python
#!/usr/bin/env python
# -*- coding: utf-8 -*-

from scipy import stats
from random import randint
import numpy as np

def regress(y, x):

    reg = slope,intercept,r_value,p_value,std_err = stats.linregress(x,y)   ## generate regression elements
    yhat = x*reg.slope + intercept                                          ## predict y using with slope(coefficient) and intercept


if __name__=="__main__":
    x= np.array([randint(0,1000) for n in range(0,100)])            ## generate 100 random integers between 1 and 1000 for x
    y= np.array([randint(0,1000) for n in range(0,100)])            ## generate 100 random integers between 1 and 1000 for y
    regress(y,x)                                                    ## run function using the 100 random integers for x & y