Link to the previous post : https://statinfer.com/204-1-4-how-good-is-my-regression-line/

In this post we will move toward the statistical measure for a good fit.

A good fit will have
- SSE (Minimum or Maximum?)
- SSR (Minimum or Maximum?)
- And we know SST= SSE + SSR
- SSE/SST(Minimum or Maximum?)
- SSR/SST(Minimum or Maximum?)
The coefficient of determination is the portion of the total variation in the dependent variable that is explained by variation in the independent variable.
The coefficient of determination is also called R-squared and is denoted as $R^{2}$

R^2 = S S R/ S S T

where 0<= $R^{2}$ <=1

(We are continuing with the python session from posts 204.1.1 – 204.1.4; we have already built models required for this practice session)

In [19]:

#What is the R-square value of Passengers vs Promotion_Budget model?
fitted1.summary()

Out[19]:

In [20]:

#What is the R-square value of Passengers vs Inter_metro_flight_ratio

fitted2.summary()

Out[20]:

coef	std err	t	P>\|t\|	[95.0% Conf. Int.]
Intercept	2.044e+04	4993.747	4.093	0.000	1.05e+04 3.04e+04
Inter_metro_flight_ratio	3.507e+04	7027.768	4.990	0.000	2.11e+04 4.91e+04

The next post is about multiple regression in python.

Link to the next post: https://statinfer.com/204-1-6-multiple-regression-in-python/

21st June 2017

204.1.5 R-Squared in Python