In: Statistics and Probability
The General Social Survey asked a sample of adults how many siblings (brothers and sisters) they had (X) and also how many children they had (Y). We show results for those who had no more than 4 children and no more than 4 siblings. Assume that the joint probability mass function is given in the following contingency table:
y | |||||
x | 0 | 1 | 2 | 3 | 4 |
0 | 0.03 | 0.01 | 0.02 | 0.01 | 0.01 |
1 | 0.09 | 0.05 | 0.08 | 0.03 | 0.01 |
2 | 0.08 | 0.05 | 0.07 | 0.04 | 0.02 |
3 | 0.06 | 0.04 | 0.08 | 0.04 | 0.02 |
4 | 0.04 | 0.03 | 0.04 | 0.03 | 0.02 |
NOTE: This is a multi-part question. Once an answer is submitted, you will be unable to return to this part.
Find σX + Y.
σX + Y =
from given data:
x | y | f(x,y) | x*f(x,y) | y*f(x,y) | x^2f(x,y) | y^2f(x,y) | xy*f(x,y) |
0 | 0 | 0.03 | 0 | 0 | 0 | 0 | 0 |
1 | 0 | 0.09 | 0.09 | 0 | 0.09 | 0 | 0 |
2 | 0 | 0.08 | 0.16 | 0 | 0.32 | 0 | 0 |
3 | 0 | 0.06 | 0.18 | 0 | 0.54 | 0 | 0 |
4 | 0 | 0.04 | 0.16 | 0 | 0.64 | 0 | 0 |
0 | 1 | 0.01 | 0 | 0.01 | 0 | 0.01 | 0 |
1 | 1 | 0.05 | 0.05 | 0.05 | 0.05 | 0.05 | 0.05 |
2 | 1 | 0.05 | 0.1 | 0.05 | 0.2 | 0.05 | 0.1 |
3 | 1 | 0.04 | 0.12 | 0.04 | 0.36 | 0.04 | 0.12 |
4 | 1 | 0.03 | 0.12 | 0.03 | 0.48 | 0.03 | 0.12 |
0 | 2 | 0.02 | 0 | 0.04 | 0 | 0.08 | 0 |
1 | 2 | 0.08 | 0.08 | 0.16 | 0.08 | 0.32 | 0.16 |
2 | 2 | 0.07 | 0.14 | 0.14 | 0.28 | 0.28 | 0.28 |
3 | 2 | 0.08 | 0.24 | 0.16 | 0.72 | 0.32 | 0.48 |
4 | 2 | 0.04 | 0.16 | 0.08 | 0.64 | 0.16 | 0.32 |
0 | 3 | 0.01 | 0 | 0.03 | 0 | 0.09 | 0 |
1 | 3 | 0.03 | 0.03 | 0.09 | 0.03 | 0.27 | 0.09 |
2 | 3 | 0.04 | 0.08 | 0.12 | 0.16 | 0.36 | 0.24 |
3 | 3 | 0.04 | 0.12 | 0.12 | 0.36 | 0.36 | 0.36 |
4 | 3 | 0.03 | 0.12 | 0.09 | 0.48 | 0.27 | 0.36 |
0 | 4 | 0.01 | 0 | 0.04 | 0 | 0.16 | 0 |
1 | 4 | 0.01 | 0.01 | 0.04 | 0.01 | 0.16 | 0.04 |
2 | 4 | 0.02 | 0.04 | 0.08 | 0.08 | 0.32 | 0.16 |
3 | 4 | 0.02 | 0.06 | 0.08 | 0.18 | 0.32 | 0.24 |
4 | 4 | 0.02 | 0.08 | 0.08 | 0.32 | 0.32 | 0.32 |
Total | 1 | 2.14 | 1.53 | 6.02 | 3.97 | 3.44 | |
E(X)=ΣxP(x,y)= | 2.14 | ||||||
E(X2)=Σx2P(x,y)= | 6.02 | ||||||
E(Y)=ΣyP(x,y)= | 1.53 | ||||||
E(Y2)=Σy2P(x,y)= | 3.97 | ||||||
Var(X)=E(X2)-(E(X))2= | 1.44 | ||||||
Var(Y)=E(Y2)-(E(Y))2= | 1.63 | ||||||
E(XY)=ΣxyP(x,y)= | 3.44 | ||||||
Cov(X,Y)=E(XY)-E(X)*E(Y)= | 0.1658 |
σX + Y = sqrt(Var(x)+Var(Y)+2*Cov(x,y)) =sqrt(1.44+1.63+2*0.1658)
=1.8442