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Possibility Matrix For Regression Results | Rate this: (4/5 from 2 votes) |
How does one create a decision model combining all regression results which also aids addition/elimination of independet variables and helps to arrrive at a decision whther to go ahead with the predciiton or not and also other things like addition/elimination of variables. a minimum acceptance level (mathematical figure) could be part of this model and possibiltities listed. Im doing this because situations will arise like for eg R square > 85% , P> 5 % , Correlation high between one independent X1 and dependent Y, Collinearity is also high say > 70 % between X1 & X2, then what do we do?? | ||
Posted by shaju304 on |
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Rate this: (4/5 from 2 votes) Currently, we can see the individual p-values and R-squared as well as R-squared matrix between the independent variables in the analysis. The most common approach to removing independent variables that are highly correlated with others is to do so one by one by removing the one with the highest R-squared in the multicollinearity matrix. As a secondary approach low value variables can be eliminated by evaluating the independent analysis. This is currently a manual process; however we are considering options to add automated feature selection based on broad sweeping rules in a future version. | |
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Posted by Excel Helper on |
Rate this: (4/5 from 2 votes) My business email [email protected] +91 9819619949 Kindly revert to me at the earliest. | |
Posted by shaju304 on |
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