Linear Regression | R Square and Adjusted R Square 1 - P6

Code Starts Here
===============

import pandas as pd

df1 = pd.DataFrame()
X1 = [10,20,30,40,50]
Y1 = [3,4,2,5,6]

df1['X'] = X1
df1['Y'] = Y1

Ymean = df1['Y'].mean()

df1['Y-Ymean'] = df1['Y'] - Ymean

df1['(Y-Ymean)Square'] = (df1['Y'] - Ymean)**2

df1['Ybar'] = 1.9 + 0.07 * df1['X']

df1['Ybar - Ymean'] = df1['Ybar'] - Ymean

df1['(Ybar - Ymean)Square'] = (df1['Ybar'] - Ymean)**2

R_Square = df1['(Ybar - Ymean)Square'].sum() / df1['(Y-Ymean)Square'].sum()

'''
Adjusted_R_Square = 1 - (1 - R_Square)**2 * (N -1 ) / (N - K -1)
N = No of points in the data
K = no of independent variables
'''
N = 5

K = 1

Adjusted_R_Square = 1 - (1 - R_Square)**2 * (N -1 ) / (N - K -1)

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