How do you predict a dependent variable?
The Regression Approach for Predictions Instead, you predict the mean of the dependent variable given specific values of the independent variable(s). For our example, we’ll use one independent variable to predict the dependent variable. I measured both of these variables at the same point in time.
Is there a way to predict all dependent variables?
One way is to build multiple models, each one predicting a single dependent variable. An alternative approach is to build a single model to predict all the dependent variables at one go (multivariate regression or PLS etc). My question is: does taking into account multiple DV’s simultaneously lead to a more robust/accurate/reliable model?
Do you treat proportion as a dependent variable in regression?
If you can assume a linear model, it will be much easier to do, say, a complicated mixed model or a structural equation model. If it’s just a single multiple regression, however, you should look into one of the other methods. A second approach is to treat the proportion as a binary response then run a logistic or probit regression.
How to choose the best predictive regression model?
From the correlation matrix, we can see that there is varying extent to which the independent variables are correlated with the target. Lower correlation means weak linear relationship but there may be a strong non-linear relationship so, we can’t pass any judgement at this level, let the algorithm work for us.
Do you need to check for correlations between dependent variables?
You need to check for correlations amongst your dependent variables ( edit: @BilalBarakat’s answer is right, the residuals are what’s important here ). If all or some are independent, you can run separate analyses on each. If they are not independent, or whichever ones aren’t, you could run a multivariate analysis.