justk

5 ms·

1.
by justk 2y ago
The correcting factor n/(n-1) in R is what explains my paradox about the law of total variance Var(Y) = E(var(Y|X)) + Var(E(X|Y)), I was obtaining result that don't match this formula because I corrected all the variances with the
2.
by justk 2y ago
From (1) On the other hand, if the variation between the group means and the grand mean is small, and the variation within groups is large, this suggests there are no real differences in the group means i.e. the variations we observe is jus
3.
by justk 2y ago
Let d1 = data[state==0] and d2 = data[state==1], then var(d1$pref) = 0.26, var(d2$pref)= 0.26 and var(d$pref)= 0.256 (using R and one of your dataframes), so the intuition is that knowing the state does not give information about the prefer
4.
by justk 2y ago
The math is correct, I am referring to your comment: >> R² is a measure like any other. In this case it measures the relative reduction in MSE - which is low because the prediction of individual votes remains quite bad even if the
5.
by justk 2y ago
The most general definition of R^2 can produce a result that is negative, and we are talking about a paradox related to values of R^2 that one should expect. So it is common to use linear models and linear regression. I don't know if t
6.
by justk 2y ago
This is a little strange, you are using a data.frame with only two points so any linear model with two different parameters will be 100% accurate. This is the line that connect two points.
7.
by justk 2y ago
I think that you are using here a different definition of R^2 for example the way you are thinking of R^2 doesn't allow for an interpretation of the constant term used in the linear model for the formula of the R^2 to be true. What yo
8.
by justk 2y ago
The math is correct, but I think the model used is not correct since it doesn't reflect that the variable s is dichotomous so rather a mixed model should be used. If we continue thinking that s is continuous we could think of this ex
9.
by justk 2y ago
Sorry, I edited my post several times and finally choose a short form with links other sources. If you fix state=1 then there are no more random variables so the R^2 doesn't have any meaning. Just for fun, what the model should predict
10.
by justk 2y ago
The reason of the apparent paradox: a) in this case the model is a mixed model b) second the variable are nominal so you have to select one of the pseudo R^2 models. For more information: (1) Pseudo R-squared: https://en.wikiped