import numpy as np, statsmodels.api as sm
nsample = 100
x = np.linspace(0, 10, nsample)
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
mod = sm.OLS(y, X)
res = mod.fit()
print res.conf_int(0.01) # 99% confidence interval
How to calculate the 99% confidence interval for the slope in a linear regression model in python?
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Origin blog.csdn.net/qq_36017395/article/details/104650650
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