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The variance of the sample cdf in the tails is smaller than near the median. I assume we're talking about a one-sample Kolmogorov-Smirnov (though similar comments come into the two-sample case). Specifically, the variance is proportional to $F(1-F)$, so as $F$ approaches $0$ or $1$ the variance goes to zero, and so does the standard error of the ecdf. ...


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TSS TSS <- function(y){ y_ = mean(y) y = y - y_ y = y^2 sum(y) } RSS RSS <- function(error){ sum(error^2) } F-statistics FS <- function(tss, rss, num_of_predictors, num_of_sample){ a=(tss-rss)/num_of_predictors b = rss/(num_of_sample-num_of_predictors-1) a/b } num_of_predictors = length(cars)-1 num_of_sample = length(y) error = ...


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