This site uses different types of cookies, including analytics and functional cookies (its own and from other sites). To change your cookie settings or find out more, click here. If you continue browsing our website, you accept these cookies.
I am trying to investigate the relationship between an independent variable and about 90 dependent variables using linear regresssion. What I have done in the past is to run one independent ( or a few) against the dependent variable, determine the relationship, then keep adding independent variables one after another and running it till I get to the final independent variables having a significant effect on the dependent variable.
The problem with this is that, this is very time consuming. I was made aware that there is a way to run all the independent variables at once and Alteryx will find the optimal significant independent variables.
I believe what you are looking for is the 'Association Analysis Tool' (link). Within this tool there is a selection that states 'Target a field for more detailed analysis'. Once this is selected, you can choose a target field which creates correlation between the different variables as well as an interactive correlation plot.
Another possibility would be the 'Spearman correlation Tool' (link).
I have attached a workflow with both tools in use and applied to the UC Irvine mpg data set.
If you are looking for determining 'real importance' within a linear model by running multiple iterations of the model with variables in different order, then you'll have to write your own implementation in R (which I have an example of as well). There is a library that already exists that does the heaving lifting for that as well (link).