Lecture 4 Flashcards

1
Q

The sample mean

A

μ(hat) = 1/M * M Σ i=1 xi

0x1 + 1x2 +…+ / total

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2
Q

E(μ(hat)) =

A

μ

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3
Q

var(μ(hat)) =

A

σ^2/M

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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.

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5
Q

The bivariate normal distribution p(x,y) =

A

1/(2πσxσy√(1-ρ^2)) exp[-1/(2(1-ρ^2)) Q(x,y)]

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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)

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7
Q

E(r) =

A

r^hat = np

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