The answer to your question is letter “D”
I don't know either. sorry.
Answer: I need help on this tooooooo
Step-by-step explanation:
An integer is a whole number that can be positive, negative, or zero.
Answer:
The expected value of X is
and the variance of X is ![Var(X)=\frac{226192}{5329} \approx 42.45](https://tex.z-dn.net/?f=Var%28X%29%3D%5Cfrac%7B226192%7D%7B5329%7D%20%5Capprox%2042.45)
The expected value of Y is
and the variance of Y is ![Var(Y)=\frac{179}{4} \approx 44.75](https://tex.z-dn.net/?f=Var%28Y%29%3D%5Cfrac%7B179%7D%7B4%7D%20%5Capprox%2044.75)
Step-by-step explanation:
(a) Let X be a discrete random variable with set of possible values D and probability mass function p(x). The expected value, denoted by E(X) or
, is
![E(X)=\sum_{x\in D} x\cdot p(x)](https://tex.z-dn.net/?f=E%28X%29%3D%5Csum_%7Bx%5Cin%20D%7D%20x%5Ccdot%20p%28x%29)
The probability mass function
of X is given by
![p_{X}(28)=\frac{28}{146} \\\\p_{X}(32)=\frac{32}{146} \\\\p_{X}(42)=\frac{42}{146} \\\\p_{X}(44)=\frac{44}{146}](https://tex.z-dn.net/?f=p_%7BX%7D%2828%29%3D%5Cfrac%7B28%7D%7B146%7D%20%5C%5C%5C%5Cp_%7BX%7D%2832%29%3D%5Cfrac%7B32%7D%7B146%7D%20%5C%5C%5C%5Cp_%7BX%7D%2842%29%3D%5Cfrac%7B42%7D%7B146%7D%20%5C%5C%5C%5Cp_%7BX%7D%2844%29%3D%5Cfrac%7B44%7D%7B146%7D)
Since the bus driver is equally likely to drive any of the 4 buses, the probability mass function
of Y is given by
![p_{Y}(28)=p_{Y}(32)=p_{Y}(42)=p_{Y}(44)=\frac{1}{4}](https://tex.z-dn.net/?f=p_%7BY%7D%2828%29%3Dp_%7BY%7D%2832%29%3Dp_%7BY%7D%2842%29%3Dp_%7BY%7D%2844%29%3D%5Cfrac%7B1%7D%7B4%7D)
The expected value of X is
![E(X)=\sum_{x\in [28,32,42,44]} x\cdot p_{X}(x)](https://tex.z-dn.net/?f=E%28X%29%3D%5Csum_%7Bx%5Cin%20%5B28%2C32%2C42%2C44%5D%7D%20x%5Ccdot%20p_%7BX%7D%28x%29)
![E(X)=28\cdot \frac{28}{146}+32\cdot \frac{32}{146} +42\cdot \frac{42}{146} +44 \cdot \frac{44}{146}\\\\E(X)=\frac{392}{73}+\frac{512}{73}+\frac{882}{73}+\frac{968}{73}\\\\E(X)=\frac{2754}{73} \approx 37.73](https://tex.z-dn.net/?f=E%28X%29%3D28%5Ccdot%20%5Cfrac%7B28%7D%7B146%7D%2B32%5Ccdot%20%5Cfrac%7B32%7D%7B146%7D%20%2B42%5Ccdot%20%5Cfrac%7B42%7D%7B146%7D%20%2B44%20%5Ccdot%20%5Cfrac%7B44%7D%7B146%7D%5C%5C%5C%5CE%28X%29%3D%5Cfrac%7B392%7D%7B73%7D%2B%5Cfrac%7B512%7D%7B73%7D%2B%5Cfrac%7B882%7D%7B73%7D%2B%5Cfrac%7B968%7D%7B73%7D%5C%5C%5C%5CE%28X%29%3D%5Cfrac%7B2754%7D%7B73%7D%20%5Capprox%2037.73)
The expected value of Y is
![E(Y)=\sum_{x\in [28,32,42,44]} x\cdot p_{Y}(x)](https://tex.z-dn.net/?f=E%28Y%29%3D%5Csum_%7Bx%5Cin%20%5B28%2C32%2C42%2C44%5D%7D%20x%5Ccdot%20p_%7BY%7D%28x%29)
![E(Y)=28\cdot \frac{1}{4}+32\cdot \frac{1}{4} +42\cdot \frac{1}{4} +44 \cdot \frac{1}{4}\\\\E(Y)=146\cdot \frac{1}{4}\\\\E(Y)=\frac{73}{2} \approx 36.5](https://tex.z-dn.net/?f=E%28Y%29%3D28%5Ccdot%20%5Cfrac%7B1%7D%7B4%7D%2B32%5Ccdot%20%5Cfrac%7B1%7D%7B4%7D%20%2B42%5Ccdot%20%5Cfrac%7B1%7D%7B4%7D%20%2B44%20%5Ccdot%20%5Cfrac%7B1%7D%7B4%7D%5C%5C%5C%5CE%28Y%29%3D146%5Ccdot%20%5Cfrac%7B1%7D%7B4%7D%5C%5C%5C%5CE%28Y%29%3D%5Cfrac%7B73%7D%7B2%7D%20%5Capprox%2036.5)
(b) Let X have probability mass function p(x) and expected value E(X). Then the variance of X, denoted by V(X), is
![V(X)=\sum_{x\in D} (x-\mu)^2\cdot p(x)=E(X^2)-[E(X)]^2](https://tex.z-dn.net/?f=V%28X%29%3D%5Csum_%7Bx%5Cin%20D%7D%20%28x-%5Cmu%29%5E2%5Ccdot%20p%28x%29%3DE%28X%5E2%29-%5BE%28X%29%5D%5E2)
The variance of X is
![E(X^2)=\sum_{x\in [28,32,42,44]} x^2\cdot p_{X}(x)](https://tex.z-dn.net/?f=E%28X%5E2%29%3D%5Csum_%7Bx%5Cin%20%5B28%2C32%2C42%2C44%5D%7D%20x%5E2%5Ccdot%20p_%7BX%7D%28x%29)
![E(X^2)=28^2\cdot \frac{28}{146}+32^2\cdot \frac{32}{146} +42^2\cdot \frac{42}{146} +44^2 \cdot \frac{44}{146}\\\\E(X^2)=\frac{10976}{73}+\frac{16384}{73}+\frac{37044}{73}+\frac{42592}{73}\\\\E(X^2)=\frac{106996}{73}](https://tex.z-dn.net/?f=E%28X%5E2%29%3D28%5E2%5Ccdot%20%5Cfrac%7B28%7D%7B146%7D%2B32%5E2%5Ccdot%20%5Cfrac%7B32%7D%7B146%7D%20%2B42%5E2%5Ccdot%20%5Cfrac%7B42%7D%7B146%7D%20%2B44%5E2%20%5Ccdot%20%5Cfrac%7B44%7D%7B146%7D%5C%5C%5C%5CE%28X%5E2%29%3D%5Cfrac%7B10976%7D%7B73%7D%2B%5Cfrac%7B16384%7D%7B73%7D%2B%5Cfrac%7B37044%7D%7B73%7D%2B%5Cfrac%7B42592%7D%7B73%7D%5C%5C%5C%5CE%28X%5E2%29%3D%5Cfrac%7B106996%7D%7B73%7D)
![Var(X)=E(X^2)-(E(X))^2\\\\Var(X)=\frac{106996}{73}-(\frac{2754}{73})^2\\\\Var(X)=\frac{106996}{73}-\frac{7584516}{5329}\\\\Var(X)=\frac{7810708}{5329}-\frac{7584516}{5329}\\\\Var(X)=\frac{226192}{5329} \approx 42.45](https://tex.z-dn.net/?f=Var%28X%29%3DE%28X%5E2%29-%28E%28X%29%29%5E2%5C%5C%5C%5CVar%28X%29%3D%5Cfrac%7B106996%7D%7B73%7D-%28%5Cfrac%7B2754%7D%7B73%7D%29%5E2%5C%5C%5C%5CVar%28X%29%3D%5Cfrac%7B106996%7D%7B73%7D-%5Cfrac%7B7584516%7D%7B5329%7D%5C%5C%5C%5CVar%28X%29%3D%5Cfrac%7B7810708%7D%7B5329%7D-%5Cfrac%7B7584516%7D%7B5329%7D%5C%5C%5C%5CVar%28X%29%3D%5Cfrac%7B226192%7D%7B5329%7D%20%5Capprox%2042.45)
The variance of Y is
![E(Y^2)=\sum_{x\in [28,32,42,44]} x^2\cdot p_{Y}(x)](https://tex.z-dn.net/?f=E%28Y%5E2%29%3D%5Csum_%7Bx%5Cin%20%5B28%2C32%2C42%2C44%5D%7D%20x%5E2%5Ccdot%20p_%7BY%7D%28x%29)
![E(Y^2)=28^2\cdot \frac{1}{4}+32^2\cdot \frac{1}{4} +42^2\cdot \frac{1}{4} +44^2 \cdot \frac{1}{4}\\\\E(Y^2)=196+256+441+484\\\\E(Y^2)=1377](https://tex.z-dn.net/?f=E%28Y%5E2%29%3D28%5E2%5Ccdot%20%5Cfrac%7B1%7D%7B4%7D%2B32%5E2%5Ccdot%20%5Cfrac%7B1%7D%7B4%7D%20%2B42%5E2%5Ccdot%20%5Cfrac%7B1%7D%7B4%7D%20%2B44%5E2%20%5Ccdot%20%5Cfrac%7B1%7D%7B4%7D%5C%5C%5C%5CE%28Y%5E2%29%3D196%2B256%2B441%2B484%5C%5C%5C%5CE%28Y%5E2%29%3D1377)
![Var(Y)=E(Y^2)-(E(Y))^2\\\\Var(Y)=1377-(\frac{73}{2})^2\\\\Var(Y)=1377-\frac{5329}{4}\\\\Var(Y)=\frac{179}{4} \approx 44.75](https://tex.z-dn.net/?f=Var%28Y%29%3DE%28Y%5E2%29-%28E%28Y%29%29%5E2%5C%5C%5C%5CVar%28Y%29%3D1377-%28%5Cfrac%7B73%7D%7B2%7D%29%5E2%5C%5C%5C%5CVar%28Y%29%3D1377-%5Cfrac%7B5329%7D%7B4%7D%5C%5C%5C%5CVar%28Y%29%3D%5Cfrac%7B179%7D%7B4%7D%20%5Capprox%2044.75)