All the numbers you mention below are approximately correct. You can now explicitly compute bias(x) and var(x) in terms of x, mean(a), mean(b), var(a) and var(b) (mean(b)=0):
Bias is the average of bias(x) over x; var is the average of var(x) over x. Set
. One can show that
Note: you can also compute the bias and variance via simulation.
Quote:
Originally Posted by the cyclist
I am struggling to replicate the variance of H_1 of Ex. 2.8 in the text. I was able to get the bias correct (and both bias and variance for H_0), as well as getting the related quiz problem correct, so this is really puzzling me.
I'm trying to narrow down where my mistake might be. Can someone please verify whether or not the correct average hypothesis is
g_bar(x) = a_mean * x + b_mean
where
a_mean ~= 0.776
and
b_mean ~= 0.
I plot that, and it does look like the figure in the book.
Also, when I take the standard deviation (over the data sets) of the coefficients a and b, I get
std(a) ~= 1.52
std(b) ~= 0.96
Do those look right? I am truly puzzled here!
