Dear all,
I’m beginning with Python that I need to use to run a linear model for the dataset below :
Location | Y | X1 | X2 |
1 | 32 | 1 | 1 |
1 | 44 | 1 | 2 |
1 | 58 | 1 | 3 |
1 | 76 | 2 | 1 |
1 | 73 | 2 | 2 |
1 | 37 | 2 | 3 |
1 | 52 | 3 | 1 |
1 | 78 | 3 | 2 |
1 | 60 | 3 | 3 |
2 | 93 | 1 | 1 |
2 | 78 | 1 | 2 |
2 | 25 | 1 | 3 |
2 | 97 | 2 | 1 |
2 | 85 | 2 | 2 |
2 | 60 | 2 | 3 |
2 | 70 | 3 | 1 |
2 | 62 | 3 | 2 |
2 | 95 | 3 | 3 |
My target is to run a linear model as follows :
Y ~ X1 + X2
And for that the following code gave me exactly what I need :
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
from statsmodels.formula.api import ols
import scipy.stats as stats
df = pd.DataFrame(dataset)
reg = ols('Y ~ C(X1) + C(X2)', data=df).fit()
df['fitted_values'] = reg.fittedvalues
result = reg.outlier_test()
df['student_resid'] = result.student_resid
What I’m not able to do is to run this code by ‘Location’, and get my columns 'fitted_values' and 'student_resid' accordingly.
Any help is highly appreciated.
Thanks a lot in advance.
Regards,
