• Title: Introduction to Probability and Statistics Using R
• Author(s) G. Jay Kerns
• Publisher: Lulu.com (September 27, 2011)
• Paperback: 386 pages
• eBook: PDF (412 pages, 1.3 MB)
• Language: English
• ISBN-10: 0557249791
• ISBN-13: 978-0557249794
• Share This:  

Book Description

This is a textbook for an undergraduate course in probability and statistics. The approximate prerequisites are two or three semesters of calculus and some linear algebra. Students attending the class include mathematics, engineering, and computer science majors.

• N/A

• Amazon

• The R Programming Language
• Statistics, and SAS Programming
• Probability, Stochastic Process, Queueing Theory, etc.
• Data Analysis and Data Mining
• Geographic Information System (GIS) and Web Mapping

• Introduction to Probability and Statistics Using R (G. Jay Kerns)
• The Mirror Site (1) - PDF
• The Mirror Site (2) - PDF
• The Mirror Site (3)

• Introduction to Probability, Statistics, and Random Processes

  This book introduces students to probability, statistics, and stochastic processes. It provides a clear and intuitive approach to these topics while maintaining mathematical accuracy. The book contains a large number of solved exercises.

• Introduction to Probability for Data Science (Stanley Chan)

  This book is an introductory textbook in undergraduate probability in the context of data science to emphasize the inseparability between data (computing) and probability (theory) in our time, with examples in both MATLAB and Python.

• Probability Theory: The Logic of Science (E. T. Jaynes)

  Going beyond the conventional mathematics of probability theory, this study views the subject in a wider context. It discusses new results, along with applications of probability theory to a variety of problems.

• Foundations of Constructive Probability Theory (Yuen-Kwok Chan)

  This book provides a systematic and general theory of probability within the framework of Constructive Mathematics. It can serve as a parallel introduction into constructive mathematics and rigorous probability theory.

• Introduction to Random Matrices: Theory and Practice

  This is a book for absolute beginners. The aim is to provide a truly accessible introductory account of Random Matrix theory. Most chapters are accompanied by MATLAB codes to guide readers through the numerical check of most analytical results.

• Probability and Mathematical Statistics (Prasanna Sahoo)

  This book presents an introduction to probability and mathematical statistics for students already having some elementary mathematical background. It blends proven coverage with new innovations to ensure you gain a solid understanding of statistics.

• Elementary Probability for Applications (Rick Durrett)

  This clear and lively introduction to probability theory concentrates on the results that are the most useful for applications, including combinatorial probability and Markov chains. Concise and focused, it is designed for students familiar with basic calculus.

• Essentials of Stochastic Processes (Rick Durrett)

  Stochastic processes have become important for many fields, including mathematical finance and engineering. Written by one of the worlds leading probabilists, this book presents recent results previously available only in specialized monographs.