Как бы я нарисовал свои результаты линейной регрессии для этой линейной регрессии, которую я сделал из панд?

import pandas as pd
from pandas.stats.api import ols

df = pd.read_csv('Samples.csv', index_col=0)
control = ols(y=df['Control'], x=df['Day'])
one = ols(y=df['Sample1'], x=df['Day'])
two = ols(y=df['Sample2'], x=df['Day'])

Я старалсяplot() но это не сработало. Я хочу нанести все три примера на один график. Есть ли какой-либо код панды или код matplotlib для записи данных в формате этих сводок?

В любом случае результаты выглядят так:

контроль

------------------------Summary of Regression Analysis-------------------------

Formula: Y ~ <x> + <intercept>

Number of Observations:         7
Number of Degrees of Freedom:   2

R-squared:         0.5642
Adj R-squared:     0.4770

Rmse:              4.6893

F-stat (1, 5):     6.4719, p-value:     0.0516

Degrees of Freedom: model 1, resid 5

-----------------------Summary of Estimated Coefficients------------------------
      Variable       Coef    Std Err     t-stat    p-value    CI 2.5%   CI 97.5%
--------------------------------------------------------------------------------
             x    -0.4777     0.1878      -2.54     0.0516    -0.8457    -0.1097
     intercept    41.4621     2.9518      14.05     0.0000    35.6766    47.2476
---------------------------------End of Summary---------------------------------

один

-------------------------Summary of Regression Analysis-------------------------

Formula: Y ~ <x> + <intercept>

Number of Observations:         6
Number of Degrees of Freedom:   2

R-squared:         0.8331
Adj R-squared:     0.7914

Rmse:              2.0540

F-stat (1, 4):    19.9712, p-value:     0.0111

Degrees of Freedom: model 1, resid 4

-----------------------Summary of Estimated Coefficients------------------------
      Variable       Coef    Std Err     t-stat    p-value    CI 2.5%   CI 97.5%
--------------------------------------------------------------------------------
             x    -0.4379     0.0980      -4.47     0.0111    -0.6300    -0.2459
     intercept    29.6731     1.6640      17.83     0.0001    26.4116    32.9345
---------------------------------End of Summary---------------------------------

два

-------------------------Summary of Regression Analysis-------------------------

Formula: Y ~ <x> + <intercept>

Number of Observations:         5
Number of Degrees of Freedom:   2

R-squared:         0.8788
Adj R-squared:     0.8384

Rmse:              1.0774

F-stat (1, 3):    21.7542, p-value:     0.0186

Degrees of Freedom: model 1, resid 3

-----------------------Summary of Estimated Coefficients------------------------
      Variable       Coef    Std Err     t-stat    p-value    CI 2.5%   CI 97.5%
--------------------------------------------------------------------------------
             x    -0.2399     0.0514      -4.66     0.0186    -0.3407    -0.1391
     intercept    24.0902     0.9009      26.74     0.0001    22.3246    25.8559
---------------------------------End of Summary---------------------------------