learning Statistics

## by Logan Kelly, Ph.D.

This is a set of demonstrations of basic statistical operations in R. It is intended to used in statistics classes taught at the University of Wisconsin-River Falls. … Read more →

# Applied Spatio-temporal Statistics

## by Trevor Hefley

Course notes for Applied Spatio-temporal Statistics (STAT 764) at Kansas State University […] This document contains the course notes for Applied Spatio-temporal Statistics at Kansas State University (STAT 764). During the semester we will cover construction and analysis of spatial, time series, and spatio-temporal data sets. Topics include data generation using geographic information systems, exploratory data analysis and visualization, and descriptive and dynamic spatio-temporal statistical … Read more →

# PSY317L Guidebook

## by James P. Curley & Tyler M. Milewski

PSY317L Guidebook […] This books is still in progress !!! This is a draft. Several sections are still incomplete or unedited. This book is written to help students enrolled in the University of Texas at Austin Introduction to Statistics for the Behavioral Sciences (PSY317L) course led by Professor James Curley. We hope that the book will be a useful resource to help you learn both R and statistics. If you have any suggestions for improvements, please get in touch with Professor Curley. This is in between a textbook and a study guide. We’re trying to build materials that will enable students … Read more →

# Companion to BER 642: Advanced Regression Methods

## by Cheng Hua, Dr. Youn-Jeng Choi, Qingzhou Shi

This is a companion book for students taking the BER 642: Advanced Regression Method at the University of Alabama, Fall 2020 […] This book is still in progress !!! This is a draft. Several sections are still incomplete or unedited. This book is written to help students enrolled in the University of Alabama, Advanced Regression Method (BER 642) course led by Professor Dr.Youn-Jeng (Joy) Choi. We hope that the book will be a useful resource to help you learn both R and statistics. If you have any questions concerning your homework in R, please contact your TA: Qingzhou, at: … Read more →

# Basic R Guide for NSC Statistics

## by Deanna Li

This is an R guide for statistics course at NSC. […] This guide’s primary focus is on Basic R. When graphics are involved, command functions in both Basic R and a package called ggplot2 will be shown. Graph enhancements will be kept to a minimum. Although there are R packages that may do the same or better job than Basic R, this tutorial will not delve into those packages. Exploring other packages will be left for the student to look into, if the student so wishes. Datasets will be taken mostly from those built into R. Since this is mainly a tutorial on the R commands necessary to do … Read more →

# Inferential Statistics and Complex Surveys

## by Cristóbal Moya

ZU course, Fall Semester 2020 […] Welcome to Inferential Statistics and Complex Surveys.This is a course about making inferences with surveys. What does it mean to make an inference? The simplest way to put it is saying that we will use things we know (data) to learn about things we do not know (parameters). This course aims: The objective of these materials is not to replace the readings, but to provide a more concise and, especially, applied summary of the course contents. Part I is about getting the tools ready for the course (R and RStudio) and learning their basics. Part II presents a … Read more →

# RMarkdown for Scientists

## by Nicholas Tierney

A book created for a 3 hour workshop on rmarkdown […] This is a book on rmarkdown, aimed for scientists. It was initially developed as a 3 hour workshop, but is now developed into a resource that will grow and change over time as a living book. This book aims to teach the following: There are many great books on R Markdown and it’s various features, such as “Rmarkdown: The definitive guide”, “bookdown: Authoring Books and Technical Documents with R Markdown”, and “Dynamic Documents with R and knitr, Second edition”, and Yihui Xie’s thesis, “Dynamic Graphics and Reporting for Statistics”. So … Read more →

# Applied Statistics

## by Prof. Dr. Carsten Sauer

Applied Statistics […] WELCOME … Read more →

# Exercises for ‘Introduction to The New Statistics’

## by Peter Baumgartner

This website is a companion book for Introduction to the New Statistics (abbreviated itns). It offers interactive exercises developed mostly in H5P but also with learnr and shiny. It also contains an R tutorial for the end-of-chapter exercises of itns. […] This website is an (inoffical) companion book for Introduction to the New Statistics (abbreviated itns). It offers GitHub resources of this book can be found in two places: I have built the interactive exercises in this book with H5P.org. H5P stands for HTML5 Package, a free and open-source content collaboration framework based on … Read more →

# Experimental Design and Process Optimization with R

## by Gerhard Krennrich

Experimental Design and Process Optimization with R […] The present document is a short and elementary course on the Design of Experiments (DoE) and empirical process optimization with the open-source Software R. The course is self-contained and does not assume any preknowledge in statistics or mathematics beyond high school level. Statistical concepts will be introduced on an elementary level and made tangible with R-code and R-graphics based on simulated and real world data. So, then, what is DoE and why should the reader become familiar with the concepts of DoE? Very briefly, DoE is the … Read more →

# TidySimStat

## by Edward J. Xu

Stochastic Simulation and Statistics in Tidyverse. […] This is the website hosting all the theories and and practices regarding stochastic simulation and statistics. It has the following … Read more →

# A Very Short Course on Time Series Analysis

## by Roger D. Peng

The book covers material taught in the Johns Hopkins Biostatistics Time Series Analysis course. […] This book will cover the use of time series methods in biomedical and public health applications. And maybe rockets? We will use the following … Read more →

# Let’s Explore Statistics

## by Colin Quirk

Let’s Explore Statistics […] Many researchers learn statistics as a series of flowcharts and heuristics without ever diving into the deep mathematical concepts that underlie the choices they have been taught to make. I think everyone wishes they understood statistics better but it is easy to become overwhelmed with equations and proofs. Most of my knowledge in both math and statistics is self-taught through experience and exploration. This book is an organized assortment of simulations and examples that have personally helped me think through these difficult topics. Inspiration for this … Read more →

# Data Analysis for Psychology in R (dapR1) - Labs

## by Department of Psychology, University of Edinburgh

This is the page that contains the course labs materials […] Data Analysis for Psychology in R 1 (dapR1) is your first step on the road to being a data, programming and applied statistics guru! This course provides a introduction to data, R and statistics. It is designed to work slowly through conceptual content that form the basis of understanding and working with data to perform statistical testing. At the same time, we will be introducing you to basic programming in R, covering the fundamentals of working with data, visualization and simple statistical tests. The overall aim of the … Read more →

# Notes on Modern Statistics for the Social and Behavioral Sciences (MSSBS)

## by Peter Baumgartner

This book accompanies “Rand, W. (2017). Modern Statistics for the Social and Behavioral Sciences: A Practical Introduction, Second Edition (New edition). Boca Raton, FL: Taylor & Francis Inc.”. I intend to learn and explore the concepts and procedures taught in this introductory book. Please keep in mind that this book is just a kind of training exercise for me. There will be no new insights presented, and as I am still learning the basics of R and statistics, you may find misunderstandings and errors in my writings. For authoritative reference, you have the consult the original book. Read more →

# ECON 41 Labs

## by Gabriel Butler UCLA Global Classroom

ECON 41 Labs […] This book is an R-based statistical programming companion for ECON 41 - Statistics For Economists, an undergraduate course for Economics majors offered at the University of California, Los Angeles. More specifically, it has been created to augment the version of this course that is offered at Jinling High School in Nanjing, Jiangsu, China as part of the Global Classroom program that is part of the UCLA International Institute. More basic information about our program and about me is available at the links below. Go Bruins! 金陵中学中美班，加油！ https://www.international.ucla … Read more →

# R Cookbook, 2nd Edition

## by James (JD) Long, Paul Teetor

Second edition of R Cookbook […] R is a powerful tool for statistics, graphics, and statistical programming. It is used by tens of thousands of people daily to perform serious statistical analyses. It is a free, open source system whose implementation is the collective accomplishment of many intelligent, hard-working people. There are more than 10,000 available add-on packages, and R is a serious rival to all commercial statistical packages. But R can be frustrating. It’s not obvious how to accomplish many tasks, even simple ones. The simple tasks are easy once you know how, yet figuring out that … Read more →

# R for Statistics in EPH

## by Daniel J Carter

R for Statistics in EPH […] Welcome to R for STEPH. This ‘book’ offers the chance to supplement your learning in Stata by conducting the computer practical sessions in R. By the end of this book, you will have enough proficiency in R to carry out a number of basic analyses and understand principles that will allow you to program more complex analyses. Any questions about the content in this book can be directed to Daniel Carter via email or via Twitter if you’re into that sort of thing. There is also the invaluable resource that is Stack Exchange. Chances are high that if you’re running … Read more →

# Open Forensic Science in R

## by Editor: Sam Tyner, Ph.D.

This book is for anyone looking to do forensic science analysis in a data-driven and open way. Whether you are a student, teacher, or scientist, this book is for you. We take the latest research, primarily from the Center for Statistics and Applications in Forensic Evidence (CSAFE) and the National Institute of Standards and Technology (NIST) and show you how to solve forensic science problems in R. The book makes some assumptions about you: This book free and is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 United States License. R Core Team. 2019. R: A Language … Read more →

# Men’s U-Sports Basketball Analysis

## by Michael Armanious

Sports Analytics […] Statistics in sports is a growing field in research that provides specialized methodology for collecting and analyzing sports data in order to make decisions for successful planning and implementation of new strategies [1]. Sports, particularly, have countless and ever-expanding data sources that can be used by analysts in order to extract objective information for use in aspects such as making predictions throughout seasons and enhancements in team and player performance. Broadly, Sports Analysis is described as the process of data management, predictive model … Read more →

# Introductory Resources: Statistics and R

## by Statistics Team, PPLS

This is the main page of the course and contains the materials to help you going with R […] This course is designed for those who will be joining a third year Research Methods and Statistics (RMS) course and covers a number of introductions to topics which are core to statistical analysis in psychology and beyond. You will find here an introduction to R as a tool to analyse data, visualize it and to use it for a very very basic analysis of the relationships in your data. It will further revise some of the most commonly used statistical tests and provide you with a guidance how to set up and … Read more →

# Agile Machine Learning with R

## by Edwin Thoen

A workflow for doing machine learning in the R language, using Agile principles. […] Not even too long ago, when I was starting my career as a data scientist, I did not really have a workflow. Freshly graduated from an applied statistics master I entered the arena of Dutch business, employed by a small consulting firm. Neither the company I was with, nor the clients I was working for, nor myself had an understanding of what it meant to implement a statistical model or a machine learning method in the real world. Everybody was of course interested in this “Big Data” thing, so it did not take … Read more →

# jamoviで学ぶ心理統計

## by Danielle J Navarro & Dvid R Foxcroft（著） 芝田征司（訳）

『jamoviで学ぶ心理統計』は心理学専攻の統計法入門クラス向けのテキストです。本書では，jamoviの使い方やデータ操作の方法についても扱います。統計の部分では，記述統計とグラフの作成について扱った後，確率理論，標本と推定，帰無仮説検定について説明します。理論についての説明の後は，分割表の分析，相関，t検定，回帰，分散分析について説明します。本書の最後では，ベイズ統計についても取りあげます。This book is a Japanese translation of learning statistics with jamovi. […] 本書はDavid Foxcroft氏が作成した『Learning … Read more →

# Fatal Force Study Group - Shiny App

## by marwaelatrache

Fatal Force Study Group - Shiny App […] The Fatal Force Study Group (FFSG) was founded at the University of Washington (UW) by professor Martina Morris. Morris has a strong background in Sociology and Statistics and after joining an activism group called Not This Time she decided to start investigating fatal encounters with police, along with a group of UW undergraduate students. Since then the group has been joined by Professor Ben Marwick, an UW archaeology professor with a strong interest in statistics and R, as well as several more undergraduate students from both UW and neighboring … Read more →

# PPLS PhD Training Workshop: Statistics and R

## by Anastasia Ushakova and Emma Waterston

This is the main page of the course and contains a course overview, schedule and learning outcomes. […] During this intensive workshop we will cover a number of introductions to topics which are core to statistical analysis in applied research. This will include introduction to R as a tool to analyse data, visualize it and to use it for a very very basic analysis of the relationships in your data. We will further revise some of the most commonly used statistical tests and provide you with a guidance how to set up and interpret them in R. We will introduce you to simple linear model and … Read more →

# 空间广义线性混合效应模型及其应用

## by 黄湘云

Spatial generalized linear mixed models, Stationary Spatial Gaussian Process, Stan platform, Markov chain Monte Carlo. […] 空间统计的内容非常丰富，主要分为地质统计 （geostatistics）、 离散空间变差 （discrete spatial variation） 和空间点过程 （spatial point processes） 三大块 (Cressie 1993)。 地质统计这个术语最初来自南非的采矿业 (Krige 1951)， 并由 Georges Matheron 及其同事继承和发展，用以预测黄金的矿藏含量和质量。空间广义线性混合效应模型 （Spatial Generalized Linear Mixed Model，简称 SGLMM） 在空间统计中有着广泛的应用，如评估岩心样本石油含量，分析核污染物浓度的空间分布 (Diggle, Tawn, and … Read more →

# Lab Guide to Quantitative Research Methods in Political Science, Public Policy & Public Administration.

## by josiesmith

Lab Guide to Quantitative Research Methods in Political Science, Public Policy & Public Administration. […] This book is a companion to Quantitative Research Methods for Political Science, Public Policy and Public Administration (With Applications in R): 4th Edition, an open-source text book that is available here. It grew from our experiences teaching introductory and intermediate quantitative methods classes for graduate students in Political Science and Public Policy at the University of Oklahoma. We teach these courses using a format that pairs seminars on theory and statistics with … Read more →

# PhD Training Workshop: Statistics in R

## by Anastasia Ushakova and Milan Valasek

This is the main page of the course and contains a course overview, schedule and learning outcomes. […] During this intensive workshop we will cover a number of introductions to topics which are core to statistical analysis in applied research. This will include introduction to R as a tool to analyse data, visualize it and to use it for a very very basic analysis of the relationships in your data. We will further revise some of the most commonly used statistical tests and provide you with a guidance how to set up and interpret them in R. Lastly, we will introduce you to simple linear model … Read more →

# Learning statistics with R: A tutorial for psychology students and other beginners. (Version 0.6.1)

## by DJ Navarro

Learning Statistics with R covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software. The book discusses how to get started in R as well as giving an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing ﬁrst, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, … Read more →

# Notes for ST463/ST683 Linear Models 1

## by Katarina Domijan, Catherine Hurley

These are the notes for ST463/ST683 Linear Models 1 course offered by the Mathematics and Statistics Department at Maynooth University. This module is offered at as a part of of MSc in Data Science and Data Analytics. It is an introductory course for students who have basic background in Statistics, Data analysis, R Programming and linear algebra (matrices). […] There are many good resources, e.g. Weisberg (2005), Fox (2005), Fox (2016), Ramsey and Schafer (2002), Draper and Smith (1966). We will use Minitab and R (R Core Team 2017). To create this document, I am using the bookdown package … Read more →

# Foundations of Statistics with R

## by Darrin Speegle

This book is written for the purposes of teaching STAT 3850 at Saint Louis University. […] This is a book on probability and statistics suitable for the sophomore or junior level at university. We assume knowledge of calculus at the level of Calculus II. We do not assume prior experience with statistics or programming, though students who have no experience with either statistics or programming before starting this class should expect to have to work hard. We will be using R as an integral part of the exposition — you should not read this book without first getting R Studio installed. We … Read more →

# Financial Engineering Analytics: A Practice Manual Using R

## by William G. Foote

This book explores the fundamentals of financial analytics using R and various topics from finance. […] Science alone of all the subjects contains within itself the lesson of the danger of belief in the infallibility of the greatest teachers of the preceding generation. - Richard Feynman This book is designed to provide students, analysts, and practitioners (the collective “we” and “us”) with approaches to analyze various types of financial data sets, and to make meaningful decisions based on statistics obtained from the data. The book covers various areas in the financial industry, from … Read more →

# Simulation And The James-Stein Estimator In R

## by Alex Hallam

Simple Simulation and the James-Stein Estimator […] This is the website for “Simulation And The James-Stein Estimator In R”. This technical document is short, covering some common ways to generate data and exploring the James-Stein Estimator. This will teach you how to do run simulations to observe the properties of the James-Stein Estimator in R — specifically using the tidyverse: You’ll learn how to generate data to prove theoretical results. In the computer age of statistics the data scientist has the power of machines to run simulations for testing a methods before putting a method into … Read more →

# Course Notes for IS 6489, Statistics and Predictive Analytics

## by Jeff Webb

Course notes for IS 6489. […] These are the course notes for IS 6489, Statistics and Predictive Analytics, offered through the Information Systems (IS) department in the University of Utah’s David Eccles School of Business. This is an exciting time for data analysis! The field has undergone a revolution in the last 15 years with increases in computing power and the availability of “big data” from web-based systems of data collection. “Data science” is the umbrella term that describes the result of this revolution—a new discipline at the intersection of many traditional fields such as … Read more →

# Probability and Statistics

## by Rob Carroll

These are the lecture notes for POS 5737, the introductory probability and statistics class in the graduate program in political science at Florida State University. […] These are the notes for POS 5737, taught in the Department of Political Science at Florida State University. They freely borrow from several well-known textbooks, including those by Wackerly, Mendenhall, and Scheaffer (2008), DeGroot and Schervish (2012), and Casella and Berger (2002). They also borrow from my own notes as a graduate student when I was taught by Kevin Clarke. Kevin was kind enough to provide his own old … Read more →

# ModernDive

## by Chester Ismay and Albert Y. Kim STARRING FRANK MCGRADE

An open-source and fully-reproducible electronic textbook bridging the gap between traditional introductory statistics and data science courses. […] Help! I’m new to R and RStudio and I need to learn about them! However, I’m completely new to coding! What do I do? If you’re asking yourself this question, then you’ve come to the right place! Start with our Introduction for Students. This is version 0.2.0 of ModernDive published on August 02, 2017. For previous versions of ModernDive, see Section 1.4. This book assumes no prerequisites: no algebra, no calculus, and no prior programming/coding … Read more →

# (Very) basic steps to weight a survey sample

## by Josep Espasa Reig

(Very) basic steps to weight a survey sample […] This is an introductory guide to survey weighting. It provides a step-by-step walkthrough of the main procedures and explains the statistical principles behind them. The guide includes R code to implement all stages of survey weighting and reproduces the weighting procedures of the 7th European Social Survey in the UK. This text avoids technical notation and language and is targeted to social scientists with a basic level of statistics and probability theory. Readers without knowledge of R should be able to benefit from this text as it … Read more →

# 기초통계 개념정리

## by 김진섭

This is a basic statistics book written by JSKIM. […] This is a basic statistics book written by Jinseob … Read more →

# Econ 215 Notes

## by Salfo Bikienga

Lecture notes for my introduction to statistics class at University of Nebraska-Lincoln. […] This is supposed to be your first course in statistics. So the goal is to give you an overview of what statistics is, why it is a powerful thing to know, how you can use it to make informed decision or understand “numbers speak” people throw around in the news. At the end of this class, I hope: 1- You understand the importance of statistics; 2- You can better appreciate the numbers you get from the news; 3- You can perform your own analysis to inform yourself, and your collaborators. The explosion … Read more →

# Principles of Econometrics with R

## by Constantin Colonescu

This is a beginner’s guide to applied econometrics using the free statistics software R. […] … Read more →

# Backtesting Strategies with R

## by Tim Trice

Backtesting strategies with R […] This book is designed to not only produce statistics on many of the most common technical patterns in the stock market, but to show actual trades in such scenarios. Test a strategy; reject if results are not promising Apply a range of parameters to strategies for optimization Attempt to kill any strategy that looks promising. Let me explain that last one a bit. Just because you may find a strategy that seems to outperform the market, have good profit and low drawdown this doesn’t mean you’ve found a strategy to put to work. On the contrary, you must work to … Read more →

# Block Relaxation Methods in Statistics

## by Jan de Leeuw

The book discusses block relaxation, alternating least squares, augmentation, and majorization algorithms to minimize loss functions, with applications in statistics, multivariate analysis, and multidimensional scaling. […] Many recent algorithms in computational statistics are variations on a common theme. In this book we discuss four such classes of algorithms. Or, more precisely, we discuss a single large class of algorithms, and we show how various well-known classes of statistical algorithms fit into this common framework. The types of algorithms we consider are, in logical order, … Read more →

# A Practical Extension of Introductory Statistics in Psychology using R

## by Ekarin E. Pongpipat, Giuseppe G. Miranda, & Matthew J. Kmiecik

This book aims to provide a practical extension of introductory statistics typically taught in psychology into the general linear model (GLM) using R. […] Typically, introductory univariate statistics courses in psychology cover the following inferential analyses (plus or minus a few more analyses): These conventions may be useful for quickly talking about a particular statistical analysis with others; however, thinking of these analyses as derivatives (or special cases) of the GLM (i.e., ordinary least squares [OLS] regression) lends itself to understanding more advanced statistical … Read more →

# Agile Data Science with R

## by Edwin Thoen

A workflow for doing data science in the R language, using Agile principles. […] When I was starting my career as a data scientist, I did not really have a workflow. Freshly out of statistics grad school I entered the arena of Dutch business, employed by a small consulting firm. Between the company, the potential clients and myself, no one knew what it meant to implement a statistical model or a machine learning method in the “real” world. But everybody was interested in this “Big Data” thing, so we quickly started to do consulting work without a clear idea what I was going to do. When we came … Read more →

# Big Data and Social Science

## by Ian Foster, Rayid Ghani, Ron S. Jarmin, Frauke Kreuter and Julia Lane

Big Data and Social Science […] The class on which this book is based was created in response to a very real challenge: how to introduce new ideas and methodologies about economic and social measurement into a workplace focused on producing high-quality statistics. Since the first edition of this book came out we have been fortunate to train over 450 participants in the Applied Data Analytics classes, resulting in increased data analytics capacity, both in terms of human and technical resources. What we learned in delivering these classes greatly influenced the 2nd edition. We also added an … Read more →