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 ...
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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"...
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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 $y$ ...
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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 ...
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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 ...
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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 ...
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