Compute Var(X) for the same distribution.

Enhance your understanding of Descriptive Statistics and Probability. Study with interactive questions and detailed explanations. Prepare effectively for your test!

Multiple Choice

Compute Var(X) for the same distribution.

Explanation:
Variance tells you how spread out the values of X are around its average. It can be computed with Var(X) = E[X^2] − (E[X])^2: you first find the mean μ = E[X], then compute E[X^2], and subtract the square of the mean. For this distribution, the expectations are such that E[X^2] − (E[X])^2 comes out to 0.49. That means the typical squared deviation from the mean is 0.49, and the standard deviation is sqrt(0.49) = 0.7, giving a sense of how far values of X typically fall from the mean. So the variance matches the value 0.49, reflecting the given spread of the distribution. The other numeric options would imply different levels of spread (different E[X] and E[X^2]), so they don’t align with this distribution’s calculated variance.

Variance tells you how spread out the values of X are around its average. It can be computed with Var(X) = E[X^2] − (E[X])^2: you first find the mean μ = E[X], then compute E[X^2], and subtract the square of the mean.

For this distribution, the expectations are such that E[X^2] − (E[X])^2 comes out to 0.49. That means the typical squared deviation from the mean is 0.49, and the standard deviation is sqrt(0.49) = 0.7, giving a sense of how far values of X typically fall from the mean.

So the variance matches the value 0.49, reflecting the given spread of the distribution. The other numeric options would imply different levels of spread (different E[X] and E[X^2]), so they don’t align with this distribution’s calculated variance.

Subscribe

Get the latest from Examzify

You can unsubscribe at any time. Read our privacy policy