I generated 3000 observations (3000) and carrying out multiple linear regression. Prior to regression i randomized my observations five times and then selected 30 % of the observations for testing and 70 % for training.
I discovered that when i choose a different set of 30% of my observations i get slightly different standardized regression coefficients and sometimes different sign (positive or negative). Then i decided to do 5-fold cross validation. But when i do this cross validation, i get five different sets of standardized regression coefficients. I want to know the best way to decide which standardized regression coefficient i should use for each independent variable.
Is it good take the geometric mean of the coefficients? If so, how do i also decide on which sign to choose for the final coefficient especially when i get both positive and negative signs during the cross validation.
On a last note, is it better to standardize the independent variables before multiple linear regression or do i regress with the unstandardized independent variables but take the standardized regression coefficient given by SigmaPlot?
The goal of my analysis is to factor the influence of each independent variable (predictor) on the dependent variable (response).
Hope to get your favorable response.
Note: In my regression i used the natural logarithm of the independent variables.