To implement univariate Linear Regression to fit a straight line using least squares.
- Hardware – PCs
- Anaconda – Python 3.7 Installation / Moodle-Code Runner
- Get the independent variable X and dependent variable Y.
- Calculate the mean of the X -values and the mean of the Y -values.
- Find the slope m of the line of best fit using the formula.
- Compute the y -intercept of the line by using the formula:
- Use the slope m and the y -intercept to form the equation of the line.
- Obtain the straight line equation Y=mX+b and plot the scatterplot.
Program for Univariate linear regression using the least squares method.
Developed by: Praneet S
RegisterNumber: 212221230078
'''
import numpy as np
# Preprocessing Input data
X = np.array(eval(input()))
Y = np.array(eval(input()))
# Mean
X_mean =np.mean(X)
Y_mean =np.mean(Y)
num=0 #for slope
denom=0 #for slope
#to find sum of (xi-x') &(yi-y') &(xi-x')^2
for i in range(len(X)):
num+=(X[i]-X_mean)*(Y[i]-Y_mean)
denom+=(X[i]-X_mean)**2
#calculate slope
m=num/denom
#calculate intercept
#(xi-x') & (yi-y')
b=Y_mean-m*X_mean
print(m,b)
#line equation
y=m*X+b
print(y)
Thus the univariate Linear Regression was implemented to fit a straight line using least squares.