My Name Is EZRA And My Pen Is Huge! In addition, the last four chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, and survival analysis. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The book has been primarily designed as a useful companion for a Masters student during each semester of the course, but will also help applied statisticians in revisiting the underpinnings of the subject. Publisher: University of Adelaide 2014 Number of pages: 564. “This book (ISL) is a great Master’s level introduction to statistical learning: statistics for complex datasets. With this dual goal in mind, the book begins with R basics and quickly covers visualization and exploratory analysis. To paraphrase provocatively, ’machine learning is statistics minus any checking of models and assumptions’. These notes describe how to use Rwhile learning introductory statistics. This book discusses how to get started in R, and gives an introduction to data manipulation and writing scripts. Inspired by the programming group “R Ladies,” the R Team works together to master the skills of statistical analysis and data visualization to untangle real-world, messy data using R. The storylines draw students into investigating contemporary issues such as marijuana legalization, voter registration, and the opioid epidemic, and lead them step-by-step through full-color illustrations of R statistics and interactive exercises. It is primarily aimed at undergraduate students in medicine, engineering, economics and biology - but will also appeal to postgraduates who have not previously covered this area, or wish to switch to using R. Gain the R programming language fundamentals for doing the applied statistics useful for data exploration and analysis in data science and data mining. The purpose is to allowthis ne software to be used in "lower-level" courses where often MINITAB, SPSS, Excel, etc. Buy an annual subscription and save 62% now! The high-level language of R is recognized as one of the most powerful and flexible statistical software environments, and is rapidly becoming the standard setting for quantitative analysis, statistics and graphics. * The emphasis throughout is on estimation of effect sizes and confidence intervals, rather than on hypothesis testing. The book discusses how to get started in jamovi as well as giving an introduction to data manipulation. This book is intended for the undergraduate and postgraduate students of Management and Engineering disciplines. It covers concepts from probability, statistical inference, linear regression, and machine learning. Given this book's accessibility, fun spirit, and use of bizarre real-world research it should be essential for anyone wanting to learn about statistics using the freely-available R software. Drawing on examples from across the social and behavioral sciences, Statistics with R: Solving Problems Using Real-World Data by Jenine K. Harris introduces foundational statistics concepts with beginner-friendly R programming in an exploration of the world’s tricky problems faced by the “R Team” characters. In addition, the book chapters contain numerous exercises with answers or solutions to the exercises provided. KEY FEATURES 1. Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. Each technique is framed within an easy-to-apply 12-step methodology that will make planning and presenting research a breeze. Instructors. One divergence is the introduction of R as part of the learning process. I am not aware of attempts to use R in introductory level courses. Like its sister textbooks, Discovering Statistics Using R is written in an irreverent style and follows the same ground-breaking structure and pedagogical approach. In addition, the last four chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, and survival analysis. A unique feature of the book is the photographs and biographies of famous persons in the field of multivariate statistics. "This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Gain the R programming language fundamentals for doing the applied statistics useful for data exploration and analysis in data science and data mining. Manjunath, Dell International Services, India Integrates the theory and applications of statistics using R A Course in Statistics with R has been written to bridge the gap between theory and applications and explain how mathematical expressions are converted into R programs. He demonstrates: populations and variables; parameters; percentages; probability and sampling; sampling distribution; estimation; hypothesis testing; and two-by-two tables. Each chapter includes discussion and explanations, tables and graphs, and R functions and outputs to enrich readers' understanding of statistics through statistical computing and modeling. R, a powerful open-source software package, was created in response to this issue. It emphasizes an estimation-based approach that accounts for recent criticisms of the over-use of probability values, and introduces alternative approaches using information criteria. No prior experience with data mining packages is necessary; however, you should have a basic understanding of data mining concepts and processes. Offer ends in 7 days 12 hrs 28 mins 48 secs. 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. There are many good resources for learning R. The following few chapters will serve as a whirlwind introduction to R… * Features step-by-step instructions that assume no mathematics, statistics or programming background, helping the non-statistician to fully understand the methodology. Publisher: Springer Science & Business Media, With Data Visualizations, Regressions, and Statistics, Data Analysis and Prediction Algorithms with R, USING STATISTICS AND PROBABILITY WITH R LANGUAGE. Color graphics and real-world examples are used to illustrate the methods presented. An instructor can select any of the supplemental materials to enhance lectures and/or provide additional coverage of concepts and topics in their statistics book. Topics; Collections; Trending; Learning Lab; … The book has an accompanying package, UsingR, available from CRAN, R’s repository of user-contributed packages. The pacing is such that students are able to master data manipulation and exploration before diving into more advanced statistical concepts. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. If you have some experience already, this book will make you more productive and enhance your understanding of foundational statistical concepts." 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