Getting uncertainty values in linear regression with python

It could be done using statsmodels like this:

import statsmodels.api as sm import numpy as np y=[];x=[] for item in arr: x.append(item[0]) y.append(item[1]) # include constant in ols models, which is not done by default x = sm.add_constant(x) model = sm.OLS(y,x) results = model.fit() 

You could then access the values you require as follows. The intercept and the slope are given by:

results.params # linear coefficients # array([-0.036924 , 0.0021368]) 

I suppose you mean the standard errors when you refer to uncertainty, they can be accessed like this:

results.bse # standard errors of the parameter estimates # array([ 1.03372221e-03, 2.38463106e-05]) 

An overview can be obtained by running

>>> print results.summary() OLS Regression Results ============================================================================== Dep. Variable: y R-squared: 0.997 Model: OLS Adj. R-squared: 0.996 Method: Least Squares F-statistic: 8029. Date: Fri, 26 Sep 2014 Prob (F-statistic): 5.61e-36 Time: 05:47:08 Log-Likelihood: 162.43 No. Observations: 30 AIC: -320.9 Df Residuals: 28 BIC: -318.0 Df Model: 1 Covariance Type: nonrobust ============================================================================== coef std err t P>|t| [95.0% Conf. Int.] ------------------------------------------------------------------------------ const -0.0369 0.001 -35.719 0.000 -0.039 -0.035 x1 0.0021 2.38e-05 89.607 0.000 0.002 0.002 ============================================================================== Omnibus: 7.378 Durbin-Watson: 0.569 Prob(Omnibus): 0.025 Jarque-Bera (JB): 2.079 Skew: 0.048 Prob(JB): 0.354 Kurtosis: 1.714 Cond. No. 220. ============================================================================== Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. 

This might also be of interest for a summary of the properties of the resulting model.

I did not compare to LINESTin Excel. I also don't know if this is possible using only the Google Charts API.