题目
Problem
A spinner can land on the numbers 10, 12, 14 and 16 only and the probability of the spinner landing on each number is the same.
The random variable X X X represents the number that the spinner lands on when it is spun once.
(a) State the name of the probability distribution of X X X .
(1)
(b) (i) Write down the value of E ( X ) E(X) E ( X )
(1)
(ii) Find Var ( X ) \operatorname{Var}(X) Var ( X )
(2)
A second spinner can land on the numbers 1, 2, 3, 4 and 5 only.
The random variable Y Y Y represents the number that this spinner lands on when it is spun once. The probability distribution of Y Y Y is given in the table below
y y y 1 2 3 4 5 P ( Y = y ) P(Y=y) P ( Y = y ) 4 30 \frac4{30} 30 4 9 30 \frac9{30} 30 9 6 30 \frac6{30} 30 6 5 30 \frac5{30} 30 5 6 30 \frac6{30} 30 6
(c) Find
(i) E ( Y ) E(Y) E ( Y )
(2)
(ii) Var ( Y ) \operatorname{Var}(Y) Var ( Y )
(3)
The random variable W = a X + b W=aX+b W = a X + b , where a a a and b b b are constants and a > 0 a>0 a > 0
Given that E ( W ) = E ( Y ) E(W)=E(Y) E ( W ) = E ( Y ) and Var ( W ) = Var ( Y ) \operatorname{Var}(W)=\operatorname{Var}(Y) Var ( W ) = Var ( Y )
(d) find the value of a a a and the value of b b b .
(5)
Each of the two spinners is spun once.
(e) Find P ( W = Y ) P(W=Y) P ( W = Y )
(2)
解答
(a)
解法一
思路
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四个可能结果概率相同,所以是离散均匀分布。
答题过程
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X X X has a discrete uniform distribution.
(b)
解法一
思路
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均匀分布的期望可以直接取平均;方差用 E ( X 2 ) − [ E ( X ) ] 2 E(X^2)-[E(X)]^2 E ( X 2 ) − [ E ( X ) ] 2 。
答题过程
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By symmetry,
E ( X ) = 13. \begin{align*}
E(X)=13.
\end{align*} E ( X ) = 13.
Also,
E ( X 2 ) = 10 2 + 12 2 + 14 2 + 16 2 4 = 174. E(X^2)=\frac{10^2+12^2+14^2+16^2}{4}
=174. E ( X 2 ) = 4 1 0 2 + 1 2 2 + 1 4 2 + 1 6 2 = 174.
Therefore
Var ( X ) = 174 − 13 2 = 5. \begin{align*}
\operatorname{Var}(X)=174-13^2=5.
\end{align*} Var ( X ) = 174 − 1 3 2 = 5.
(c)
解法一
思路
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先算 E ( Y ) E(Y) E ( Y ) ,再算 E ( Y 2 ) E(Y^2) E ( Y 2 ) ,最后相减求方差。
答题过程
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E ( Y ) = 1 ( 4 ) + 2 ( 9 ) + 3 ( 6 ) + 4 ( 5 ) + 5 ( 6 ) 30 = 90 30 = 3. \begin{aligned}
E(Y)
=&\,\frac{1(4)+2(9)+3(6)+4(5)+5(6)}{30}\\
=&\,\frac{90}{30}=3.
\end{aligned} E ( Y ) = = 30 1 ( 4 ) + 2 ( 9 ) + 3 ( 6 ) + 4 ( 5 ) + 5 ( 6 ) 30 90 = 3.
Also,
E ( Y 2 ) = 1 2 ( 4 ) + 2 2 ( 9 ) + 3 2 ( 6 ) + 4 2 ( 5 ) + 5 2 ( 6 ) 30 = 324 30 = 10.8. \begin{aligned}
E(Y^2)
=&\,\frac{1^2(4)+2^2(9)+3^2(6)+4^2(5)+5^2(6)}{30}\\
=&\,\frac{324}{30}=10.8.
\end{aligned} E ( Y 2 ) = = 30 1 2 ( 4 ) + 2 2 ( 9 ) + 3 2 ( 6 ) + 4 2 ( 5 ) + 5 2 ( 6 ) 30 324 = 10.8.
Therefore
Var ( Y ) = 10.8 − 3 2 = 1.8. \begin{align*}
\operatorname{Var}(Y)=10.8-3^2=1.8.
\end{align*} Var ( Y ) = 10.8 − 3 2 = 1.8.
(d)
解法一
思路
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用线性变换公式建立方程。方差方程先给出 a a a ,再用期望方程求 b b b 。
答题过程
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Since W = a X + b W=aX+b W = a X + b ,
E ( W ) = a E ( X ) + b = 13 a + b . \begin{align*}
E(W)=aE(X)+b=13a+b.
\end{align*} E ( W ) = a E ( X ) + b = 13 a + b .
Given E ( W ) = E ( Y ) = 3 E(W)=E(Y)=3 E ( W ) = E ( Y ) = 3 ,
13 a + b = 3. \begin{align*}
13a+b=3.
\end{align*} 13 a + b = 3.
Also,
Var ( W ) = a 2 Var ( X ) = 5 a 2 . \begin{align*}
\operatorname{Var}(W)=a^2\operatorname{Var}(X)=5a^2.
\end{align*} Var ( W ) = a 2 Var ( X ) = 5 a 2 .
Given Var ( W ) = Var ( Y ) = 1.8 \operatorname{Var}(W)=\operatorname{Var}(Y)=1.8 Var ( W ) = Var ( Y ) = 1.8 ,
5 a 2 = 1.8. \begin{align*}
5a^2=1.8.
\end{align*} 5 a 2 = 1.8.
Since a > 0 a>0 a > 0 ,
a = 1.8 5 = 0.6. \begin{align*}
a=\sqrt{\frac{1.8}{5}}=0.6.
\end{align*} a = 5 1.8 = 0.6.
Then
13 ( 0.6 ) + b = 3 , \begin{align*}
13(0.6)+b=3,
\end{align*} 13 ( 0.6 ) + b = 3 ,
so
b = − 4.8. \begin{align*}
b=-4.8.
\end{align*} b = − 4.8.
(e)
解法一
思路
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把 X = 10 , 12 , 14 , 16 X=10,12,14,16 X = 10 , 12 , 14 , 16 代入 W = 0.6 X − 4.8 W=0.6X-4.8 W = 0.6 X − 4.8 。这些 W W W 值都不是 Y Y Y 可取的整数值。
答题过程
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The possible values of W W W are
0.6 ( 10 ) − 4.8 = 1.2 , 0.6 ( 12 ) − 4.8 = 2.4 , 0.6 ( 14 ) − 4.8 = 3.6 , 0.6 ( 16 ) − 4.8 = 4.8. \begin{aligned}
0.6(10)-4.8=&\,1.2,\\
0.6(12)-4.8=&\,2.4,\\
0.6(14)-4.8=&\,3.6,\\
0.6(16)-4.8=&\,4.8.
\end{aligned} 0.6 ( 10 ) − 4.8 = 0.6 ( 12 ) − 4.8 = 0.6 ( 14 ) − 4.8 = 0.6 ( 16 ) − 4.8 = 1.2 , 2.4 , 3.6 , 4.8.
Since Y Y Y can only take the values 1 , 2 , 3 , 4 , 5 1,2,3,4,5 1 , 2 , 3 , 4 , 5 , there are no cases where W = Y W=Y W = Y .
Therefore
P ( W = Y ) = 0. \begin{align*}
P(W=Y)=0.
\end{align*} P ( W = Y ) = 0.