python import numpy as np import statsmodels.api as sm x = np.linspace(0, 10, 100) y = 2 * x + np.random.normal(0, 1, 100) X = sm.add_constant(x) model = sm.OLS(y, X) results = model.fit() print(results.summary()) residuals = results.resid influence = results.get_influence() outliers = influence.outlier_test()["bonf(p)"] print(outliers) vif = sm.variance_inflation_factor(X, 1) print(vif) het = results.diagn['het_test']['Lagrange multiplier statistic'] print(het) fig, ax = plt.subplots() ax.scatter(results.fittedvalues, residuals) plt.show() print(sm.stats.normal_ad(residuals))