# [SciPy-User] [SciPy-user] Covariance matrix

suzana8447 k-assem84@hotmail....
Wed Feb 15 07:36:59 CST 2012

```Thanks all for your  help.

What I have understood is that I get what so called the cov_x from least
square root fit and then multipy this matrix by the error variance.

I have two more questions.

1) What is meant by the  error variance? How one can extract it?

2) Do you mean by ||err||= func-data?

Charles R Harris wrote:
>
> On Mon, Feb 13, 2012 at 8:56 AM, <josef.pktd@gmail.com> wrote:
>
>> On Sat, Feb 11, 2012 at 7:39 PM, Kevin Gullikson
>> <kevin.gullikson@gmail.com> wrote:
>> > Use full_output=True when you call leastq, and you will get a matrix
>> (among
>> > other things). If you multiply that matrix by the standard deviation of
>> the
>> > residuals, it will be the covariance matrix.
>>
>> As Charles pointed out, multiply by the error variance not the
>> standard deviation. Docstring is wrong in this.
>>
>>
> Even more precisely, multiply by ||err||^2/(n - dof), since it is possible
> that the error has an offset unless the model can perfectly fit a
> constant.
> If this actually makes a difference, the model is inadequate, but the
> variance estimate might be useful if you are using something like the
> Akaike information criterion to choose the number of parameters.
>
> <snip>
>
> Chuck
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User@scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
>

--
View this message in context: http://old.nabble.com/Covariance-matrix-tp33301423p33328834.html
Sent from the Scipy-User mailing list archive at Nabble.com.

```