From 2f4b5de75c8ef6d3ab7f7b73d776106925bc56bb Mon Sep 17 00:00:00 2001
From: Nivedhakaliaperumal76 <nivedhakaliaperumal76@gmail.com>
Date: Tue, 26 May 2026 14:02:09 +0530
Subject: [PATCH] Update README.md

---
 README.md | 60 ++++++++++++++++++++++++++++++++++++++++++++++++++++---
 1 file changed, 57 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index 85c90cdc..e8484162 100644
--- a/README.md
+++ b/README.md
@@ -20,10 +20,64 @@ If y represents the dependent variable and x the independent variable, this rela
 ![image](https://user-images.githubusercontent.com/104613195/168225866-ac8f6610-bdc3-4ac2-a24e-2b24ba08e189.png)
 
 # Program :
+NAME: K NIVEDHA
 
-![image](https://github.com/ramjan1729/Correlation_Regression/assets/103921593/9eb48cbf-8ca3-4cd9-8440-ff45fd98333e)
+REF NO: 212225230204
 
+```import numpy as np
+import math
+import matplotlib.pyplot as plt
 
-# Result
+x=[ int(i) for i in input().split()]
+y=[ int(i) for i in input().split()]
+
+N=len(x)
+
+Sx=0
+Sy=0
+Sxy=0
+Sx2=0
+Sy2=0
+
+for i in range(0,N):
+    Sx=Sx+x[i]
+    Sy=Sy+y[i]
+    Sxy=Sxy+x[i]*y[i]
+    Sx2=Sx2+x[i]**2
+    Sy2=Sy2+y[i]**2
+
+r=(N*Sxy-Sx*Sy)/(math.sqrt(N*Sx2-Sx**2)*math.sqrt(N*Sy2-Sy**2))
+
+print("The Correlation coefficient is %0.3f"%r)
+
+byx=(N*Sxy-Sx*Sy)/(N*Sx2-Sx**2)
+
+xmean=Sx/N
+ymean=Sy/N
+
+print("The Regression line Y on X is ::: y = %0.3f + %0.3f (x-%0.3f)"%(ymean,byx,xmean))
+
+plt.scatter(x,y)
 
-# Output 
+def Reg(x):
+    return ymean + byx*(x-xmean)
+
+x=np.linspace(20,80,51)
+
+y1=Reg(x)
+
+plt.plot(x,y1,'r')
+
+plt.xlabel('x-data')
+plt.ylabel('y-data')
+
+plt.legend(['Regression Line','Data points'])
+
+plt.show()
+```
+
+# Output
+<img width="835" height="637" alt="image" src="https://github.com/user-attachments/assets/c222ba61-cd51-441c-b68d-7dc443df0801" />
+
+# Result
+Thus to analyse given data using coeffificient of correlation and regression line is created.