题目
Problem
The heights of adult Indian elephants are normally distributed with mean 2.74 m 2.74\,\mathrm{m} 2.74 m and standard deviation 0.25 m 0.25\,\mathrm{m} 0.25 m .
A random sample of 250 of these elephants is chosen.
(a) How many of these 250 elephants would you expect to have a height between 2.40 m 2.40\,\mathrm{m} 2.40 m and 3.10 m 3.10\,\mathrm{m} 3.10 m ?
(4)
The heights of adult African elephants are normally distributed with mean μ m \mu\,\mathrm{m} μ m and standard deviation 0.28 m 0.28\,\mathrm{m} 0.28 m . It is known that 62% of adult African elephants have a height greater than 3.15 m 3.15\,\mathrm{m} 3.15 m .
(b) Find the value of μ \mu μ .
(3)
A random sample of 90 adult African elephants is chosen.
(c) Use an approximation to find the probability that more than 50 of these 90 elephants have a height greater than 3.15 m 3.15\,\mathrm{m} 3.15 m .
(5)
题目中文翻译
成年印度象的身高服从均值为 2.74 m 2.74\,\mathrm{m} 2.74 m 、标准差为 0.25 m 0.25\,\mathrm{m} 0.25 m 的正态分布。
从这些大象中随机抽取 250 头作为样本。
(a) 预计这 250 头大象中,有多少头的身高在 2.40 m 2.40\,\mathrm{m} 2.40 m 与 3.10 m 3.10\,\mathrm{m} 3.10 m 之间?
(4)
成年非洲象的身高服从均值为 μ m \mu\,\mathrm{m} μ m 、标准差为 0.28 m 0.28\,\mathrm{m} 0.28 m 的正态分布。已知 62% 的成年非洲象身高大于 3.15 m 3.15\,\mathrm{m} 3.15 m 。
(b) 求 μ \mu μ 的值。
(3)
随机抽取 90 头成年非洲象作为样本。
(c) 利用近似方法,求这 90 头大象中超过 50 头的身高大于 3.15 m 3.15\,\mathrm{m} 3.15 m 的概率。
(5)
原卷通用作答要求
Answer all questions. You should use a calculator where appropriate.
You must show all necessary working clearly; no marks will be given for unsupported answers from a calculator.
Give non-exact numerical answers correct to 3 significant figures, or 1 decimal place for angles in degrees, unless a different level of accuracy is specified in the question.
请回答所有题目。适当时应使用计算器。必须清楚展示所有必要的计算过程;仅给出计算器答案而没有过程,不予评分。除非题目另有精度要求,非精确数值答案保留 3 位有效数字,以度为单位的角度保留 1 位小数。
解答
(a)
解法一
思路
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先将两个身高边界标准化,算出区间概率,再乘样本数。预计头数取整数。
答题过程
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Let H H H be the height, in metres, of an adult Indian elephant. Then H ∼ N ( 2.74 , 0.25 2 ) H\sim\mathrm{N}(2.74,0.25^2) H ∼ N ( 2.74 , 0.2 5 2 ) .
For Z ∼ N ( 0 , 1 ) Z\sim\mathrm{N}(0,1) Z ∼ N ( 0 , 1 ) ,
P ( 2.40 < H < 3.10 ) = P ( 2.40 − 2.74 0.25 < Z < 3.10 − 2.74 0.25 ) = P ( − 1.36 < Z < 1.44 ) = Φ ( 1.44 ) − Φ ( − 1.36 ) = 0.838151 … . \begin{align*}
\mathrm{P}(2.40<H<3.10)
=&\,\mathrm{P}\biggl(\frac{2.40-2.74}{0.25}<Z
<\frac{3.10-2.74}{0.25}\biggr)\\[4mm]
=&\,\mathrm{P}(-1.36<Z<1.44)\\[4mm]
=&\,\Phi(1.44)-\Phi(-1.36)\\[4mm]
=&\,0.838151\ldots.
\end{align*} P ( 2.40 < H < 3.10 ) = = = = P ( 0.25 2.40 − 2.74 < Z < 0.25 3.10 − 2.74 ) P ( − 1.36 < Z < 1.44 ) Φ ( 1.44 ) − Φ ( − 1.36 ) 0.838151 … .
The expected number is 250 × 0.838151 … = 209.54 … 250\times0.838151\ldots=209.54\ldots 250 × 0.838151 … = 209.54 … , giving 210 elephants .
(b)
解法一
思路
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右尾概率为0.62,意味着3.15低于均值,其左侧累计概率为0.38。先找对应的负标准分数再求均值。
答题过程
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Let H H H be the height, in metres, of an adult African elephant. Since P ( H > 3.15 ) = 0.62 \mathrm{P}(H>3.15)=0.62 P ( H > 3.15 ) = 0.62 ,
P ( H ≤ 3.15 ) = 0.38. \mathrm{P}(H\leq3.15)=0.38. P ( H ≤ 3.15 ) = 0.38.
The corresponding standard normal quantile is Φ − 1 ( 0.38 ) = − 0.30548 … \Phi^{-1}(0.38)=-0.30548\ldots Φ − 1 ( 0.38 ) = − 0.30548 … . Therefore,
3.15 − μ 0.28 = − 0.30548 … , μ = 3.15 + 0.28 ( 0.30548 … ) = 3.2355 … ≈ 3.24 . \begin{align*}
\frac{3.15-\mu}{0.28}=&\,-0.30548\ldots,\\[4mm]
\mu=&\,3.15+0.28(0.30548\ldots)\\[4mm]
=&\,3.2355\ldots\\[4mm]
\approx&\,\boxed{3.24}.
\end{align*} 0.28 3.15 − μ = μ = = ≈ − 0.30548 … , 3.15 + 0.28 ( 0.30548 … ) 3.2355 … 3.24 .
(c)
解法一
思路
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样本中超过指定身高的头数服从二项分布。均值和方差足够大,可用正态近似;“超过50”从51开始,连续性修正后的边界是50.5。
答题过程
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Let X X X be the number of elephants in the sample with height greater than 3.15 m 3.15\,\mathrm{m} 3.15 m . Then
X ∼ B ( 90 , 0.62 ) . X\sim\mathrm{B}(90,0.62). X ∼ B ( 90 , 0.62 ) .
Here n p = 55.8 np=55.8 n p = 55.8 and n ( 1 − p ) = 34.2 n(1-p)=34.2 n ( 1 − p ) = 34.2 are both greater than 5, so a normal approximation is suitable. Its mean and variance are
μ X = 90 ( 0.62 ) = 55.8 , σ X 2 = 90 ( 0.62 ) ( 0.38 ) = 21.204. \begin{align*}
\mu_X=&\,90(0.62)=55.8,\\[4mm]
\sigma_X^2=&\,90(0.62)(0.38)=21.204.
\end{align*} μ X = σ X 2 = 90 ( 0.62 ) = 55.8 , 90 ( 0.62 ) ( 0.38 ) = 21.204.
Use Y ∼ N ( 55.8 , 21.204 ) Y\sim\mathrm{N}(55.8,21.204) Y ∼ N ( 55.8 , 21.204 ) . Applying a continuity correction,
P ( X > 50 ) = P ( X ≥ 51 ) ≈ P ( Y > 50.5 ) = P ( Z > 50.5 − 55.8 21.204 ) = P ( Z > − 1.15098 … ) = 0.8751 … ≈ 0.875 . \begin{align*}
\mathrm{P}(X>50)=&\,\mathrm{P}(X\geq51)\\[4mm]
\approx&\,\mathrm{P}(Y>50.5)\\[4mm]
=&\,\mathrm{P}\biggl(Z>\frac{50.5-55.8}{\sqrt{21.204}}\biggr)\\[4mm]
=&\,\mathrm{P}(Z>-1.15098\ldots)\\[4mm]
=&\,0.8751\ldots\\[4mm]
\approx&\,\boxed{0.875}.
\end{align*} P ( X > 50 ) = ≈ = = = ≈ P ( X ≥ 51 ) P ( Y > 50.5 ) P ( Z > 21.204 50.5 − 55.8 ) P ( Z > − 1.15098 … ) 0.8751 … 0.875 .