• Title: Answering Questions with Data : Introductory Statistics
• Author(s) Matthew J.C. Crump, Danielle Navarro and Jeffrey Suzuki
• Publisher: Open Library; eBook (Creative Commons Licensed)
• License(s): Creative Commons License (CC)
• Paperback: N/A
• eBook: HTML and PDF (391 pages)
• Language: English
• ISBN-10: N/A
• ISBN-13: N/A
• Share This:  

Book Description

This is a free textbook teaching introductory statistics for undergraduates. This textbook is part of a larger OER course package for teaching undergraduate statistics, including this textbook, a lab manual, and a course website. All of the materials are free and copiable, with source code maintained in Github repositories.

It provides broad coverage, limited theory, clear explanations, plenty of practice opportunities, and examples that engage today's students! Students will learn to select an appropriate data analysis technique, carry out the analysis, and draw appropriate conclusions.

• Matthew Crump is an Associate Professor Department of Psychology Brooklyn College of CUNY.

• Amazon

• Statistics, Mathematical Statistics, and SAS Programming
• Probability, Stochastic Process, Queueing Theory, etc.
• Data Processing, Data Analysis and Data Mining

• Answering Questions with Data : Introductory Statistics (Matthew Crump, et al.)
• The Mirror Site (1) - HTML and PDF

• R for Statistical Modelling and Computing (Petra Kuhnert, et al.)

  An excellent tutorial on the R language, providing examples that illustrate programming concepts in the context of practical statistical modelling and computational problems. Understanding of quantitative methods and apply to real world apps.

• Modern Statistics with R: Wrangling, Inference and Predicting

  The aim of the book is to introduce you to key parts of the modern statistical toolkit. It teaches you: - Data wrangling - importing, formatting, reshaping, merging, and filtering data in R.

• Introduction to Statistics and Data Analysis: A Case-Based Approach

  This short book is a complete introduction to statistics and data analysis using R and RStudio. It contains hands-on exercises with real data - mostly from social sciences. It presents four key ingredients of statistical data analysis.

• Introduction to Statistics and Data Analysis (Geoffrey M. Boynton)

  Build a solid foundation in data analysis, This guide starts with an overview of statistics and why it is so important. Be confident that you understand what your data are telling you and that you can explain the results to others!

• Discovering Statistics Using R (Andy Field, et al.)

  This book takes readers on a journey of statistical discovery using the freeware R, and is written in an irreverent style and follows the same ground breaking structure and pedagogical approach.

• Bootstrap Methods and Applications to R (A. C. Davison, et al.)

  This book provides a compact introduction to the Bootstrap Method. It is motivated by practical examples and the implementations of the corresponding algorithms are always given directly in R in a comprehensible form.

• 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 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 Inference for Data Science (Brian Caffo)

  The book gives a rigorous treatment of the elementary concepts in statistical inference from a classical frequentist perspective. The ideal readers are quantitatively literate and have a basic understanding of statistical concepts and R programming.

• Statistical Analysis of Networks (Konstantin Avrachenkov, et al.)

  This book is a general introduction to the statistical analysis of networks. Numerous fundamental tools and concepts needed for the analysis of networks are presented, such as network modeling, community detection, etc.

• Statistical Foundations of Actuarial Learning and its Applications

  This open access book discusses the statistical modeling of insurance problems. It presents the mathematical foundations behind these fundamental statistical concepts and how they can be applied in daily actuarial practice.

• Explanatory Model Analysis: Explore, Explain, Examine Models

  This book presents a collection of model agnostic methods that may be used for any black-box model together with real-world applications to classification and regression problems. With examples in R and Python.