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.
where 0<= R2<=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)
#What is the R-square value of Passengers vs Promotion_Budget model?
fitted1.summary()
#What is the R-square value of Passengers vs Inter_metro_flight_ratio
fitted2.summary()
Dep. Variable: | Passengers | R-squared: | 0.242 |
---|---|---|---|
Model: | OLS | Adj. R-squared: | 0.232 |
Method: | Least Squares | F-statistic: | 24.90 |
Date: | Wed, 27 Jul 2016 | Prob (F-statistic): | 3.58e-06 |
Time: | 11:48:27 | Log-Likelihood: | -848.30 |
No. Observations: | 80 | AIC: | 1701. |
Df Residuals: | 78 | BIC: | 1705. |
Df Model: | 1 | ||
Covariance Type: | nonrobust |
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 |
Omnibus: | 10.172 | Durbin-Watson: | 1.385 |
---|---|---|---|
Prob(Omnibus): | 0.006 | Jarque-Bera (JB): | 10.098 |
Skew: | 0.822 | Prob(JB): | 0.00641 |
Kurtosis: | 3.573 | Cond. No. | 9.48 |
The next post is about multiple regression in python.
Link to the next post: https://statinfer.com/204-1-6-multiple-regression-in-python/