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Learning Base R
R is an open source programming language and interactive programming environment that has become the software tool of choice in data analytics. Learning Base R provides an introduction to the language for those with and without prior programming experience. It introduces the key topics that you will need to begin analyzing data and programming in R. The focus here is on the R language rather than a particular application. Nearly 200 exercises allow you to assess your understanding of R.
Probability
This calculus-based introduction to probability covers all of the traditional topics, along with a secondary emphasis on Monte Carlo simulation. Examples that introduce applications from a wide range of fields help the reader apply probability theory to real-world problems. The text covers all of the topics associated with Exam P given by the Society of Actuaries. Over 100 figures highlight the intuitive and geometric aspects of probability. Over 800 exercises are used to reinforce concepts and make this text appropriate for classroom use.
Reliability
This text provides an elementary introduction to the probabilistic models and statistical methods used by reliability engineers that are applied to a system of components. Probability models include the exponential distribution, Weibull distribution, competing risks, mixtures, accelerated life model, proportional hazards model, and repairable systems models. Statistical methods emphasize determining point and interval estimates for parameters from censored data sets. Applications are drawn from a variety of disciplines. Over 600 exercises make this text appropriate for a class on reliability.
R - FOR BASIC AND APPLIED SCIENCES
R is a Statistcal programming language. R is Free and open source. R is an interpreted language not a compiled one. The R programming environment contains the range of tools for parallel computing, machine and deep learning and for working with big Data, including Torch and Tensar flow facilitating construction and implementation of neural networks. The Bioconductor repository contains over a thousand of software packages written in R for analyzing data sets from CDNA microarrays to copy-number variation and epigenomics (Robert Gentleman-Sorin Draghicia). Due to Data Handling and Modeling capabilities and its flexibility, R is becoming the most widely used software in bioinformatics. The R p...
Discrete-event Simulation
CONTENIDO: Models - Random-number generation - Discrete-event simulation - Statistics - Next-event simulation - Discrete random variables - Continuous random variables - Output analysis - Input modeling - Projects.
Probability
This calculus-based introduction to probability covers all of the traditional topics, along with a secondary emphasis on Monte Carlo simulation. Examples that introduce applications from a wide range of fields help the reader apply probability theory to real-world problems. More than 100 figures highlight the intuitive and geometric aspects of probability, and in excess of 500 exercises are used to reinforce concepts and make this text appropriate for classroom use.
The R Book
The high-level language of R is recognized as one of the mostpowerful and flexible statistical software environments, and israpidly becoming the standard setting for quantitative analysis,statistics and graphics. R provides free access to unrivalledcoverage and cutting-edge applications, enabling the user to applynumerous statistical methods ranging from simple regression to timeseries or multivariate analysis. Building on the success of the author’s bestsellingStatistics: An Introduction using R, The R Book ispacked with worked examples, providing an all inclusive guide to R,ideal for novice and more accomplished users alike. The bookassumes no background in statistics or computing and in...
Benford's Law
Benford's law states that the leading digits of many data sets are not uniformly distributed from one through nine, but rather exhibit a profound bias. This bias is evident in everything from electricity bills and street addresses to stock prices, population numbers, mortality rates, and the lengths of rivers. Here, Steven Miller brings together many of the world’s leading experts on Benford’s law to demonstrate the many useful techniques that arise from the law, show how truly multidisciplinary it is, and encourage collaboration. Beginning with the general theory, the contributors explain the prevalence of the bias, highlighting explanations for when systems should and should not follow...
Probability and Statistics with Applications: A Problem Solving Text
This text is listed on the Course of Reading for SOA Exam P. Probability and Statistics with Applications is an introductory textbook designed to make the subject accessible to college freshmen and sophomores concurrent with Calc II and III, with a prerequisite of just one smester of calculus. It is organized specifically to meet the needs of students who are preparing for the Society of Actuaries qualifying Examination P and Casualty Actuarial Society's new Exam S. Sample actuarial exam problems are integrated throughout the text along with an abundance of illustrative examples and 870 exercises. The book provides the content to serve as the primary text for a standard two-semester advanced...
Mathematical Statistics
Mathematical Statistics describes the mathematics behind the modern practice of statistics. The book covers random sampling, point estimation, interval estimation, and hypothesis testing. The pre-requisite for the text is a course in calculus-based probability.
