Would anyone explain the solution a little bit to me?

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The discussion focuses on understanding the calculation of E(X^3µσ) in probability, particularly the significance of certain steps in the solution. It addresses the formula for E[X-µ]^n and clarifies that variance, defined as σ² = E[(X-µ)²], is derived from the expected value of squared deviations from the mean. The conversation also emphasizes that for continuous distributions, E[X^n] can be calculated using integration, and the relationship E[aX+c] = aE[X]+c is essential for proving formulas. Additionally, the alternative formula for variance, Var(X) = E(X²) - µ², is highlighted, leading to the conclusion that E(X²) = σ² + µ². Understanding these concepts is crucial for mastering probability calculations in the upcoming math class.
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I am preparing for my math probability class next semester. There is question: Calculate E(x3µσ) Would anyone explain the solution in the picture a little bit to me?

1.Why is the step hold? Is there a formula or something that i can calculate E[X-µ]n?
2.Why is the fourth step hold? Where is the σ from and why variance σ2= E[X-µ]2?
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If the distribution is continuous, then we have ## E[X^n]=\int X^n \rho(X) dX ##.
Then it can be proved that ## E[aX+c]=aE[X]+c ## which can be used to prove the first formula.
And variance is defined to be the expected value of the squared deviation from the mean, which means ## \sigma^2=E[(X-\mu)^2] ##.
 
Shyan said:
If the distribution is continuous, then we have ## E[X^n]=\int X^n \rho(X) dX ##.
Then it can be proved that ## E[aX+c]=aE[X]+c ## which can be used to prove the first formula.
And variance is defined to be the expected value of the squared deviation from the mean, which means ## \sigma^2=E[(X-\mu)^2] ##.

Note that ##\sigma^2 \equiv \text{Var}(X)## is given by
\begin{array}{cll} \text{Var}(X)&amp; = E(X - \mu)^2 &amp;\text{definition}\\<br /> &amp; = E(X^2) - \mu^2 &amp; \text{alternative formula}<br /> \end{array}
Therefore, we have ##E(X^2) = \sigma^2 + \mu^2##.

To get the "alternative formula", just expand out ##(X - \mu)^2## and then take expectations.
 
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