• Title: Introduction to Statistical Thinking (With R, Without Calculus)
• Author(s) Benjamin Yakir
• Publisher: CreateSpace (September 19, 2014); The Hebrew University of Jerusalem (June, 2011)
• Paperback: 324 pages
• eBook: PDF, 324 page, 4.28 MB
• Language: English
• ISBN-10: 1502424665
• ISBN-13: 978-1502424662
• Share This:  

Book Description

This is an introduction to statistics, with R, without calculus. The target audience for this book is college students who are required to learn statistics, students with little background in mathematics and often no motivation to learn more.

It shows you how to derive actionable conclusions from data analysis, solve real problems, and improve real processes. Here, you'll discover how to implement statistical thinking and methodology in your work to improve business performance.

• Benjamin Yakir is a Professor of Statistics at The Hebrew University of Jerusalem.

• Amazon

• Statistics and Mathematical Statistics
• The R Programming Language
• Probability, Stochastic Process, Queueing Theory, etc.
• Applied Mathematics

• Introduction to Statistical Thinking (With R, Without Calculus) by Benjamin Yakir
• The Mirror Site (1) - PDF
• The Mirror Site (2) - PDF

• Introduction to Statistical Thinking (Benjamin Yakir)

  This book offers a detailed, illustrated breakdown of the fundamentals of statistics. Develop and use formal logical thinking abilities to understand the message behind numbers and charts in science, politics, and economy.

• Statistical Thinking for the 21st Century (Russell A. Poldrack)

  Statistical thinking is increasingly essential to making informed decisions based on uncertain data. This book provides the tools to describe complex patterns that emerge from data and to make accurate predictions and decisions based on data.

• Introduction to Statistical Thought (Michael Lavine)

  The book is intended as an upper level undergraduate or introductory graduate textbook in statistical thinking with a likelihood emphasis for students with a good knowledge of calculus and the ability to think abstractly.

• Applied Statistics with R (David Dalpiaz)

  This book provides an integrated treatment of statistical inference techniques in data science using the R Statistical Software. It provides a much-needed, easy-to-follow introduction to statistics and the R programming language.

• Learning Statistics with R (Daniel Navarro)

  This book takes you on a guided tour of software development with R, from basic types and data structures to advanced topics like closures, recursion, and anonymous functions. No statistical knowledge is required.

• R Cookbook: Recipes for Data Analysis, Statistics, and Graphics

  This book is full of how-to recipes, each of which solves a specific problem. Each recipe includes a quick introduction to the solution followed by a discussion that aims to unpack the solution and give you some insight into how it works.

• Doing Data Science in R: An Introduction for Social Scientists

  This approachable introduction to doing data science in R provides step-by-step advice on using the tools and statistical methods to carry out data analysis. It builds knowledge and skills gradually.

• Beyond Multiple Linear Regression: Linear & Multilevel Models in R

  This book is designed for undergraduate students who have successfully completed a multiple linear regression course, helping them develop an expanded modeling toolkit that includes non-normal responses and correlated structure.

• Introduction to Statistical Data Analysis with R (Matthias Kohl)

  The book offers an introduction to statistical data analysis applying the free statistical software R, probably the most powerful statistical software today. The analyses are performed and discussed using real data.

• R for Data Science: Visualize, Model, Transform, Tidy, Import

  This book teaches you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, visualize it and model it, how data science can help you work with the uncertainty and capture the opportunities.

• Mastering Software Development in R (Roger D. Peng, et al.)

  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.

• Hands-On Programming with R: Functions and Simulations

  This book not only teaches you how to program, but also shows you how to get more from R than just visualizing and modeling data. You’ll gain valuable programming skills and support your work as a data scientist at the same time.

• R Packages: Organize, Test, Document, and Share Your Code

  Turn your R code into packages that others can easily download and use. This practical book shows you how to bundle reusable R functions, sample data, and documentation together by applying author's package development philosophy.