Back to Topic 4.14 — E(X), Var(X) & estimators (HL only)
4.14.2Math AI HL8 flashcards

Combining variables & estimators

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Card 1 of 84.14.2
4.14.2
Question

For independent X and Y, what is E(X + Y) and E(X − Y)?

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All 8 Flashcards — Combining variables & estimators

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Card 1formula

Question

For independent X and Y, what is E(X + Y) and E(X − Y)?

Answer

E(X + Y) = E(X) + E(Y); E(X − Y) = E(X) − E(Y). Means take the sign.

Card 2formula

Question

For independent X and Y, what is Var(X ± Y)?

Answer

Var(X + Y) = Var(X − Y) = Var(X) + Var(Y). Variances ALWAYS add, even for a difference.

Card 3concept

Question

Can you add standard deviations to combine spreads?

Answer

No — add the VARIANCES (square the SDs), then square-root: SD(X±Y) = √(SD(X)² + SD(Y)²).

Card 4formula

Question

Sum of n independent copies of X (mean μ, variance σ²): mean and variance?

Answer

Mean = nμ; Variance = nσ² (so SD = σ√n).

Card 5concept

Question

Difference between Var(nX) and Var(X₁+…+Xₙ)?

Answer

Var(nX) = n²σ² (one copy scaled up); Var(sum of n independent copies) = nσ² (separate items partly cancel).

Card 6concept

Question

What is the unbiased estimate of the population mean?

Answer

The sample mean x̄ — it's unbiased as is.

Card 7formula

Question

What is the unbiased estimate of the population variance?

Answer

sₙ₋₁² = Σ(x − x̄)²/(n − 1) — divide by n − 1, the GDC's Sx² (not σx² which uses ÷n).

Card 8formula

Question

Relationship between sₙ₋₁² and the biased sₙ²?

Answer

sₙ₋₁² = [n/(n − 1)]·sₙ² — scale the biased variance up by n/(n − 1).

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