Correlation and Regression Lines - A Quick Recap #2.py

#RAMESH CHANDRA

import math
x = [15 , 12 , 8  , 8 ,  7 ,  7 ,  7 ,  6   , 5  , 3]
y = [10  ,25 , 17  ,11 , 13 , 17 , 20 , 13 , 9  , 15]
#calculate mean of x and y
sum_x,sum_y=0,0
for i in xrange(len(x)):
    sum_x,sum_y=sum_x+x[i],sum_y+y[i]

mean_x = float(sum_x)/len(x)
mean_y = float(sum_y)/len(y)
#now calculate r = A/sqrt(B*C)
# A = sum ((x[i]-x_mean_x)*(y[i]-mean_y)) 
# B = sum(x[i]-mean_x)^2
# C = sum(y[i]-mean_y)^2

A, B , C ,= 0,0,0;
for i in xrange(len(x)):
    A += (x[i]-mean_x)*(y[i]-mean_y)
    B += (x[i]-mean_x)**2
    C += (y[i]-mean_y)**2
#Co-relation Coefficient
r = float(A)/math.sqrt(B*C)
#slope  b = r*(sd_x/sd_y)
#s_d and s_y are standard deviations of x and y respectively
std_x = math.sqrt( (float(B)/(len(x))))
std_y = math.sqrt( (float(C)/(len(y))))
#final slop
ans = float(r*std_y)/std_x
print("%.3f"%ans)

#for more info....go to this link   https://www.youtube.com/watch?v=GhrxgbQnEEU