问题
Need to run multiple single-factor (univariate) regression models in python between a column in a dataframe and several other columns in the same dataframe
-
so based on the image, i want to run regression models between x1 & dep, x2 & dep and so on and so forth
Want to output - beta, intercept, R-sq, p-value, SSE, AIC, BIC, Normality test of residuals etc
回答1:
There are two options you can use here. One is the popular scikit-learn library. It is used as follows
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X, y) # where X is your feature data and y is your target
reg.score(X, y) # R^2 value
>>> 0.87
reg.coef_ # slope coeficients
>>> array([1.45, -9.2])
reg.intercept_ # intercept
>>> 6.1723...
There are not many other statistics you can use with scikit.
Another option is statsmodels which offers far richer detail into the statistics of the model
import numpy as np
import statsmodels.api as sm
# generate some synthetic data
nsample = 100
x = np.linspace(0, 10, 100)
X = np.column_stack((x, x**2))
beta = np.array([1, 0.1, 10])
e = np.random.normal(size=nsample)
X = sm.add_constant(X)
y = np.dot(X, beta) + e
# fit the model and get a summary of the statistics
model = sm.OLS(y, X)
results = model.fit()
print(results.summary())
OLS Regression Results
==============================================================================
Dep. Variable: y R-squared: 1.000
Model: OLS Adj. R-squared: 1.000
Method: Least Squares F-statistic: 4.020e+06
Date: Mon, 08 Jul 2019 Prob (F-statistic): 2.83e-239
Time: 02:07:22 Log-Likelihood: -146.51
No. Observations: 100 AIC: 299.0
Df Residuals: 97 BIC: 306.8
Df Model: 2
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const 1.3423 0.313 4.292 0.000 0.722 1.963
x1 -0.0402 0.145 -0.278 0.781 -0.327 0.247
x2 10.0103 0.014 715.745 0.000 9.982 10.038
==============================================================================
Omnibus: 2.042 Durbin-Watson: 2.274
Prob(Omnibus): 0.360 Jarque-Bera (JB): 1.875
Skew: 0.234 Prob(JB): 0.392
Kurtosis: 2.519 Cond. No. 144.
==============================================================================
You can see that statsmodels offer much more details, such as the AIC, BIC, t-statistics etc.
来源：https://stackoverflow.com/questions/56930145/univariate-regression-in-python