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.

import numpy as np
import matplotlib.pyplot as plt
X=np.array(eval(input()))
Y=np.array(eval(input()))
Xmean=np.mean(X)
Ymean=np.mean(Y)
num,den=0,0
for i in range(len(X)):
    num+=(X[i]-Xmean)*(Y[i]-Ymean)
    den+=(X[i]-Xmean)**2
m=num/den
c=Ymean-m*Xmean
print(m,c)
Y_pred=m*X+c
print(Y_pred)
plt.scatter(X,Y)
plt.plot(X,Y_pred,color="orange")
plt.show()

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