Intro Stat with Randomization and Simulation, 1st Edition

This textbook takes a different approach, where the foundations for inference are provided using randomization and simulation methods. Once a solid foundation is formed, a transition is made to traditional approaches.

**Tag(s):**
Statistics

**Publication date**: 18 Jul 2014

**ISBN-10**:
1500576697

**ISBN-13**:
9781500576691

**Paperback**:
354 pages

**Views**: 1,919

**Type**: Textbook

**Publisher**:
CreateSpace Independent Publishing Platform

**License**:
Creative Commons Attribution-ShareAlike 3.0 Unported

**Post time**: 14 Dec 2016 08:00:00

Intro Stat with Randomization and Simulation, 1st Edition

This textbook takes a different approach, where the foundations for inference are provided using randomization and simulation methods. Once a solid foundation is formed, a transition is made to traditional approaches.

You are free to:

Share — copy and redistribute the material in any medium or format

Adapt — remix, transform, and build upon the material for any purpose, even commercially.

The licensor cannot revoke these freedoms as long as you follow the license terms.

Click**here** to read the full license.

Share — copy and redistribute the material in any medium or format

Adapt — remix, transform, and build upon the material for any purpose, even commercially.

The licensor cannot revoke these freedoms as long as you follow the license terms.

Click

From the Book Description:

Table of Contents:

Chapter 1: Introduction to data.

Chapter 2: Foundations for inference.

Chapter 3: Inference for categorical data

Chapter 4: Inference for numerical data.

Chapter 5: Introduction to linear regression.

Chapter 6: Multiple and logistic regression.

More Resources:

Barr, Diez, and Rundel wrote:This textbook may be downloaded as a free PDF on the project's website, and the paperback is sold royalty-free. OpenIntro develops free textbooks and course resources for introductory statistics that exceeds the quality standards of traditional textbooks and resources, and that maximizes accessibility options for the typical student. The approach taken in this textbooks differs from OpenIntro Statistics in its introduction to inference. The foundations for inference are provided using randomization and simulation methods. Once a solid foundation is formed, a transition is made to traditional approaches, where the normal and t distributions are used for hypothesis testing and the construction of confidence intervals.

Table of Contents:

Chapter 1: Introduction to data.

Chapter 2: Foundations for inference.

Chapter 3: Inference for categorical data

Chapter 4: Inference for numerical data.

Chapter 5: Introduction to linear regression.

Chapter 6: Multiple and logistic regression.

More Resources:

Tweet

About The Author(s)

Christopher D. Barr was a Research Assistant Professor, in the Texas Institute for Measurement, Evaluation, and Statistics (TIMES), at the University of Houston. He now works as an Analyst at Varadero Capital. His research interests include application of multilevel and latent variable modelling techniques for studying nested data structures, particularly in the area of teacher and school level effects on students’ educational outcomes.

Mine Çetinkaya-Rundel is the Director of Undergraduate Studies and an Assistant Professor of the Practice in the Department of Statistical Science at Duke University. Her research focuses on innovation in statistics pedagogy, with an emphasis on student-centered learning, computation, reproducible research, and open-source education.

Book Categories

Computer Science
Introduction to Computer Science
Introduction to Computer Programming
Algorithms and Data Structures
Artificial Intelligence
Computer Vision
Machine Learning
Neural Networks
Game Development and Multimedia
Data Communication and Networks
Coding Theory
Computer Security
Information Security
Cryptography
Information Theory
Computer Organization and Architecture
Operating Systems
Image Processing
Parallel Computing
Concurrent Programming
Relational Database
Document-oriented Database
Data Mining
Big Data
Data Science
Digital Libraries
Compiler Design and Construction
Functional Programming
Logic Programming
Object Oriented Programming
Formal Methods
Software Engineering
Agile Software Development
Information Systems
Geographic Information System (GIS)

Mathematics
Mathematics
Algebra
Abstract Algebra
Linear Algebra
Number Theory
Numerical Methods
Precalculus
Calculus
Differential Equations
Category Theory
Proofs
Discrete Mathematics
Theory of Computation
Graph Theory
Real Analysis
Complex Analysis
Probability
Statistics
Game Theory
Queueing Theory
Operations Research
Computer Aided Mathematics

Supporting Fields
Web Design and Development
Mobile App Design and Development
System Administration
Cloud Computing
Electric Circuits
Embedded System
Signal Processing
Integration and Automation
Network Science
Project Management

Operating System
Programming/Scripting
Ada
Assembly
C / C++
Common Lisp
Forth
Java
JavaScript
Lua
Microsoft .NET
Rexx
Perl
PHP
Python
R
Rebol
Ruby
Scheme
Tcl/Tk

Miscellaneous
Sponsors