Respuesta :
Answer:
Part 2: The probability of X≤2 or X≥4 is 0.5.
Part 3: The value of marginal probability of y is [tex]f_y(y)=\frac{10-y}{32}[/tex] for [tex]2\leq y\leq 10[/tex]
Part 4:The value of E(y) is 4.6667.
Part 5:The value of [tex]f_{xy}(x)[/tex] is [tex]\frac{2}{10-y}[/tex] for [tex]2\leq y\leq 2x\leq 10[/tex]
Part 6:The value of [tex]M_{x,y}(y)[/tex] is [tex]\frac{y+10}{4}[/tex]
Part 7:The value of E(x) is 3.6667.
Part 8:The value of E(x,y) is 36.
Part 9:The value of Cov(x,y) is 18.8886.
Part 10:X and Y are not independent variables as [tex]f_{xy}(x,y)\neq f_x(x).f_y(y)\\[/tex]
Step-by-step explanation:
As the complete question is here, however some of the values are not readable, thus the question is found online and is attached herewith.
From the given data, the joint distribution is given as
[tex]f(x,y)=\frac{1}{16}[/tex] for [tex]2\leq y\leq 2x\leq 10[/tex]
Now the distribution of x is given as
[tex]f_x(x)=\int\limits^{\infty}_{-\infty} {f(x,y)} \, dy[/tex]
Here the limits for y are [tex]2\leq y\leq 2x[/tex] So the equation becomes
[tex]f_x(x)=\int\limits^{\infty}_{-\infty} {f(x,y)} \, dy\\f_x(x)=\int\limits^{2x}_{2} \frac{1}{16} \, dy\\f_x(x)=\frac{1}{16} (2x-2)\\f_x(x)=\frac{x-1}{8} \,\,\,\,\,\,\,\,\,\,\,\, for \,\,\,\,\,\,\,\,\,\ 1\leq x\leq 5[/tex]
Part 2:
The probability is given as
[tex]P(X\leq 2 U X\geq 4)=\int\limits^2_1 {f_x(x)} \, dx +\int\limits^5_4 {f_x(x)} \, dx\\P(X\leq 2 U X\geq 4)=\int\limits^2_1 {\frac{x-1}{8}} \, dx +\int\limits^5_4 {\frac{x-1}{8}} \, dx\\P(X\leq 2 U X\geq 4)=\frac{1}{16}+\frac{7}{16}\\P(X\leq 2 U X\geq 4)=0.5[/tex]
So the probability of X≤2 or X≥4 is 0.5.
Part 3:
The distribution of y is given as
[tex]f_y(y)=\int\limits^{\infty}_{-\infty} {f(x,y)} \, dx[/tex]
Here the limits for x are [tex]y/2\leq x\leq 5[/tex] So the equation becomes
[tex]f_y(y)=\int\limits^{\infty}_{-\infty} {f(x,y)} \, dx\\f_y(y)=\int\limits^{5}_{y/2} \frac{1}{16} \, dx\\f_y(y)=\frac{1}{16} (5-\frac{y}{2})\\f_y(y)=\frac{10-y}{32} \,\,\,\,\,\,\,\,\,\,\,\, for \,\,\,\,\,\,\,\,\,\ 2\leq y\leq 10[/tex]
So the value of marginal probability of y is [tex]f_y(y)=\frac{10-y}{32}[/tex] for [tex]2\leq y\leq 10[/tex]
Part 4
The value is given as
[tex]E(y)=\int\limits^{10}_2 {yf_y(y)} \, dy\\E(y)=\int\limits^{10}_2 {y\frac{10-y}{32}} \, dy\\E(y)=\frac{1}{32}\int\limits^{10}_2 {10y-y^2} \, dy\\E(y)=4.6667[/tex]
So the value of E(y) is 4.6667.
Part 5
This is given as
[tex]f_{xy}(x)=\frac{f_{xy}(x,y)}{f_y(y)}\\f_{xy}(x)=\frac{\frac{1}{16}}{\frac{10-y}{32}}\\f_{xy}(x)=\frac{2}{10-y}[/tex]
So the value of [tex]f_{xy}(x)[/tex] is [tex]\frac{2}{10-y}[/tex] for [tex]2\leq y\leq 2x\leq 10[/tex]
Part 6
The value is given as
[tex]\geq M_{x,y}(y)=E(f_{xy}(x))=\int\limits^5_{y/2} {x f_{xy}(x)} \, dx \\M_{x,y}(y)=\int\limits^5_{y/2} {x \frac{2}{10-y}} \, dx \\M_{x,y}(y)=\frac{2}{10-y}\left[\frac{x^2}{2}\right]^5_{\frac{y}{2}}\\M_{x,y}(y)=\frac{2}{10-y}\left(\frac{25}{2}-\frac{y^2}{8}\right)\\M_{x,y}(y)=\frac{y+10}{4}[/tex]
So the value of [tex]M_{x,y}(y)[/tex] is [tex]\frac{y+10}{4}[/tex]
Part 7
The value is given as
[tex]E(x)=\int\limits^{5}_1 {xf_x(x)} \, dx\\E(x)=\int\limits^{5}_1 {x\frac{x-1}{8}} \, dx\\E(x)=\frac{1}{8}\left(\frac{124}{3}-12\right)\\E(x)=\frac{11}{3} =3.6667[/tex]
So the value of E(x) is 3.6667.
Part 8
The value is given as
[tex]E(x,y)=\int\limits^{5}_1 \int\limits^{10}_2 {xyf_{x,y}(x,y)} \,dy\, dx\\E(x,y)=\int\limits^{5}_1 \int\limits^{10}_2 {xy\frac{1}{16}} \,dy\, dx\\E(x,y)=\int\limits^{5}_1 \frac{x}{16}\left[\frac{y^2}{2}\right]^{10}_2\, dx\\E(x,y)=\int\limits^{5}_1 3x\, dx\\\\E(x,y)=3\left[\frac{x^2}{2}\right]^5_1\\E(x,y)=36[/tex]
So the value of E(x,y) is 36
Part 9
The value is given as
[tex]Cov(X,Y)=E(x,y)-E(x)E(y)\\Cov(X,Y)=36-(3.6667)(4.6667)\\Cov(X,Y)=18.8886\\[/tex]
So the value of Cov(x,y) is 18.8886
Part 10
The variables X and Y are considered independent when
[tex]f_{xy}(x,y)=f_x(x).f_y(y)\\[/tex]
Here
[tex]f_x(x).f_y(y)=\frac{x-1}{8}\frac{10-y}{32} \\[/tex]
And
[tex]f_{xy}(x,y)=\frac{1}{16}[/tex]
As these two values are not equal, this indicates that X and Y are not independent variables.
