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.