Statistics and Data with R: An Applied Approach Through Examples


R, an Open Source software, has become the de facto statistical computing environment. It has an excellent collection of data manipulation and graphics capabilities. It is extensible and comes with a large number of packages that allow statistical analysis at all levels – from simple to advanced – and in numerous fields including Medicine, Genetics, Biology, Environmental Sciences, Geology, Social Sciences and much more. The software is maintained and developed by academicians and professionals and as such, is continuously evolving and up to date. Statistics and Data with R presents an accessible guide to data manipulations, statistical analysis and graphics using R.

Assuming no previous knowledge of statistics or R, the book includes:

  • A comprehensive introduction to the R language.
  • An integrated approach to importing and preparing data for analysis, exploring and analyzing the data, and presenting results.
  • Over 300 examples, including detailed explanations of the R scripts used throughout.
  • Over 100 moderately large data sets from disciplines ranging from Biology, Ecology and Environmental Science to Medicine, Law, Military and Social Sciences.
  • A parallel discussion of analyses with the normal density, proportions (binomial), counts (Poisson) and bootstrap methods.
  • Two extensive indexes that include references to every R function (and its arguments and packages used in the book) and to every introduced concept.

An accompanying Wiki website, includes all the scripts and data used in the book. The website also features a solutions manual, providing answers to all of the exercises presented in the book. Visitors are invited to download/upload data and scripts and share comments, suggestions and questions with other visitors. Students, researchers and practitioners will find this to be both a valuable learning resource in statistics and R and an excellent reference book.

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Published on
Nov 20, 2008
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Mathematics / Probability & Statistics / Regression Analysis
Mathematics / Probability & Statistics / Stochastic Processes
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Control theory can be roughly classified as deterministic or stochastic. Each of these can further be subdivided into game theory and optimal control theory. The central problem of control theory is the so called constrained maximization (which- with slight modifications--is equivalent to minimization). One can then say, heuristically, that the major problem of control theory is to find the maximum of some performance criterion (or criteria), given a set of constraints. The starting point is, of course, a mathematical representation of the performance criterion (or criteria)- sometimes called the objective functional--along with the constraints. When the objective functional is single valued (Le. , when there is only one objective to be maximized), then one is dealing with optimal control theory. When more than one objective is involved, and the objectives are generally incompatible, then one is dealing with game theory. The first paper deals with stochastic optimal control, using the dynamic programming approach. The next two papers deal with deterministic optimal control, and the final two deal with applications of game theory to ecological problems. In his contribution, Dr. Marc Mangel applies the dynamic proQramming approach, as modified by his recent work--with Dr. Colin Clark, from the University of British Columbia (Mangel and Clark 1987}*--to modelling the "behavioral decisions" of insects. The objective functional is a measure of fitness. Readers interested in detailed development of the subject matter may consult Mangel (1985). My contributions deal with two applications of optimal control theory.
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