计算自变量在解释线性回归中因变量的方差中的重要性

Question

I am working on a Media Mix Modeling (MMM) project where I have to build linear model for predicting traffic factoring in various spends as input variables. I have got the linear model equation which is:

Traffic = 1918 + 0.08*TV_Spend + 0.01*Print_Spend + 0.05*Display_spend

I want to calculate two things which I don't know how to do:

1. How much each variable is contributing in explaining variance of traffic?
2. What percentage of total traffic is due to each independent variable?

Answer 1

I think this question is already been answered several times at several places (a duplicate?);

For example see:

https://stats.stackexchange.com/questions/79399/calculate-variance-explained-by-each-predictor-in-multiple-regression-using-r

You also may want to compute the standardized regression coefs (first standardize the variables and next rerun the regression analysis) to find out which independent variable has the largest effect on the dependent variable (if significant, I would like to add). I think the interpretation of standardized regression weights is more intuitively than considering the explained variance.

Cheers, Peter