[SciPy-User] StdErr Problem with Gary Strangman's linregress function

Bruce Southey bsouthey@gmail....
Sun Jan 10 19:21:17 CST 2010


On Sun, Jan 10, 2010 at 3:35 PM, <totalbull@mac.com> wrote:

>
> Hello, Excel and scipy.stats.linregress are disagreeing on the standard
> error of a regression.
>
> I need to find the standard errors of a bunch of regressions, and prefer to
> use pure Python than RPy. So I am going to scipy.stats.linregress, as
> advised at:
>
> http://www2.warwick.ac.uk/fac/sci/moac/currentstudents/peter_cock/python/lin_reg/#linregress
>
> from scipy import stats
>
> x = [5.05, 6.75, 3.21, 2.66]
>
> y = [1.65, 26.5, -5.93, 7.96]
>
> gradient, intercept, r_value, p_value, std_err = stats.linregress(x,y)
>
> gradient
>
> 5.3935773611970186
>
> intercept
>
> -16.281127993087829
>
> r_value
>
> 0.72443514211849758
>
> r_value**2
>
> 0.52480627513624778
>
> std_err
>
> 3.6290901222878866
>
>
> The problem is that the std error calculation does not agree with what is
> returned in Microsoft Excel's STEYX function (whereas all the other output
> does). From Excel:
>
>
>
>
> Anybody knows what's going on? Any alternative way of getting the standard
> error without going to R?
>
>
>
>
> _______________________________________________
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>
>
The Excel help is rather cryptic by   :"Returns the standard error of the
predicted y-value for each x in the regression. The standard error is a
measure of the amount of error in the prediction of y for an individual x."
But clearly this is not the same as the standard error of the 'gradient'
(slope) returned by linregress. Without checking the formula, STEYX appears
returns the square root what most people call the mean square error (MSE).

Bruce
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