general conclusions :25. Given X ~ N(µx, ox), prove that X~ N(Hx, ox/n). Hint: var (X) = + X "). Expand this, recalling some of the properties of the var

MATLAB: An Introduction with Applications
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ISBN:9781119256830
Author:Amos Gilat
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Question C.25
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C.25. Given \( X \sim N(\mu_X, \sigma^2_X) \), prove that \( \overline{X} \sim N(\mu_X, \sigma^2_X/n) \). Hint: \(\text{var} (\overline{X}) = \text{var} \left(\frac{X_1 + X_2 + \cdots + X_n}{n}\right)\). Expand this, recalling some of the properties of the variance discussed in Appendix B and the fact that the \( X_i \) are i.i.d.

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Note: The text discusses proving that the sample mean \(\overline{X}\) of a normally distributed random variable \(X\) is also normally distributed with an adjusted variance, specifically \(\sigma^2_X/n\), where the \(X_i\) are independent and identically distributed (i.i.d.).
Transcribed Image Text:Certainly! Here is the transcribed text from the image: --- C.25. Given \( X \sim N(\mu_X, \sigma^2_X) \), prove that \( \overline{X} \sim N(\mu_X, \sigma^2_X/n) \). Hint: \(\text{var} (\overline{X}) = \text{var} \left(\frac{X_1 + X_2 + \cdots + X_n}{n}\right)\). Expand this, recalling some of the properties of the variance discussed in Appendix B and the fact that the \( X_i \) are i.i.d. --- Note: The text discusses proving that the sample mean \(\overline{X}\) of a normally distributed random variable \(X\) is also normally distributed with an adjusted variance, specifically \(\sigma^2_X/n\), where the \(X_i\) are independent and identically distributed (i.i.d.).
Expert Solution
Step 1

Given that X~Nμx,σx2

Consider,

E(X¯)=EX1+X2+.....+Xnn       =1nE(X1)+E(X2)+.....+E(Xn)      =1nμx+μx+....+μx     =μx

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