Difference between revisions of "Analyze data with the Statistics software R"
From JSXGraph Wiki
Line 92: | Line 92: | ||
var est1 = a[4]*1.0; | var est1 = a[4]*1.0; | ||
var est2 = a[5]*1.0; | var est2 = a[5]*1.0; | ||
+ | |||
+ | console.log(t); | ||
if (!graph2) { | if (!graph2) { | ||
graph2 = brd.createElement('curve', [[x[0],x[x.length-1]],[m,m]], {strokecolor:'red'}); | graph2 = brd.createElement('curve', [[x[0],x[x.length-1]],[m,m]], {strokecolor:'red'}); |
Revision as of 11:32, 21 February 2013
Contents
Normal Location and Scale
This litte application sends the y-coordinates of the points which are normal distributed (pseudo-)random numbers to the server.
There, location and scale of the sample are estimated using the Statistics software R.
The return values are plotted and displayed.
The computed estimates are:
- mean, standard deviation: red (non-robust!)
- median and MAD: black (most-robust!)
- radius-minimax estimator: green (optimally robust; cf. Rieder et al. (2008))
By changing the y-position of the four movable points you should recognize the instability (non-robustness) of mean and standard deviation in contrast to the robust estimates; e.g., move one of the four movable points to the top of the plot.
Online results:
Statistics:<br>
The underlying source code
The underlying JavaScript and PHP code
The R script can be downloaded here.
References
- The Costs of not Knowing the Radius, Helmut Rieder, Matthias Kohl and Peter Ruckdeschel, Statistical Methods and Application 2008 Feb; 17(1): p.13-40; cf. also [1] for an extended version.
- Robust Asymptotic Statistics, Helmut Rieder, Springer, 1994.
- Numerical Contributions to the Asymptotic Theory of Robustness, Matthias Kohl, PhD-Thesis, University of Bayreuth, 2005; cf. also [2].