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Using R for Numerical Analysis in Science and Engineering

By: (Author) Victor A. Bloomfield

Manufacture on Demand

Ksh 17,100.00

Format: Hardback or Cased Book

ISBN-10: 143988448X

ISBN-13: 9781439884485

Series: Chapman & Hall/CRC The R Series

Publisher: Taylor & Francis Inc

Imprint: Chapman & Hall/CRC

Country of Manufacture: US

Country of Publication: GB

Publication Date: Apr 24th, 2014

Print length: 360 Pages

Weight: 630 grams

Dimensions (height x width x thickness): 16.20 x 24.10 x 2.30 cms

Product Classification: Mathematical & statistical software

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  • Description

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This practical guide shows how to use R and its add-on packages to obtain numerical solutions to complex mathematical problems commonly faced by scientists and engineers. Providing worked examples and code, the text not only addresses necessary aspects of the R programming language but also demonstrates how to produce useful graphs and statistically analyze and fit data to linear and nonlinear models. It covers Monte Carlo, stochastic, and deterministic methods and explores topics such as numerical differentiation and integration, interpolation and curve fitting, and optimization.

Instead of presenting the standard theoretical treatments that underlie the various numerical methods used by scientists and engineers, Using R for Numerical Analysis in Science and Engineering shows how to use R and its add-on packages to obtain numerical solutions to the complex mathematical problems commonly faced by scientists and engineers. This practical guide to the capabilities of R demonstrates Monte Carlo, stochastic, deterministic, and other numerical methods through an abundance of worked examples and code, covering the solution of systems of linear algebraic equations and nonlinear equations as well as ordinary differential equations and partial differential equations. It not only shows how to use R’s powerful graphic tools to construct the types of plots most useful in scientific and engineering work, but also:

  • Explains how to statistically analyze and fit data to linear and nonlinear models
  • Explores numerical differentiation, integration, and optimization
  • Describes how to find eigenvalues and eigenfunctions
  • Discusses interpolation and curve fitting
  • Considers the analysis of time series

Using R for Numerical Analysis in Science and Engineering

provides a solid introduction to the most useful numerical methods for scientific and engineering data analysis using R.


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