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Jun 2, 2016 1:25 AM
(2304 views)

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Jun 2, 2016 10:34 AM
(2842 views)

Solution

The cause is collinearity among the independent variables.

The overall test just says that the model including all the variables is better than the mean of the response.

The test for significance of the individual parameters is conditioned on all the other parameters in the model.

If there is substantial correlation between two independent variables, both important in explaining the response, then neither will be significant with the other in the model.

Collinearity can be more complicated than just two variables being correlated. It can happened if one variable is correlated with a linear combination of several other variables which is common when one has many independent variables.

The solution in general is to identify the subsets of variables that are highly correlated among each other and use a subset that is not too correlated.

Not being a JMP expert, more SAS, I will leave it to others to give you more specific advice on how do identify the highly correlated subsets

Some ideas that come to mind are looking at pairwise correlations. Another idea is to use the partition platform to pick candidate independent variables.

If your goal is prediction, then using recursive partitioning is fine. If you goal is inference, using recursive partitioning to select variables will bias the p-values small and make any inference suspect.

5 REPLIES

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Jun 2, 2016 10:34 AM
(2843 views)

The cause is collinearity among the independent variables.

The overall test just says that the model including all the variables is better than the mean of the response.

The test for significance of the individual parameters is conditioned on all the other parameters in the model.

If there is substantial correlation between two independent variables, both important in explaining the response, then neither will be significant with the other in the model.

Collinearity can be more complicated than just two variables being correlated. It can happened if one variable is correlated with a linear combination of several other variables which is common when one has many independent variables.

The solution in general is to identify the subsets of variables that are highly correlated among each other and use a subset that is not too correlated.

Not being a JMP expert, more SAS, I will leave it to others to give you more specific advice on how do identify the highly correlated subsets

Some ideas that come to mind are looking at pairwise correlations. Another idea is to use the partition platform to pick candidate independent variables.

If your goal is prediction, then using recursive partitioning is fine. If you goal is inference, using recursive partitioning to select variables will bias the p-values small and make any inference suspect.

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Jun 5, 2016 4:56 AM
(2120 views)

Thank you very much for the replay, I will check if I have correlation between my parameters

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Jun 3, 2016 5:43 AM
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Jun 3, 2016 10:40 AM
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Jun 5, 2016 5:01 AM
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