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StatPlus:mac requires macOS StatPlus:mac does not require Internet connection except for optional auto-update feature. StatPlus:mac StatPlus for Windows. StatPlus:mac But what if you work for a math-heavy industry, pharmacology, medicine or sociology, and complex statistical analysis is part of your daily routine?

Upgrade Now. View Features List. Looking for Windows version? Free Trial We have free trial that gives you an opportunity to evaluate the software before you purchase it. Affordable StatPlus:mac is most affordable solution for data analysis on Mac with Excel and Numbers support. Requirements StatPlus:mac requires macOS Priority support. Bug fix warranty and free major upgrades during your maintenance period.

Access to Windows version. Basic Statistics Detailed descriptive statistics. One-sample t-test. Two-sample t-test also includes Pagurova Criterion, G-Criterion. Two-sample t-test for summarized data. Fisher F-test. One-sample and two-sample z-tests. Correlation coefficients Pearson, Fechner and covariation. Normality tests includes D'Agostino's tests.


Cross-tabulation and Chi-square. Frequency tables analysis for discrete and continuous variables. Three-way analysis of variance. Data Classification Discriminant function analysis. Rank and percentile. Chi-square test. Cochran's Q Test. Regression Analysis Multivariate linear regression. Notes for Nonparametric Statistics.

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MSc in Statistics for Data Science. The course is designed to have, roughly, one lesson per each main topic in the syllabus. The schedule is tight due to time constraints, which will inevitably make the treatment of certain methods a little superficial compared with what it would be the optimal. This book aims to provide a practical extension of introductory statistics typically taught in psychology into the general linear model GLM using R.

There are two programming languages that will be taught through the course: R and Javascript. This hands-on guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. The jamovi quickstart guide features a collection of non-technical tutorials on how to conduct common operations in jamovi. Additionally, the tutorials cover the use of csv files, wide data format, and setting the data type in jamovi. University of Calgary ARC Manual […] This manual contains all of the code developed to run neuroimaging programs on the University of Calgary Arc high performance computing systems.

The first official book authored by the core R Markdown developers that provides a comprehensive and accurate reference to the R Markdown ecosystem. With R Markdown, you can easily create reproducible data analysis reports, presentations, dashboards, interactive applications, books, dissertations, websites, and journal articles, while enjoying the simplicity of Markdown and the great power of R and other languages.

These materials are excellent and provide an in-depth look at RAP in R. Dit zijn de lecture notes van het opleidingsonderdeel Beleidsinformatica […] Dit document bevatten de lecture notes voor het opleidingsonderdeel Beleidsinformatica , gedoceerd aan de Universiteit Hasselt. We raden aan om deze lecture notes steeds kort na het hoorcollege door te nemen en aan te vullen met je eigen notities uit het college. 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. Chapters 1 to 8 are almost ready, but more content is being added especially to chapter 8.

Some exercises might be at the wrong place too and more are coming. This document consists of three components: 1. A background section describing the objectives, tools, and approach to developing new water quality assessment tools. Editing refers to the detection and correction of errors in the data, whilst imputation is a method of correcting errors in a dataset. This document presents findings from work carried out at the Office for National Statistics on the use of machine learning in imputation.

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. There is also the invaluable resource that is Stack Exchange. This is a bookdown template based on ElegantBook. This seminar is taught by Paul C. Bauer right now - Spring Semester - at the University of Mannheim. The material was developed by Paul C. Seeing through the developping lens: […] Through this project, we aim to decipher post-transcriptional regulation network in the developping lens.

In the past decades, post-transcriptional gene regulation PTGR was shown to be of particular importance in the developping lens. Indeed, the alteration of PTGR network can result in abnormal development of the lens, of the eye. A useR guide to creating highly interactive graphics for exploratory and expository visualization. The study comprises three survey embedded experiments; one video vignette experiment in Norway, one text vignette experiment in Sweden, and one conjoint experiment in Norway. KULeuven R tutorial for marketing students […] In this tutorial, we will explore R as a tool to analyse and visualise data.

R is a statistical programming language that has rapidly gained popularity in many scientific fields. There are no buttons to click. R is run entirely by typing commands into a text interface. This may seem daunting, but hopefully by the end of this tutorial you will see how R can help you to do better statistical analysis. A handbook on teaching and learning with Jupyter notebooks. Barba, Lecia J. Barker, Douglas S. Blank, Jed Brown, Allen B. Downey, Timothy George, Lindsey J. Heagy, Kyle T.

Mandli, Jason K. Moore, David Lippert, Kyle E. Niemeyer, Ryan R. Watkins, Richard H. West, Elizabeth Wickes, Carol Willing, and Michael Zingale This handbook is for any educator teaching a topic that includes data analysis or computation in order to support learning. The final report for DSBA Final Project […] For our project we explored data related to opioids, in an effort to better understand and obtain insight into the opioid epidemic.

Our domain problem is one for a researcher wanting to explore the connection between prescriber rates of opioid prescriptions and opioid-related deaths both in the country as a whole and drilling down to the state level. The first part of the data we examined was prescriber data. His models are re-fit in brms, plots are redone with ggplot2, and the general data wrangling code predominantly follows the tidyverse style. This book consists of two components: 1. A background section describing the objectives, tools, and approach to developing new water quality assessment tools, and 2.

A full-scale demonstration of the current state of this new toolset via the application of these tools to the IR period of record dataset This is an open source collaborative book. Doch was verbirgt sich dahinter? Dies vor allem aus Sicht der Praxis. Speziell aus dem Blickwinkel der Betriebswirtschaft werden Use-Cases versucht einfach und nachvollziehbar darzustellen. Viel Spass auf der Entdeckungsreise. Content on this site is actively built and refined throughout the semester.

Unless specifically directed to a section of this online text, you should refer to the Programming Skills for Data Science textbook. This book covers the foundation skills necessary to start writing computer programs to work with data using modern and reproducible techniques. It requires no technical background. This book introduces concepts and skills that can help you tackle real-world data analysis challenges. This is an introductory textbook that focuses on how to use R to do technical analysis.

It is well-suited to do computationally heavy financial analysis. In particular, evaluating performance of trading rule based on technical indicators. Moreover, R can be one-stop solution to the whole procedure of data analysis. A standard procedure of financial data analysis is: You can do all of them inside R without using other software. This short book is a short introduction on how to use R and RStudio to do financial data analysis from the beginning. A guide to visualise networks of Twitter interactions in R using the twinetverse.

The following pages will walk you trough the packages contained within the twinetverse, from collecting twitter data to building various types of networks to visualising them. The source code for this book can be found on Github. This book is organised with each chapter corresponding to lectures from the Mathematical Modelling component of the course.

This book contains many code chunks that can be copied and pasted into an R console to create Shiny apps of the models being discussed.

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Online version including Shiny apps were created by Nicole Fortuna. Yihui Xie es ingeniero de software de RStudio, autor de distintos paquetes como knitr, blogdown, xaringan, tinytex y bookdown. Este archivo puede ser un manual de usuario, nuestras notas de estudio e incluso nuestro diario. Online booklet for conference workshop on data visualization with R, geared to those who have never used R. You can download the slides from the workshop by clicking here. To try out these examples, you need some set-up: This section will walk you through each step.

R is free and open-source software. This is a compendium of the letters written, sent and received during October until September We are socially fragmented. It could be used as an argument for a non-local consciousness theory. Therefore, with this text, I am just trying to compile pieces of what I have been in order to know a bit better what I am know. I have been told in the past that there is wisdom in the text that I wrote, and I am certainly sure that there is wisdom in the texts that I received.

This document provides an introduction to machine learning for applied researchers. While conceptual in nature, demonstrations are provided for several common machine learning approaches of a supervised nature. In addition, all the R examples, which utilize the caret package, are also provided in Python via scikit-learn.

MSc in Applied Mathematics. The emphasis is placed in building intuition behind the methods, gaining insights into their asymptotic properties, and showing their application through the use of statistical software. Notes for Predictive Modeling. MSc in Big Data Analytics. Predictive Soil Mapping aims to produce the most accurate, most objective, and most usable maps of soil variables by using state-of-the-art Statistical and Machine Learning methods. This books explains how to implement common soil mapping procedures within the R programming language.

Pull requests and general comments are welcome. Elles peuvent exister dans un classeur Excel sous format xls, xlsx ou csv. An introduction to generalized additive models GAMs is provided, with an emphasis on generalization from familiar linear models. It makes extensive use of the mgcv package in R. Discussion includes common approaches, standard extensions, and relations to other techniques. More technical modeling details are described and demonstrated as well.

Analysis of case studies and exercies with a focus on using the tidyverse and ggplot2. This handbook was created using the bookdown package in RStudio. As a scientist, we are trained to formulate a hypothesis and design a series of experiments that allow us to test the hypothesis effectively. How to do comparative methods for evolution and ecology […] This book was created as part of my PhyloMeth class, which focuses on sensibly using and developing comparative methods. This is an analysis report of a comparative conjoint study on the legitimacy of EU referendums. Broderstad, Mikael P. Johannesson, and Jonas Linde.

This is an introduction to mixed models in R. It covers a many of the most common techniques employed in such models, and relies heavily on the lme4 package. The basics of random intercepts and slopes models, crossed vs. Discussion includes extensions into generalized mixed models and realms beyond.

This process is fundamental for our life on Earth, and it has been intensively studied for centuries by scientists. Scientists like me, like us. El mejor libro en espanol de ciencia de datos, libre y abierto. Este libro ha sido elaborado por BEST. Y es que nuestra sociedad ha evolucionado, y con ellos ciertas necesidades. You can view this book in any modern desktop browser, as well as on your phone or tablet device.

Dylan Childs is running the course this year. Please email him if you spot any problems with the course book. An applied textbook on generalized linear models and multilevel models for advanced undergraduates, featuring many real, unique data sets. It is intended to be accessible to undergraduate students who have successfully completed a regression course. Even though there is no mathematical prerequisite, we still introduce fairly sophisticated topics such as likelihood theory, zero-inflated Poisson, and parametric bootstrapping in an intuitive and applied manner.

We believe strongly in case studies featuring real data and real research questions; thus, most of the data in the textbook arises from collaborative research conducted by the authors and their students, or from student projects. Our goal is that, after working through this material, students will develop an expanded toolkit and a greater appreciation for the wider world of data and statistical modeling. Introduction to geostatistics with R in Polish. The output format for was elaborated with bookdown::gitbook. Dynamic Documents with R and Knitr.

This is a textbook for use in undergraduate critical thinking courses. There are many good textbooks for critical thinking on the market today, so why write another one? None cover all of the topics that I would like to cover in class. If you want to know more about me or contact me you can visit my Linkedin profile or my Twitter account. This is my final project for the Kschool Master on Data Science 8th edition. Je sais ce que je veux et comment y arriver. Je suis flexible et dynamique. This document is full of supplemental resources and content from the Shiny in Production Workshop delievered at rstudio::conf The story of a small journey in Autumn Its two participants head south in their car Dot.

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.

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This is a book on data visualization using ggplot2 created for the Stanford Data Challenge Lab. These are solutions from the book by Richard McElreath. Referentiekaarten […] Voor de KRW is een groot deel van het oppervlaktewater aangewezen als waterlichaam. Voor deze wateren moet de toestand van het aquatisch ecosysteem beschreven worden. De informatie over de huidige situatie en ontwikkelingen van het aquatisch ecosysteem in de regio Amstel, Gooi en Vechtstreek bundelen wij in een zogenaamde Atlas met thema kaarten. Onze doelstellingen en de huidige ecologische kwaliteit zijn verbeeld in de afbeeldingen hieronder.

Op een andere pagina Waterkwaliteit in beeld staan kaarten van verschillende indicatoren van ecologische kwaliteit voor de Habitatrichtlijn Natura en de Kaderrichtlijn water. Waterkwaliteit in beeld […] De informatie over de huidige situatie en ontwikkelingen van het aquatisch ecosysteem in de regio Amstel, Gooi en Vechtstreek bundelen wij in een zogenaamde Atlas met thema kaarten. In hoofdstuk 2 staan kaarten van verschillende indicatoren van ecologische kwaliteit voor de Habitatrichtlijn Natura en de Kaderrichtlijn water. El libro se compone de 4 partes. Parte I cubre la parte introductoria.

Simple cookbook for functions and idioms within the scope of the tidyverse.

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As an extra I would like to provide similar solutions based on base R functionality. Some reasons to write this book: One strength of the tidyverse is that it hides a lot of quirks that base R provides and inherits to many packages that rely on it. This allows to stick to a specific workflow from the point you enter the tidyverse until you leave it.

Bauer at the University of Bern Fall Semester Notes for Statistical Inference. You can read more about this project by reading its main paper or looking at the code repository. This guide will not explain or require any analysis of the java code. The tutorial has since then been expanded continuously, based on response from the many users and based on new developments.

It is an introductory course for students who have basic background in Statistics, Data analysis, R Programming and linear algebra matrices. This document will introduce participants to the basics of R Markdown. After an introduction to concepts related to reproducible programming and research, demonstrations of standard markdown, as well as overviews of different formats, will be provided, including exercises. Dieses Buch befindet sich zur Zeit in Arbeit.

An geeigneter Stelle wird auf diese Quellen verwiesen. 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. 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. This is a workflow for metabolomics studies. It would cover a full reproducible metabolomics workflow for data analysis and important topics related to metabolomics. Here is a list: This is a book written in Bookdown. You could contribute it by a pull request in Github.

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. Stock and Mark W.

Watson 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. Ecologische waterkwaliteit Botshol […] AGV is als waterbeheerder verantwoordelijk dat de wateren in haar beheergebied voldoen aan de waterkwaliteitsdoelstellingen van de Europese Kaderrichtlijn Water KRW en aan doelstellingen die zijn geformuleerd in het Natura beheerplan.

Deze richtlijnen hebben als einddoel schoon en gezond water. Mastering DFS Analytics is a data-driven program to improve your daily fantasy sports results. In addition to the ebook, you get Comments? The Grange-Lab Manual provides information on all you want or need to know about working in the Grange Lab. The focus of this document is on common data processing and exploration techniques in R, especially as a prelude to visualization.

For visualization, the focus will be on using ggplot2 and other packages that allow for interactivity. In addition, basic programming concepts and techniques are introduced. Exercises may be found in the document as well. In addition, the demonstrations of the data processing section are available in Python via Jupyter notebooks. First try on a book on tidy Portfolio Managment in R. In the past years I have been a heavy promoter of the Rmetrics tools for my lectures and research.

However, in the last year the development of the project has stagnated due to the tragic death of its founder Prof. This document provides a brief comparison of various approaches to dealing with clustered data situations. This document focuses on structural equation modeling. It is conceptually based, and tries to generalize beyond the standard SEM treatment.

It includes special emphasis on the lavaan package. This document covers a wide range of topics, including how to process text generally, and demonstrations of sentiment analysis, parts-of-speech tagging, word embeddings, and topic modeling. Exercises are provided for some topics. This book is the official documentation for the R package IsoriX.

The chapters 1 to 5 are almost complete but you will have to wait for the other chapters to follow. A guide to creating modern data visualizations with R. Starting with data preparation, topics include how to create effective univariate, bivariate, and multivariate graphs. In addition specialized graphs including geographic maps, the display of change over time, flow diagrams, interactive graphs, and graphs that help with the interpret statistical models are included.

Focus is on the 45 most popular graph types. The guide also includes detailed instructions on how to customizing graphs, and ends with a chapter on graphing best practices. Although strongly based on the ggplot2 package, other approaches are included as well. This guide aims to introduce the reader to the main elements of the R ecosystem. Both editions of his text have been from a frequentist OLS perspective.

This document provides an introduction to Bayesian data analysis. It is conceptual in nature, but uses the probabilistic programming language Stan for demonstration and its implementation in R via rstan. From elementary examples, guidance is provided for data preparation, efficient modeling, diagnostics, and more.

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The idea was to embed code chunks of R or other languages in Markdown documents. Looking back over the five years, it seems to be fair to say that Markdown has become the most popular document format, which is what we expected. This is a short course on survival analysis applied to the financial field. It is mainly focussed on applying the stastical tecnquines developed in the survival field to the financial industry.

I taught this course off and on from — 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. Perhaps even more importantly work with data puts learners in the position to use data to ask and answer questions, a potentially empowering capability.

This is a collection of the discussion lists from Macroeconomics. Item 2 is for data visualization. And item 3 is for general discussion regarding world news. Data Visualization Project […] This study aims at investigating how the change of information dissemination process would affect the window-dressing behaviors of mutual fund managers.

By convention, window-dressing is defined as the portfolio manipulations right before the quarter-end date, when all the fund managers are required to disclosure their holding firms of that date. Over the past decades, technological progresses largely change the way how information disseminates, and these further influence the information flow of capital markets.

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. This neorealist appropriation has met substantial criticism and many revisionist scholars have urged a re-examining of Thucydides. A summary of research conducted hitherto.

Using the bookdown package has enabled me to piece together my research in a quick and neat manner. I have tried to convey complex terms as simply as possible utilizing visual examples where I can. Script for a an R course at the European University Institute. Dit zijn de lecture notes van het opleidingsonderdeel Business Process Management […] Dit document bevatten de lecture notes voor het opleidingsonderdeel Business Process Management , gedoceerd aan de Universiteit Hasselt.

Brief introduction to Statistic […] Many statistical quantities derived from data samples are found to follow the Chi-squared distribution. Hence we can use it to test whether a population fits a particular theoretical probability distribution. In this section, we consider a multinomial experiment with k outcomes that correspond to categories of a single qualitative variable. The results of such an experiment are summarized in a one-way table.

The term one-way is used because only one variable is classified. Het idee van dit document is een begeleidende tekst aan te reiken ter ondersteuning van de slide-decks die gebruikt worden tijdens de hoorcolleges. This is a minimal book created by using the bookdown package. The output format for this little book is bookdown::gitbook. Novel methods for dose—response meta-analysis […] A single experiment can hardly provide a definitive answer to a scientific question.

Science is oftentimes referred to as a cumulative process where results from many studies, aiming to address a common question of interest, contribute to create and update the scientific evidence. In the cumulative paradigm, meta-analysis is the statistical methodology to combine and compare the current evidence in the field. This process lies at the heart of the concept of evidence-based medicine and plays a major role in policy and decision making.

We aim to create a platform for the applied social scientists in which we can demonstrate basic statistical procedures using R R Core Team b and real data. My book about adventures in a video game named Minecraft. 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. For now, you have to install the development versions of bookdown from Github: Remember each Rmd file contains one and only one chapter, and a chapter is defined by the first-level heading.

This book explores the fundamentals of financial analytics using R and various topics from finance. This book is an introduction to functional programming and unit testing with the R programming language, for the purpose of data muning […] This book is still being written, some chapters are not finished yet, and there might be there are some typos. You can purchase a digital copy of this book at leanpub. The version on Leanpub will not always be up-to-date, I only update it when I made very big changes new chapters, etc.

But once this book will be finished, both version are going to be the same. This is the item response function IRF. The IRF is therefore a function relating the latent trait to the probability of answering the item correctly. This technical document is short, covering some common ways to generate data and exploring the James-Stein Estimator. This is a collection of data visualization handouts from Macroeconomics. This book provides selected solutions to the exercises in the wonderful book R for Data Science by Wickham Hadley. The chapter labels in this book is the same as the original R4DS book; go to the corresponding chapter for solutions.

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A tutorial to R bookdownplus, an extension of R bookdown package. This books shows helps you write academic journal articles, guitar books, chemical equations, mails, calendars, and diaries, on the basis of R bookdown. This document describes the concept of Mass Customisation as it applies to Business Analytics and provides case study implementations of R Studio […] It has been great being part of the Analytical Community the last few years.

The talent being attracted into Analytics is awe inspiring. A track of papers we read and papers we collect to read in future. Arbeitsprozesse und Werkzeuge des wissenschaftlichen Arbeitens. Studieren und Forschen mit dem Internet ist beim StudienVerlag herausgekommen und heute vergriffen. The book covers R software development for building data science tools.

As the field of data science evolves, it has become clear that software development skills are essential for producing useful data science results and products. You will obtain rigorous training in the R language, including the skills for handling complex data, building R packages and developing custom data visualizations. You will learn modern software development practices to build tools that are highly reusable, modular, and suitable for use in a team-based environment or a community of developers. Course notes for IS This is an exciting time for data analysis!