How Do You Prove the Variance Formula for a Linear Combination of Variables?

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Homework Help Overview

The discussion revolves around proving the variance formula for a linear combination of random variables, specifically Var(a₁X₁ + a₂X₂) in relation to a covariance matrix Σ. The original poster presents an expression involving variances and covariances and seeks clarification on the equivalence to a quadratic form involving the covariance matrix.

Discussion Character

  • Exploratory, Conceptual clarification, Mathematical reasoning

Approaches and Questions Raised

  • The original poster attempts to derive the variance of a linear combination of variables and expresses uncertainty about the completeness of their solution. Some participants question whether the derived expression matches the quadratic form aᵀΣa. Others raise the issue of whether the covariance matrix Σ can be assumed to be positive.

Discussion Status

The discussion is ongoing, with participants exploring different aspects of the problem. Some guidance has been offered regarding the relationship between variance and the covariance matrix, but there is no explicit consensus on the assumptions regarding Σ.

Contextual Notes

Participants are considering the implications of the covariance matrix being positive and its effect on the variance expression. There is also mention of additional parts of the problem that require further exploration.

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Homework Statement



Let [tex]\Sigma =[/tex]
( var(X1) cov(X1, X2) )
( cov (X2. X1) var(X2) )

Show that [tex]Var (a_1 X_1 + a_2 X_2) = a^T \Sigma a[/tex]

where [tex]a^T = [a_1 a_2][/tex] is the transpose of the of the column vector a

Homework Equations





The Attempt at a Solution



I got this far:

[tex]Var (a_1 X_1 + a_2 X_2) = a_1^2 Var(X_1) + a_2^2 Var(X_2) + 2a_1 a_2 Cov (X_1, X_2) = a_1^2 Var(X_1) + a_2^2 Var(X_2) + a_1 a_2 Cov (X_1, X_2) + a_1 a_2 Cov (X_2, X_1)[/tex]

Thats all I got so far, any hints
 
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Aren't you done? Isn't that what [tex]a^T \Sigma a[/tex] is?
 
Thought there was more to it than that.

There's another part of the question that says: Using [tex]Var (a_1 X_1 + a_2 X_2)[/tex] show that for every choice of a1 and a2 that [tex]a^T \Sigma a \geq 0[/tex]

Can I assume that [tex]\Sigma[/tex] is always positive?
 
[tex]Var (a_1 X_1 + a_2 X_2)\ge0[/tex] always, since it's variance! And you just showed it equals [tex]a^T \Sigma a[/tex]
 

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