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2 weeks ago - last edited 2 weeks ago

After running proc GLM with multiple regressors.

I got the regressors and their Estimate, P, and T values.

I want to find out the best regressors for my model.

how should I use P, T, or Estimate column to determine which regressors are best for my model.

Also, what is the meaning of Estimate, P, and T values in model output.

Please help

this is how the output looks like

Dependent | Parameter | Estimate | Biased | StdErr | tValue | Probt |

ln_dep1 | ln_rw_base*region_nm PACIFIC NORTHWEST | 0.9604395696 | 0 | 0.00481642 | 199.41 | 0.0000 |

ln_dep1 | ln_rw_base*region_nm PACIFIC CENTRAL | 0.9601801559 | 0 | 0.00476460 | 201.52 | 0.0000 |

ln_dep1 | ln_rw_base*region_nm NEW ENGLAND | 0.9600740196 | 0 | 0.00479192 | 200.35 | 0.0000 |

ln_dep1 | ln_rw_base*region_nm PACIFIC NORTH | 0.9600171774 | 0 | 0.00475982 | 201.69 | 0.0000 |

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2 weeks ago

This sounds like multiple questions regarding understanding the GLM output.

I recommend that you take a look at some of the vell described PROC GLM examples from the documentation here:

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2 weeks ago - last edited 2 weeks ago

P-Value is more small , the variable is more significant .

Also Check PROC GLMSELECT + SELECTION=LASSO

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2 weeks ago

Ksharp wrote:

P-Value is more small , the variable is more significant .

Also Check PROC GLMSELECT + SELECTION=LASSO

My $0.02 Philosophical difference on p-value interpretaton: The p-value is used to reject the Null hypothesis or not. The rejection level should be specified before the test is done.

With that caveat small values such as 0.05 or 0.025 are common values to compare and a p-value smaller than those will reject the null hypothesis.

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2 weeks ago

Agreeing with @draycut, your questions are all basic statistics, that can be found in documentation provided by SAS and in many statistics textbooks, and you need to do some reading there to learn these fundamental concepts.