Learn how to use R to turn raw data into insight, knowledge, and understanding. 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. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

The OpenIntro project was founded in 2009 to improve the quality and availability of education by producing exceptional books and teaching tools that are free to use and easy to modify. We feature real data whenever possible, and files for the entire textbook are freely available at openintro.org. Visit our website, openintro.org. We provide free videos, statistical software labs, lecture slides, course management tools, and many other helpful resources.

Based on Neil J. Salkind’s bestselling text, Statistics for People Who (Think They) Hate Statistics, this adapted Excel 2016 version presents an often intimidating and difficult subject in a way that is clear, informative, and personable. Researchers and students uncomfortable with the analysis portion of their work will appreciate the book′s unhurried pace and thorough, friendly presentation. Opening with an introduction to Excel 2016, including functions and formulas, this edition shows students how to install the Excel Data Analysis Tools option to access a host of useful analytical techniques and then walks them through various statistical procedures, beginning with correlations and graphical representation of data and ending with inferential techniques and analysis of variance. New to the Fourth Edition: A new chapter 20 dealing with large data sets using Excel functions and pivot tables, and illustrating how certain databases and other categories of functions and formulas can help make the data in big data sets easier to work with and the results more understandable. New chapter-ending exercises are included and contain a variety of levels of application. Additional TechTalks have been added to help students master Excel 2016. A new, chapter-ending Real World Stats feature shows readers how statistics is applied in the everyday world. Basic maths instruction and practice exercises for those who need to brush up on their math skills are included in the appendix.

INTRODUCTION TO STATISTICS AND DATA ANALYSIS introduces you to the study of statistics and data analysis by using real data and attention-grabbing examples. The authors guide you through an intuition-based learning process that stresses interpretation and communication of statistical information. Simple notation--including frequent substitution of words for symbols--helps you grasp concepts and cement your comprehension. You'll also find coverage of most major technologies as a problem-solving tool, plus hands-on activities in each chapter that allow you to practice statistics firsthand.

Model formulae represent a powerful methodology for describing, discussing, understanding, and performing that large part of statistical tests known as linear statistics. The book aims to put this methodology firmly within the grasp of undergraduates.

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.

The introduction to statistics that psychology students can't afford to be without Understanding statistics is a requirement for obtaining and making the most of a degree in psychology, a fact of life that often takes first year psychology students by surprise. Filled with jargon-free explanations and real-life examples, Psychology Statistics For Dummies makes the often-confusing world of statistics a lot less baffling, and provides you with the step-by-step instructions necessary for carrying out data analysis. Psychology Statistics For Dummies: Serves as an easily accessible supplement to doorstop-sized psychology textbooks Provides psychology students with psychology-specific statistics instruction Includes clear explanations and instruction on performing statistical analysis Teaches students how to analyze their data with SPSS, the most widely used statistical packages among students

A guide to using the power of S-PLUS to perform statistical analyses, providing both an introduction to the program and a course in modern statistical methods. Readers are assumed to have a basic grounding in statistics, thus the book is intended for would-be users, as well as students and researchers using statistics. Throughout, the emphasis is on presenting practical problems and full analyses of real data sets, with many of the methods discussed being modern approaches to topics such as linear and non-linear regression models, robust and smooth regression methods, survival analysis, multivariate analysis, tree-based methods, time series, spatial statistics, and classification. This second edition is intended for users of S-PLUS 3.3, or later, and covers both Windows and UNIX. It treats the recent developments in graphics and new statistical functionality, including bootstraping, mixed effects linear and non-linear models, factor analysis, and regression with autocorrelated errors. The authors have written several software libraries which enhance S-PLUS, and these, plus all the datasets used, are available on the Internet.

The fourth edition of this popular book by Jessica Utts develops statistical literacy and critical thinking through real-world applications, with an emphasis on ideas, not calculations. This text focuses on the key concepts that educated citizens need to know about statistics. These ideas are introduced in interesting applied and real contexts, without using an abundance of technicalities and calculations that only serve to confuse students. NEW for Fall 2020 - Turn your students into statistical thinkers with the Statistical Analysis and Learning Tool (SALT). SALT is an easy-to-use data analysis tool created with the intro-level student in mind. It contains dynamic graphics and allows students to manipulate data sets in order to visualize statistics and gain a deeper conceptual understanding about the meaning behind data. SALT is built by Cengage, comes integrated in Cengage WebAssign Statistics courses and available to use standalone. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.