# Statistics

# Geostatystyka w R

## by Jakub Nowosad

Introduction to geostatistics with R (in Polish). […] Masz przed sobą skrypt zawierający materiały do ćwiczeń z geostatystyki. Składa się ona z kilkunastu rozdziałów pokazujących jak: wygląda geostatystyczna analiza danych i w jaki sposób można odtworzyć przykłady zawarte w skrypcie (rozdział ??), dodawać i wizualizować dane przestrzenne w R (rozdział 2), wykonywać wstępną eksplorację danych nieprzestrzennych (rozdział 3), wstępnie analizować dane przestrzenne (rozdział 4), wykorzystywać deterministyczne metody interpolacji (rozdział 5), rozumieć i tworzyć przestrzenne miary podobieństwa i … Read more →

# Doing Meta Analysis in R

## by Mathias Harrer, B.Sc. & Dr. habil. David Ebert

This is a guide on how to conduct Meta-Analysis in R. […] This guide shows you how to conduct Meta-Analyses in R from scratch. The focus of this guide is primarily on clinical outcome research in psychology. It is designed for staff and collaborators of the PROTECT Lab, which is headed by Dr. David D. Ebert. The guide will show you how to: What this guide will not cover Although this guide will provide some information on the statistics behind meta-analysis, it will not give you an in-depth introduction into how meta-analyses are calculated statistically. It is also beyond the scope of this … Read more →

# Modern R with the tidyverse

## by Bruno Rodrigues

This book will teach you how to use R to solve you 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. […] This book is still being written. Chapters 1 to 6 are almost ready. Chapter 7 is outdated, but the key messages are still useful. Chapters 8 and 9 are quite complete too. 10 and 11 are empty for now. Some exercises might be at the wrong place too. If you already like what you … 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. […] Definition 1.1 (Random experiment) A random experiment is an experiment with the following properties: The following concepts are associated with a random experiment: Example 1.1 The next experiments are random experiments: A probability function is defined as a mapping of subsets (events) of the sample space (\Omega) to elements in ([0,1]). Therefore, it is convenient to count on a “good” structure for these subsets, which will provide “good” properties to the probability … 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 →

# 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. It should also be noted that there is a comment functionality in this book - leaving notes and questions … Read more →

# New statistics for the design researcher

## by Martin Schmettow

A statistics book for designers, human factors specialists, UX researchers, applied psychologists and everyone else who works hard to make this world a better place. […] This book makes the following assumptions: Chapter @ref(design_research) introduces a framework for quantitative design research. It carves out the basic elements of empirical design research, such as users, designs and performance and links them to typical research problems. Then the idea of design as decision making under uncertainty is developed at the example of two case studies. Chapter @ref(bayesian_statistics) … 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 →

# Basic Social Justice Orientations scale testing

## by Cristóbal Moya

This is a minimal example of using the bookdown package to write a book. The output format for this example is bookdown::gitbook. […] The original publication has three tables where ALLBUS-2014 is used: The following analysis are focused on the first two tables (Table 15 and Table 8), because they contain the main resutls regarding this data source and the table from supplementary materials should not matter as long as factor loadings in Table 8 are correct. The descriptive statistics of the eight items displayed in Table 15 of the original article are reproduced from the article’s website … Read more →

# An Introduction to Statistical and Data Sciences via R

## by Chester Ismay and Albert Y. Kim

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 0.4.0 of ModernDive published on July 21, 2018. For previous versions of ModernDive, see Section 1.5. This book assumes no prerequisites: no algebra, no calculus, and no prior programming/coding … 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 →

# 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. […] 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 … 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 →

# The Queens College Stats Guide

## by The Queens College Collective Consciousness

This is a minimal example of using the bookdown package to write a book. The output format for this example is bookdown::gitbook. […] This is a guide to learning statistics at Queens College. Currently we support the following courses directly with … Read more →

# Broadening Your Statistical Horizons

## by J. Legler and P. Roback

Test. […] Broadening Your Statistical Horizons (BYSH): Generalized Linear Models and Multilevel Models is intended to be accessible to undergraduate students who have successfully completed a regression course through, for example, a textbook like Stat2. We started teaching this course at St. Olaf in 2003 so students would be able to deal with the non-normal, correlated world we live in. It has been offered at St. Olaf every year since; in fact, it is required for all statistics concentrators. Even though there is no mathematical prerequisite, we still introduce fairly sophisticated topics … 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 →

# 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 →

# 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. Colin Gillespie is Senior lecturer (Associate professor) at Newcastle University, UK. His research interests are high performance statistical computing and Bayesian statistics. He is regularly employed as a consultant by Jumping Rivers and has been teaching R since 2005 at a variety of levels, ranging … 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 →

# Odds & Ends

## by Jonathan Weisberg

A textbook introducing philosophy students to probability, decision theory, and the philosophical foundations of statistics […] This textbook is for introductory philosophy courses on probability and inductive logic. It is based on a typical such course I teach at the University of Toronto, where we offer “Probability & Inductive Logic” in the second year, alongside the usual deductive logic intro.(\,) The book assumes no deductive logic. The early chapters introduce the little that’s used. In fact almost no formal background is presumed, only very simple high school algebra. Several well … Read more →