On Thursday 06 March 2008 (07:03:34), Prof Brian Ripley wrote:
> The only thing you are adding to earlier replies is incorrect:
>
> fitting by least squares does not imply a normal distribution.
>
Thanks for the clarification, 'implies' is to strong. I should have
written 'suggests' or 'is
On Thu, 6 Mar 2008, Wolfgang Waser wrote:
Thanks for your comments!
Yes. You are fitting by least-squares on two different scales:
differences in y and differences in log(y) are not comparable.
Both are correct solutions to different problems. Since we have no idea
what 'x' and 'y' are, we
Thanks for your comments!
> Yes. You are fitting by least-squares on two different scales:
> differences in y and differences in log(y) are not comparable.
>
> Both are correct solutions to different problems. Since we have no idea
> what 'x' and 'y' are, we cannot even guess which is more appro
The only thing you are adding to earlier replies is incorrect:
fitting by least squares does not imply a normal distribution.
For a regression model, least-squares is in various senses optimal when
the errors are i.i.d. and normal, but it is a reasonable procedure for
many other situati
On 6/03/2008, at 2:53 AM, Wolfgang Waser wrote:
> Dear all,
>
> I did a non-linear least square model fit
>
> y ~ a * x^b
>
> (a) > nls(y ~ a * x^b, start=list(a=1,b=1))
>
> to obtain the coefficients a & b.
>
> I did the same with the linearized formula, including a linear model
>
> log(y) ~ log
On Wed, 5 Mar 2008, Wolfgang Waser wrote:
> Dear all,
>
> I did a non-linear least square model fit
>
> y ~ a * x^b
>
> (a) > nls(y ~ a * x^b, start=list(a=1,b=1))
>
> to obtain the coefficients a & b.
>
> I did the same with the linearized formula, including a linear model
>
> log(y) ~ log(a) + b
On Wednesday 05 March 2008 (14:53:27), Wolfgang Waser wrote:
> Dear all,
>
> I did a non-linear least square model fit
>
> y ~ a * x^b
>
> (a) > nls(y ~ a * x^b, start=list(a=1,b=1))
>
> to obtain the coefficients a & b.
>
> I did the same with the linearized formula, including a linear model
>
> l
Write out the objective functions that they are minimizing and it
will be clear they are different so you can't expect the same
results.
On Wed, Mar 5, 2008 at 8:53 AM, Wolfgang Waser <[EMAIL PROTECTED]> wrote:
> Dear all,
>
> I did a non-linear least square model fit
>
> y ~ a * x^b
>
> (a) > nls
Dear all,
I did a non-linear least square model fit
y ~ a * x^b
(a) > nls(y ~ a * x^b, start=list(a=1,b=1))
to obtain the coefficients a & b.
I did the same with the linearized formula, including a linear model
log(y) ~ log(a) + b * log(x)
(b) > nls(log10(y) ~ log10(a) + b*log10(x), start=l
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