2
votes
How to calculate the R-square with the following figure?
There are many equivalent ways of thinking about $R^2$. The one that makes the most sense to me is comparing the performance of your model to the performance of some baseline model that always ...
- 33.9k
1
vote
How is the relationship between two variables $X$ and $Y$ supposed to "explain" $R^2\text%$ of the variation of the data?
$R^2$ is a comparison of the variance of the error terms of two models. One model is naïve and always makes the same guess of $\bar y$ each time; this is how we get the "total sum of squares"...
- 33.9k
1
vote
Why do we use $R^2$ instead of $R$ in linear regression?
$R^2$ has several equivalent definitions. One is the squared correlation between the $x$ and $y$ variables in a simple linear regression. One is the squared correlation between the true values of $y$ ...
- 33.9k
1
vote
Interpretation of "low variance" in PCA
can it be said that no predictor has a strong influence on the system
no. You could have strong influence, but in a way that cannot be well approximated linearly.
Imagine a situation where controls ...
1
vote
Interpretation of "low variance" in PCA
Based on your comment, I believe there could be two, not necessarily related, things at play:
Your principal components do not explain enough of the variance; and
Your groups cannot be distinguished ...
- 171
1
vote
Interpretation of "low variance" in PCA
If the variance is "low" in every PC then you can conclude that no linear combination of variables can explain much variability, and this does include individual variables also since if you ...
- 6,495
Only top scored, non community-wiki answers of a minimum length are eligible