To implement univariate Linear Regression to fit a straight line using least squares.

1. Hardware – PCs
2. Anaconda – Python 3.7 Installation / Moodle-Code Runner

1. Get the independent variable X and dependent variable Y.
2. Calculate the mean of the X -values and the mean of the Y -values.
3. Find the slope m of the line of best fit using the formula.
4. Compute the y -intercept of the line by using the formula:
5. Use the slope m and the y -intercept to form the equation of the line.
6. Obtain the straight line equation Y=mX+b and plot the scatterplot.

Developed by: Lokesh N
Register No: 212222100023

import numpy as np
import matplotlib.pyplot as gp
x=np.array(eval(input()))
y=np.array(eval(input()))
x_mean=np.mean(x)
y_mean=np.mean(y)
num,denom=0,0
for i in range(len(x)):
    num+=(x[i]-x_mean)*(y[i]-y_mean)
    denom+=(x[i]-x_mean)**2
m=num/denom
b=y_mean-(m*x_mean)
print(m,b)
y_pred=(m*x)+b
print(y_pred)
gp.scatter(x,y,color='red')
gp.plot(x,y_pred,color="green")
gp.show()

Thus the univariate Linear Regression was implemented to fit a straight line using least squares.