Scipy linregress function erroneous standard error return?

You could try the statsmodels package from scikits: In 37: import scikits. Statsmodels as sm In 38: x = 5.05, 6.75, 3.21, 2.66 In 39: y = 1.65, 26.5, -5.93, 7.96 In 40: X = sm. Add_constant(x) # intercept In 41: model = sm.

OLS(y, X) In 42: fit = model.fit() In 43: fit. Params Out43: array( 5.39357736, -16.28112799) In 44: fit. Rsquared Out44: 0.52480627513624789 In 45: np.

Sqrt(fit. Mse_resid) Out45: 11.696414461570097.

Brilliant. Thanks ars. Exactly what I needed.

– Thomas Browne Jan 11 '10 at 20:07 Glad to help. :) – ars Jan 11 '10 at 22:36.

I've just been informed by the SciPy user group that the std_err here represents the standard error of the gradient line, not the standard error of the predicted y's, as per Excel. Nevertheless users of this function should be careful, because this was not always the behaviour of this library - it used to output exactly as Excel, and the changeover appears to have occurred in the past few months. Anyway still looking for an equivalent to STEYX in Python.

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