(1) Is this homework? (This list doesn't do homework for people!)
(Animals maybe, but not people! :-) )
(2) Your question isn't really an R question but rather a
statistics/linear modelling
question. It is possible that you might get some insight from Frank
Harrel's book
"Regression Modelling Strategies" (Springer, 2001).
cheers,
Rolf Turner
On 11/22/13 12:52, srecko joksimovic wrote:
Hi,
I'm trying to fit regression model, but there is something wrong with it.
The dataset contains 85 observations for 85 students.Those observations are
counts of several actions, and dependent variable is final score. More
precisely, I have 5 IV and one DV. I'm trying to build regression model to
check whether those variables can predict the final score.
I'm attaching output of several steps, but I tried to following procedure:
- build model with only those two variables
- summary shows that non of them is significant predictor of the final
outcome.
- test for multicollinearity revealed tolerance below 0.2 (potential
problem)
- build two new models having as a predictor only one of those values
- both models show that variable used for the model is significant
predictor. Separately they are significant, together not. Probably
multicollinearity problem, but...
- as I keep adding other variables to one or the other model, Multiple
R-squared slightly increases.
- I tried to compare different models using anova, but non of them seems to
be better.
How to determine which model is better?
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