Least squares regression with statsmodels

import numpy as np
import statsmodels.api as sm
#generate some sample data
num_periods = 9
all_values = np.array([np.random.random(8)
                      for i in range(num_periods)])
#Filter the data
y_values = all_values[:, 0] #First column values as Y
x_values = all_values[:, 1:]#All other values as X
x_values = sm.add_constant(x_values) #inclue the intercept
results = sm.OLS(y_values,x_values).fit() #Regress and fit the model
print(results.params)
[ 2.16638276 -0.29200215 -2.43522211  1.6595394   0.24384436 -0.70530175
  0.71402003 -1.9226281 ]