Examining explained and unexplained variance of the DV
I would begin analysis by finding the R-squared (R2) value of a model with all predictor variables, and then determine the change in R-squared when iteratively leaving out each predictor variable one at a time. Such an analysis should weed out the predictors with minimal impact on the regression, and give you a good idea of the impact for the remaining predictor variables. I would choose the R-squared fit statistic for this analysis as it is generally held to explain the amount of dependent data variance explained by the model, and I calculate R-squared by using numpy as "R2 = 1.0 - (numpy.var(regression_error) / numpy.var(dependent_data))"
What statistical techniques should I adopt when trying to determine how much my independent variables explain the variance of my dependent variable?
For further context - I have been asked to develop a model in Python with the aim of examining the extent to which the predictor variables impact upon the response variable. Having usually focused on developing models for predictive purposes, I am unsure of where to start here.