What type of test can run 2 or more explanatory variables to predict 1 response variable?
All Answers (9) By definition multiple regression model predicts the value of a dependent variable (i.e. one outcome variable) based on the value of two or more independent variables (i.e multiple predictor variables).
Does one variable predict another?
Relationships, or correlations between variables, are crucial if we want to use the value of one variable to predict the value of another. The relationship between the variables is curvilinear.
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?
Can a multivariate analysis be run on all dependent variables?
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. This will maximize your power while holding the type I error rate at your alpha level. You should know, however, that this will not make your analysis more accurate/robust.
What does it mean to have multiple regression models?
Multiple regression model is one that attempts to predict a dependent variable which is based on the value of two or more independent variables.
Can a classification model support multiple target variables?
Machine Learning classifiers usually support a single target variable. In the case of regression models, the target is real valued, whereas in a classification model, the target is binary or multivalued. F o r classification models, a problem with multiple target variables is called multi-label classification.