It is statistical measure of how close the data are to the fitted regression line. It is also known as the coefficient of determination, or the coefficient of multiple determination for multiple regression.
Now , this was definition , let me explain in practical way.
I all have pizza… very yummy.. but why the price of pizza is very high?.. it is may be due to the ingredients they put in it. The ingredients they put in pizza has high rate. Eg. the price of dough and toppings , cheese in it.
So, here we can say 89% of price of pizza is due to the price of dough and toppings.
So, we get R square = 89%
Which means, 89% of the price of pizza is dependent on dough and toppings.
Now , in theoretical way, we can say that 89% change in the dependent variable occured due to the given independent variable while rest of 11% may be caused by random error.
Here are few points to understand R squared :
- The proportion of the variation in your dependent variable explained by all of your independent variable in the model.
- It assumes that every independent variable in the model helps to explain variation in the dependent variable.
- If Independent variable added, then R square value increases.
- But sometime, some variables do not contribute in predicting target variable.
