I have four variables, and I'm supposed to forward select (using their p values) them at the 5% level. Normally you'd probably start with the variable that has the lowest p value, but I have some confusing output on R.

This is what summary() does to my model when all variables are included i.e. Heat = yint + A + B + C+ D:

            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  96.0803    12.1436   7.912 4.73e-05 ***
A             1.1577     0.2268   5.105 0.000924 ***
B             0.1843     0.1339   1.376 0.206003    
C            -0.3649     0.1928  -1.892 0.095087 .  
D            -0.4719     0.1312  -3.596 0.007024 ** 

From this output, which gives the p value given that all other variables are in the model, we have A as the lowest p-value var at p = 0.000924.

However, when I use those variables in their own model D actually has the lowest p value (p of D = 0.000576 vs p of A = 0.00455):


            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 117.5679     5.2622  22.342 1.62e-10 ***
D            -0.7382     0.1546  -4.775 0.000576 ***


            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  81.4793     4.9273   16.54 4.07e-09 ***
A             1.8687     0.5264    3.55  0.00455 ** 

So would I be using A as my first variable or D as my first variable to forward select the rest with? My guess would be D since you don't start with the full model when forward selecting, but I have no idea what I'm doing.



1 Answer 1


According to this, you should choose variable D as the first variable to add to the model. Start by considering all the models with a single variable and add the varibale whose inclusion gives the most statistically significant improvement of the fit.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.