Introduction to Randomness and Statistics

Small book cover: Introduction to Randomness and Statistics

Introduction to Randomness and Statistics

Publisher: arXiv
Number of pages: 95

This text provides a practical introduction to randomness and data analysis, in particular in the context of computer simulations. At the beginning, the most basics concepts of probability are given, in particular discrete and continuous random variables. The text is basically self-contained, comes with several example C programs and contains eight practical exercises.

Home page url

Download or read it online for free here:
Download link
(2.4MB, PDF)

Similar books

Book cover: Applied Nonparametric RegressionApplied Nonparametric Regression
by - Cambridge University Press
Nonparametric regression analysis has become central to economic theory. Hardle, by writing the first comprehensive and accessible book on the subject, contributed enormously to making nonparametric regression equally central to econometric practice.
Book cover: Think Stats: Probability and Statistics for ProgrammersThink Stats: Probability and Statistics for Programmers
by - Green Tea Press
Think Stats is an introduction to Probability and Statistics for Python programmers. This new book emphasizes simple techniques you can use to explore real data sets and answer interesting statistical questions. Basic skills in Python are assumed.
Book cover: Principles of Data AnalysisPrinciples of Data Analysis
by - Prasenjit Saha
This is a short book about the principles of data analysis. The emphasis is on why things are done rather than on exactly how to do them. If you already know something about the subject, then working through this book will deepen your understanding.
Book cover: Reversible Markov Chains and Random Walks on GraphsReversible Markov Chains and Random Walks on Graphs
by - University of California, Berkeley
From the table of contents: General Markov Chains; Reversible Markov Chains; Hitting and Convergence Time, and Flow Rate, Parameters for Reversible Markov Chains; Special Graphs and Trees; Cover Times; Symmetric Graphs and Chains; etc.