Lecture 4 Flashcards
1
Q
The sample mean
A
μ(hat) = 1/M * M Σ i=1 xi
0x1 + 1x2 +…+ / total
2
Q
E(μ(hat)) =
A
μ
3
Q
var(μ(hat)) =
A
σ^2/M
4
Q
The central limit theorem
A
Explains importance of normal pdf in statistics
But still based on asymptotic behaviour of an infinite ensemble of samples that we didn’t actually observe.
5
Q
The bivariate normal distribution p(x,y) =
A
1/(2πσxσy√(1-ρ^2)) exp[-1/(2(1-ρ^2)) Q(x,y)]
6
Q
where the quadratic form Q(x,y) =
A
(x-μx/σx)^2 +(x-μy/σy)^2 - 2ρ(x-μx/σx)(x-μy/σy)
7
Q
E(r) =
A
r^hat = np