sturlamolden wrote:
> robert wrote:
>
>> Think of such example: A drunken (x,y) 2D walker is supposed to walk along a
>> diagonal, but he makes frequent and unpredictable pauses/slow motion. You
>> get x,y coordinates in 1 per second. His speed and time pattern at all do
>> not matter - you jus
robert wrote:
> Think of such example: A drunken (x,y) 2D walker is supposed to walk along a
> diagonal, but he makes frequent and unpredictable pauses/slow motion. You get
> x,y coordinates in 1 per second. His speed and time pattern at all do not
> matter - you just want to know how well he
sturlamolden wrote:
> robert wrote:
>
>> here the bootstrap test will as well tell us, that the confidence intervall
>> narrows down by a factor ~sqrt(10) - just the same as if there would be
>> 10-fold more of well distributed "new" data. Thus this kind of error
>> estimation has no reasonable
robert wrote:
> here the bootstrap test will as well tell us, that the confidence intervall
> narrows down by a factor ~sqrt(10) - just the same as if there would be
> 10-fold more of well distributed "new" data. Thus this kind of error
> estimation has no reasonable basis for data which is no
sturlamolden wrote:
> robert wrote:
>
>>> t = r * sqrt( (n-2)/(1-r**2) )
>
>> yet too lazy/practical for digging these things from there. You obviously
>> got it - out of that, what would be a final estimate for an error range of r
>> (n big) ?
>> that same "const. * (1-r**2)/sqrt(n)" which I f
