studentized residuals python
def internally_studentized_residual(X,Y): X = np.array(X, dtype=float) Y = np.array(Y, dtype=float) mean_X = np.mean(X) mean_Y = np.mean(Y) n = len(X) diff_mean_sqr = np.dot((X - mean_X), (X - mean_X)) beta1 = np.dot((X - mean_X), (Y - mean_Y)) / diff_mean_sqr beta0 = mean_Y - beta1 * mean_X y_hat = beta0 + beta1 * X residuals = Y - y_hat h_ii = (X - mean_X) ** 2 / diff_mean_sqr + (1 / n) Var_e = math.sqrt(sum((Y - y_hat) ** 2)/(n-2)) SE_regression = Var_e*((1-h_ii) ** 0.5) studentized_residuals = residuals/SE_regression return studentized_residuals def deleted_studentized_residual(X,Y): #formula from https://newonlinecourses.science.psu.edu/stat501/node/401/ r = internally_studentized_residual(X,Y) n = len(r) return [r_i*math.sqrt((n-2-1)/(n-2-r_i**2)) for r_i in r]
