How would I build linear regression results for this linear regression made by me 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 build all three samples on one graph, is there a pandas code or matplotlib code for hadle data in the format of these summaries?
In any case, 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---------------------------------
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