Question is incomplete.
Let us take an example related to given problem.
Referring to the random variables whose joint probability density function of 𝑋 and 𝑌 is given by
𝑓(𝑥, 𝑦) = {
𝑥 + 𝑦
3
0 ≤ 𝑥 ≤ 1, 0 ≤ 𝑦 ≤ 2
0 𝑒𝑙𝑠𝑒𝑤ℎ𝑒𝑟𝑒
Find the two lines of regression.
Solution:
lines of regression:
y = a 1 x + b 1 y=a_1x+b_1 y = a 1 x + b 1
a 1 = σ X Y / σ X 2 , b 1 = μ Y − a 1 μ X a_1=\sigma_{XY}/\sigma^2_X,b_1=\mu_Y-a_1\mu_X a 1 = σ X Y / σ X 2 , b 1 = μ Y − a 1 μ X
x = a 2 y + b 2 x=a_2y+b_2 x = a 2 y + b 2
a 2 = σ X Y / σ Y 2 , b 2 = μ X − a 2 μ Y a_2=\sigma_{XY}/\sigma^2_Y,b_2=\mu_X-a_2\mu_Y a 2 = σ X Y / σ Y 2 , b 2 = μ X − a 2 μ Y
covariance:
σ X Y = ∬ ( x − μ X ) ( y − μ Y ) f ( x , y ) d y d x \sigma_{XY}=\iint (x-\mu_X)(y-\mu_Y)f(x,y)dydx σ X Y = ∬ ( x − μ X ) ( y − μ Y ) f ( x , y ) d y d x
f X ( x ) = ∫ 0 2 f ( x , y ) d y = 1 3 ∫ 0 2 ( x + y ) d y = 1 3 ( 2 x + 1 ) f_X(x)=\int^2_0 f(x,y)dy=\frac{1}{3}\int^2_0 (x+y)dy=\frac{1}{3}(2x+1) f X ( x ) = ∫ 0 2 f ( x , y ) d y = 3 1 ∫ 0 2 ( x + y ) d y = 3 1 ( 2 x + 1 )
f Y ( y ) = ∫ 0 1 f ( x , y ) d x = 1 3 ∫ 0 1 ( x + y ) d x = 1 3 ( 1 / 2 + y ) f_Y(y)=\int^1_0 f(x,y)dx=\frac{1}{3}\int^1_0 (x+y)dx=\frac{1}{3}(1/2+y) f Y ( y ) = ∫ 0 1 f ( x , y ) d x = 3 1 ∫ 0 1 ( x + y ) d x = 3 1 ( 1/2 + y )
μ X = ∫ 0 1 x f X ( x ) d x = 1 3 ∫ 0 1 x ( 2 x + 1 ) d x = 1 3 ( 2 / 3 + 1 / 2 ) = 7 / 18 \mu_X=\int^1_0 xf_X(x)dx=\frac{1}{3}\int^1_0 x(2x+1)dx=\frac{1}{3}(2/3+1/2)=7/18 μ X = ∫ 0 1 x f X ( x ) d x = 3 1 ∫ 0 1 x ( 2 x + 1 ) d x = 3 1 ( 2/3 + 1/2 ) = 7/18
μ Y = ∫ 0 2 y f Y ( y ) d y = 1 3 ∫ 0 2 y ( 1 / 2 + y ) d y = 1 3 ( 1 + 8 / 3 ) = 11 / 9 \mu_Y=\int^2_0 yf_Y(y)dy=\frac{1}{3}\int^2_0 y(1/2+y)dy=\frac{1}{3}(1+8/3)=11/9 μ Y = ∫ 0 2 y f Y ( y ) d y = 3 1 ∫ 0 2 y ( 1/2 + y ) d y = 3 1 ( 1 + 8/3 ) = 11/9
σ X = E ( X 2 ) − μ X 2 \sigma_X=\sqrt{E(X^2)-\mu_X^2} σ X = E ( X 2 ) − μ X 2
E ( X 2 ) = ∫ 0 1 x 2 f X ( x ) d x = 1 3 ∫ 0 1 x 2 ( 2 x + 1 ) d x = 1 3 ( 1 / 2 + 1 / 3 ) = 5 / 18 E(X^2)=\int^1_0 x^2f_X(x)dx=\frac{1}{3}\int^1_0 x^2(2x+1)dx=\frac{1}{3}(1/2+1/3)=5/18 E ( X 2 ) = ∫ 0 1 x 2 f X ( x ) d x = 3 1 ∫ 0 1 x 2 ( 2 x + 1 ) d x = 3 1 ( 1/2 + 1/3 ) = 5/18
σ X = 5 / 18 − ( 7 / 18 ) 2 = 41 / 18 = 0.356 \sigma_X=\sqrt{5/18-(7/18)^2}=\sqrt{41}/18=0.356 σ X = 5/18 − ( 7/18 ) 2 = 41 /18 = 0.356
σ Y = E ( Y 2 ) − μ Y 2 \sigma_Y=\sqrt{E(Y^2)-\mu_Y^2} σ Y = E ( Y 2 ) − μ Y 2
E ( Y 2 ) = ∫ 0 2 x 2 f Y ( y ) d y = 1 3 ∫ 0 2 y 2 ( 1 / 2 + y ) d y = 1 3 ( 4 / 3 + 4 ) = 16 / 9 E(Y^2)=\int^2_0 x^2f_Y(y)dy=\frac{1}{3}\int^2_0 y^2(1/2+y)dy=\frac{1}{3}(4/3+4)=16/9 E ( Y 2 ) = ∫ 0 2 x 2 f Y ( y ) d y = 3 1 ∫ 0 2 y 2 ( 1/2 + y ) d y = 3 1 ( 4/3 + 4 ) = 16/9
σ Y = 16 / 9 − ( 11 / 9 ) 2 = 23 / 9 = 0.533 \sigma_Y=\sqrt{16/9-(11/9)^2}=\sqrt{23}/9=0.533 σ Y = 16/9 − ( 11/9 ) 2 = 23 /9 = 0.533
σ X Y = 1 3 ∫ 0 1 ∫ 0 2 ( x − 7 / 18 ) ( y − 11 / 9 ) ( x + y ) d y d x = \sigma_{XY}=\frac{1}{3}\intop^1_0 \intop^2_0 (x-7/18)(y-11/9)(x+y)dydx= σ X Y = 3 1 ∫ 0 1 ∫ 0 2 ( x − 7/18 ) ( y − 11/9 ) ( x + y ) d y d x =
= 1 3 ∫ 0 1 ( x − 7 / 18 ) ( x y 2 / 2 − 11 x y / 9 + y 3 / 3 − 11 y 2 / 18 ) ∣ 0 2 d x = =\frac{1}{3}\intop^1_0(x-7/18)(xy^2/2-11xy/9+y^3/3-11y^2/18)|^2_0dx= = 3 1 ∫ 0 1 ( x − 7/18 ) ( x y 2 /2 − 11 x y /9 + y 3 /3 − 11 y 2 /18 ) ∣ 0 2 d x =
= 1 3 ∫ 0 1 ( x − 7 / 18 ) ( 2 x − 22 x / 9 + 8 / 3 − 44 / 18 ) d x = =\frac{1}{3}\intop^1_0(x-7/18)(2x-22x/9+8/3-44/18)dx= = 3 1 ∫ 0 1 ( x − 7/18 ) ( 2 x − 22 x /9 + 8/3 − 44/18 ) d x =
= 1 3 ∫ 0 1 ( x − 0.39 ) ( − 0.44 x + 0.22 ) d x = 1 3 ( − 0.15 + 0.11 + 0.09 − 0.09 ) = − 0.013 =\frac{1}{3}\intop^1_0(x-0.39)(-0.44x+0.22)dx=\frac{1}{3}(-0.15+0.11+0.09-0.09)=-0.013 = 3 1 ∫ 0 1 ( x − 0.39 ) ( − 0.44 x + 0.22 ) d x = 3 1 ( − 0.15 + 0.11 + 0.09 − 0.09 ) = − 0.013
a 1 = − 0.013 / 0.35 6 2 = − 0.1 a_1=-0.013/0.356^2=-0.1 a 1 = − 0.013/0.35 6 2 = − 0.1
b 1 = 11 / 9 + 0.1 ⋅ 7 / 18 = 1.26 b_1=11/9+0.1\cdot7/18=1.26 b 1 = 11/9 + 0.1 ⋅ 7/18 = 1.26
y = − 0.1 x + 1.26 y=-0.1x+1.26 y = − 0.1 x + 1.26
a 2 = − 0.013 / 0.53 3 2 = − 0.05 a_2=-0.013/0.533^2=-0.05 a 2 = − 0.013/0.53 3 2 = − 0.05
b 1 = 7 / 18 + 0.05 ⋅ 11 / 9 = 0.45 b_1=7/18+0.05\cdot11/9=0.45 b 1 = 7/18 + 0.05 ⋅ 11/9 = 0.45
x = − 0.05 y + 0.45 x=-0.05y+0.45 x = − 0.05 y + 0.45
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