[SciPy-user] linear regression

[Skipper Seabold]

On Wed, May 27, 2009 at 11:59 AM, Skipper Seabold <jsseabold at gmail.com> wrote:
> On Wed, May 27, 2009 at 11:54 AM, ms <devicerandom at gmail.com> wrote:
>> josef.pktd at gmail.com ha scritto:
>>> On Wed, May 27, 2009 at 10:05 AM, ms <devicerandom at gmail.com> wrote:
>>>> jason-sage at creativetrax.com ha scritto:
>>>>> Is there a recommended way now of calculating the slope of a linear
>>>>> regression?  Using the scipy.stats.linregress function gives a
>>>>> deprecation warning, apparently because that function uses the
>>>>> scipy.mean function:
>>>> I think you can use polyfit for doing linear regression, isn't it?
>>>
>>> but you don't get the slope coefficient and the standard errors, if
>>> you want more than just prediction.
>>
>> You mean the correlation coefficient? This is numpy.corrcoef() or
>> something like that.
>
> He means that polyfit does not provide the Betas in a linear fit of,
> for example, y = Beta * x + Beta2 * x**2 and their associated standard
> errors.  It will only give you the predictions (ie., Y-hats) for your
> data based on the fit.

Err, sorry I don't think this isn't right for polyfit after having a
look.  One day I will learn to look before I leap...

Have a look here <http://www.scipy.org/Cookbook/LinearRegression>