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14.2 Computer software

14.2 Computer software. R CRAN.

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14.2 Computer software

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  1. 14.2 Computer software R CRAN Ross Ihaka is one the originators of R, a software package for statistical computing that has had phenomenal uptake internationally. It can be downloaded free and easily customised for a very wide variety of applications. The package and the paper introducing it have been cited over 1700 times, by far the highest for publications in the mathematical sciences over the last ten years, worldwide. It is now disseminated from over 75 internet sites in 30 countries.

  2. The package is used both for teaching and research by hundreds of universities around the world, including Stanford, Oxford, Cambridge and Berkeley. There are over 40 books written about, or featuring, the use of R. http://www.nytimes.com/2009/01/07/technology/business-computing/07program.html?scp=2&sq=ihaka&st=cse

  3. Time series data. t: time (but could be space) 0  t  T (continuous) t = 0,...,T-1 (discrete) t = 1 ,..., N (unique) value Y(t) Y in R {0,1} Rp N, category, [0, 2)

  4. Useful when there is special period P t=nP+p, p=0,...,P-1 Yn(p)=Y(nP+p)

  5. Vector ts case matplot(type="l" use several line types colors use to force comparison

  6. Tufte (1983). 10th or 11th century movement of planets and sun

  7. Connected. Good for smooth series individual data points not unambiguously displayed irregular sampling can be unclear plot(type="l",

  8. Symbol graph. Good for long term behavior cannot appreciate middle and high frequency behavior irregular sampling can be unclear plot(type="p",...) seals T = 940

  9. Both. Points and line segments plot(type="b",

  10. Both. Points on top of connected plot(type="o",

  11. plot(type="n" points(pch="*"

  12. plot(type="n" lines(type=3

  13. plot(type="h" Good when need to see individual values when series long about centralvalue (next) Not good when strong trend

  14. plot(type="h",...) Amazon T = 32875 Good about central value

  15. Box-Jenkins Model-building strategy. 1. model specification 2. model fitting 3. model diagnostics Scientific method

  16. EDA I. "...three of the main strategies of data analysis are: 1. graphical presentation. 2. provision of flexibility in viewpoint and in facilities, 3. intensive search for parsimony and simplicity ..." II. "In exploratory data analysis there can be no substitute for flexibility; for adapting what is calculated - and what we hope plotted - both to the needs of the situation and the clues that the data have already provided." III. "I would like to convince you that the histogram is old-fashioned ..." IV. "Exploratory data analysis ... does not need probability, significance or confidence." V. "... I hope that I have shown that exploratory data analysis is actively incisive rather than passively descriptive, with real emphasis on the discovery of the unexpected ..." VI. "'exploratory data analysis' is an attitude, a state of flexibility, a willingness to look for those things that we believe are not there, as well as those we believe to be there." VII. "Exploratory data analysis isolates patterns and features of the data and reveals these forcefully to the analyst." VIII. "If we need a short suggestion of what exploratory data analysis is, I would suggest that: 1. it is an attitude, AND 2. a flexibility, AND 3. some graph paper (or transparencies, or both)."

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