linear regression python

from sklearn.linear_model import LinearRegression
X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
y = np.dot(X, np.array([1, 2])) + 3
reg = LinearRegression().fit(X, y)
reg.score(X, y)
reg.coef_
reg.intercept_
reg.predict(np.array([[3, 5]]))

from scipy import stats
x = np.random.random(10)
y = 1.6*x + np.random.random(10)
slope, intercept, r_value, p_value, std_err = stats.linregress(x, y)