How would I plot my linear regression results for this linear regression I did from pandas?
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'])
I tried plot() but it did not work. I want to plot all three samples on one plot are there any pandas code or matplotlib code to hadle data in the format of these summaries?
Anyways the results look like this:
Control
------------------------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---------------------------------
one
-------------------------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---------------------------------
two
-------------------------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---------------------------------