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G:\Statistiske grundbegreber-v8\s1v8-forside.wpd

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Flerdimensional stokastisk variabel<br />

Man kan (som det ses nedenfor) vise, at<br />

X 1 og X 2 stat. uafhængige ⇒ E( X ⋅ X ) = E( X ) ⋅ E( X ) ⇔ V( X , X ) = 0 ⇔ ρ(<br />

X , X ) = 0<br />

Bevis: Vi har<br />

X 1 og X 2 stat. uafhængige ⇔ f ( x , x ) = f ( x ) ⋅ f ( x )<br />

i j i j i j i j<br />

i j i i j j<br />

∑∑ ∑∑<br />

⇒ E( X ⋅ X ) = x ⋅x ⋅ f ( x , x ) = x ⋅x ⋅ f ( x ) ⋅ f ( x )<br />

∑<br />

i j i j i j<br />

xi<br />

x j<br />

xi<br />

x j<br />

∑<br />

128<br />

i j i i j j<br />

= xi ⋅ fi( xi) xj ⋅ f j( xj) = E( Xi) ⋅E(<br />

X j)<br />

xi x j<br />

⇔ V( Xi, X j) = E( Xi ⋅ X j) − µ i ⋅ µ j =<br />

V( Xi, X j)<br />

E( Xi) ⋅ E( X j) − µ i ⋅ µ j = 0 ⇔ ρ(<br />

Xi, X j)<br />

=<br />

= 0.<br />

σ ⋅ σ<br />

Estimater for kovarians, varians og korrelationskoefficient<br />

Ud fra en stikprøve ( x1, y1),( x2, y2), ...,( xn, yn)<br />

kan man beregne<br />

SAPXY n<br />

≡ ∑ ( xi − x) ⋅( yi − y)<br />

,<br />

n<br />

2<br />

SAKX ≡ ∑ ( xi − x)<br />

,<br />

n<br />

SAKX ≡ ∑ ( xi − x)<br />

i=<br />

1<br />

( SAP = “Sum af Afvigelsers Produkter” , SAK = “Sum af Afvigelsers Kvadrater” )<br />

og heraf danne estimater for kovarians, varianser og korrelationskoefficient:<br />

SAPXY<br />

SAK X SAKY<br />

kovarians: V( X, Y)<br />

≈ og varianser: V( X)≈<br />

, VY ( )≈<br />

n − 1<br />

n − 1 n − 1<br />

korrelationskoefficient: ρ( XY , ) ≈r≡ i=<br />

1<br />

SAPXY<br />

SAK ⋅ SAK<br />

X Y<br />

Det kan således vises (for enhver fordelingstype), at<br />

E , ,<br />

SAP ⎛ XY ⎞<br />

⎜ ⎟ = V( X, Y)<br />

⎝ n − 1 ⎠<br />

E SAK ⎛ X ⎞<br />

⎜ ⎟ = V( X)<br />

⎝ n − 1 ⎠<br />

E SAK ⎛ Y ⎞<br />

⎜ ⎟ = VY ( )<br />

⎝ n − 1 ⎠<br />

Bevis: Vi har<br />

n<br />

n<br />

∑ ( )( ) ∑ ( ( i µ x) ( µ x) ( ( i µ Y) ( µ Y)<br />

)<br />

SAP = X − X Y − Y = X − − X − ⋅ Y − − Y −<br />

XY i i<br />

i=<br />

1 i=<br />

1<br />

n<br />

n<br />

∑ ∑<br />

∑ ∑<br />

= ( X − µ )( Y − µ ) + ( X − µ )( Y − µ ) − ( X − µ )( Y − µ ) − ( X − µ )( Y − µ )<br />

i x i Y<br />

x Y i x Y<br />

x i Y<br />

i=<br />

1 i=<br />

1<br />

i=<br />

1 i=<br />

1<br />

n<br />

∑ ( Xi i=<br />

1<br />

n<br />

µ x)( Yi µ Y)<br />

n ( X µ x)( Y µ Y) ( nX n µ x)( Y µ Y) ( X µ x)( nY n µ Y)<br />

= − − + ⋅ − − − − ⋅ − − − − ⋅<br />

= ∑ ( Xi − µ x)( Yi − µ Y)<br />

− n⋅( X − µ x)( Y − µ Y)<br />

.<br />

i=<br />

1<br />

Altså fås ved hjælp af linearitetsreglen:<br />

n<br />

E( SAPXY ) = ∑ E ( Xi− µ x )( Yi− µ Y ) − n⋅E ( X − µ x )( Y − µ Y )<br />

i=<br />

1<br />

n<br />

( ) ( )<br />

1<br />

= ∑ V( Xi, Yi)<br />

− ⋅ E ( X1 + X2+ ... + Xn − n⋅ i=<br />

1 n<br />

x)( Y1 + Y2+ ... + Yn − n⋅<br />

Y)<br />

n<br />

⎛ n<br />

n<br />

1<br />

⎞<br />

= ∑V( X, Y)<br />

− ⋅E⎜∑( Xi− µ x) ∑(<br />

Yj<br />

− µ<br />

Y ) ⎟<br />

i=<br />

1 n ⎝ i=<br />

1 j=<br />

1 ⎠<br />

n<br />

( µ µ )<br />

n<br />

i=<br />

1<br />

2<br />

i j

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