若\(X\sim N(\mu,\sigma^2)\),则新的随机变量\(Z=\dfrac{X-\mu}{\sigma}\sim N(0,1)\). 以下证明是否正确? 证明:记\(Z=\dfrac{X-\mu}{\sigma}\),则其分布函数\[F_Z(z)=P\{Z\le z\}=P\left\{\dfrac{X-\mu}{\sigma}\le z\right\}=P\{X\le \sigma z+\mu\}=F_X(\sigma z+\mu).\]于是\[f_Z(z)=\frac{d}{dz}{\left[F_Z(z)\right]}=\frac{d}{dz}{\left[F_X(\sigma z+\mu)\right]}=f_X(\sigma z+\mu)\cdot \sigma\\\qquad\qquad=\frac{1}{\sqrt{2\pi}\sigma}e^{-\dfrac{(\sigma z+\mu-\mu)^2}{2\sigma^2}}\cdot \sigma=\frac{1}{\sqrt{2\pi}}e^{-\dfrac{z^2}{2}}=\varphi(z).\]即\(Z=\dfrac{X-\mu}{\sigma}\sim N(0,1)\).
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若\(X\sim N(\mu,\sigma^2)\),则其分布函数\(F_X(x)=P\{X\le x\}=P\left\{\dfrac{X-\mu}{\sigma}\le \dfrac{x-\mu}{\sigma}\right\}\),而\(\dfrac{X-\mu}{\sigma}\sim N(0,1)\),于是\(F_X(x)=\varPhi(\dfrac{x-\mu}{\sigma})\).
积分\(\int_0^1{e^{\frac{{-x^2}}{2}}dx}\)可通过标准正态分布的分布函数化为:\(\sqrt{2\pi}\left[\varPhi(1)-\varPhi(0)\right]\),然后查表得到。
若随机变量\(X\sim N(\mu,\sigma^2)\) ,则\(P\left\{\left|\dfrac{X-\mu}{\sigma}\right|\lt 1\right\}\)随\(\sigma\)的增大而
A. 增大
B. 减小
C. 不变
D. 条件不足,无法判断
若随机变量\(X\sim N(\mu_1,\sigma_1^2)\) ,\(Y\sim N(\mu_2,\sigma_2^2)\),且\(P\{|X-\mu_1|\lt 1\}\lt P\{|Y-\mu_2|\lt 1\}\) ,则有:\(\sigma_1\lt \sigma_2\). (改编自2006研考题)