Analyze data with the Statistics software R: Difference between revisions

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                 y[6+i] = 10+z2*3;
                 y[6+i] = 10+z2*3;
                 x[6+i] = 6+i;
                 x[6+i] = 6+i;
                 p[i] = brd.createElement('point', [x[i],y[i]],{name:' ',fixed:true,style:7});  
                 p[i] = brd.createElement('point', [x[i],y[i]],{name:' ',fixed:true,size:2,face:'[]'});  
                 p[6+i] = brd.createElement('point', [x[6+i],y[6+i]],{name:' ',fixed:true,style:7});
                 p[6+i] = brd.createElement('point', [x[6+i],y[6+i]],{name:' ',fixed:true,size:2,face:'[]'});
             }else{
             }else{
                 y[i] = 10+z1*3;
                 y[i] = 10+z1*3;
                 p[i] = brd.createElement('point', [x[i],y[i]],{name:' ',fixed:true,style:7});  
                 p[i] = brd.createElement('point', [x[i],y[i]],{name:' ',fixed:true,size:2,face:'[]'});  
             }
             }
         }
         }
Line 61: Line 61:
         brd.removeObject(p[9]);
         brd.removeObject(p[9]);
          
          
         p[0] = brd.createElement('glider', [x[0],y[0],l[0]],{name:' ',style:6});  
         p[0] = brd.createElement('glider', [x[0],y[0],l[0]],{name:' ',size:4,face:'o'});  
         p[10] = brd.createElement('glider', [x[10],y[10],l[1]],{name:' ',style:6});  
         p[10] = brd.createElement('glider', [x[10],y[10],l[1]],{name:' ',size:4,face:'o'});  
         p[8] = brd.createElement('glider', [x[8],y[8],l[2]],{name:' ',style:6});  
         p[8] = brd.createElement('glider', [x[8],y[8],l[2]],{name:' ',size:4,face:'o'});  
         p[9] = brd.createElement('glider', [x[9],y[9],l[3]],{name:' ',style:6});  
         p[9] = brd.createElement('glider', [x[9],y[9],l[3]],{name:' ',size:4,face:'o'});  
         brd.unsuspendUpdate();
         brd.unsuspendUpdate();


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                 }});
                 }});
         }
         }
brd.addHook(doIt, 'mouseup');
</jsxgraph>
</jsxgraph>


Revision as of 09:16, 7 June 2011

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].

External links

Retrieved from "http://jsxgraph.org/wiki/index.php?title=Analyze_data_with_the_Statistics_software_R&oldid=5119"