Get your own Python server

                            OLS Regression Results
==============================================================================
Dep. Variable:        Calorie_Burnage   R-squared:                       0.000
Model:                            OLS   Adj. R-squared:                 -0.006
Method:                 Least Squares   F-statistic:                   0.04975
Date:                Tue, 24 Nov 2020   Prob (F-statistic):              0.824
Time:                        12:26:11   Log-Likelihood:                -1145.8
No. Observations:                 163   AIC:                             2296.
Df Residuals:                     161   BIC:                             2302.
Df Model:                           1
Covariance Type:            nonrobust
=================================================================================
                    coef    std err          t      P>|t|      [0.025      0.975]
---------------------------------------------------------------------------------
Intercept       346.8662    160.615      2.160      0.032      29.682     664.050
Average_Pulse     0.3296      1.478      0.223      0.824      -2.588       3.247
==============================================================================
Omnibus:                      124.542   Durbin-Watson:                   1.620
Prob(Omnibus):                  0.000   Jarque-Bera (JB):              938.541
Skew:                           2.927   Prob(JB):                    1.58e-204
Kurtosis:                      13.195   Cond. No.                         811.
==============================================================================