Consider the process Y: RE,, where Y = "total number of up-dots on 4 dice" and RE, ="rol| 4 dice." Note that Y:RE, ~ X1 + X2 + X3 + X4, where the X{'s are IID with common PMF P(X, = j) = 10 = 1, ..., 6). Here, X; measures the number of up-dots on the ith rolled die, i = 1,2,3,4. (a) Compute mean(Y:RE,). Hint: E(Y) = E(X1 + X2 + X3 + X4). (b) Compute var(Y:RE,). Hint: var(Y) = var(X, + X2 + X3 + X4).

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**Exercise 2.**

Consider the process \( Y: RE_1 \), where \( Y = \) "total number of up-dots on 4 dice" and \( RE_1 = \) "roll 4 dice."

Note that \( Y: RE_1 \sim X_1 + X_2 + X_3 + X_4 \), where the \( X_i \)'s are IID with common PMF
\[
P(X_1 = j) = \frac{1}{6} \, (j = 1, \ldots, 6).
\]
Here, \( X_i \) measures the number of up-dots on the \( i^{th} \) rolled die, \( i = 1, 2, 3, 4 \).

(a) Compute \( \text{mean}(Y:RE_1) \). Hint: \( E(Y) = E(X_1 + X_2 + X_3 + X_4) \).

(b) Compute \( \text{var}(Y:RE_1) \). Hint: \( \text{var}(Y) = \text{var}(X_1 + X_2 + X_3 + X_4) \).

I generated data \( Y:S \), where sample \( S \) is an outcome of \( RE_1 (N \equiv 10^6) \).

(c) Explain why you would expect the sample distribution \( \text{dist}(Y: S) \) to be approximately equal to the process distribution \( \text{dist}(Y: RE_1) \). That is, what fundamental result leads to this expectation and why is it applicable?

(d) I computed the sample mean of my data, \( \text{mean}(Y: S) \). What value [approximately] do you think it was? Explain why your approximate value is likely very reasonable.

(e) I computed the sample variance of my data, \( \text{var}(Y: S) \). What value [approximately] do you think it was? Explain why your approximate value is likely very reasonable.

(f) I computed the sample proportion of my data values equal to 4, \( \text{prop}_S(Y = 4) \). What value [approximately] do you think it was? Explain why your approximate value is likely very reasonable.
Transcribed Image Text:**Exercise 2.** Consider the process \( Y: RE_1 \), where \( Y = \) "total number of up-dots on 4 dice" and \( RE_1 = \) "roll 4 dice." Note that \( Y: RE_1 \sim X_1 + X_2 + X_3 + X_4 \), where the \( X_i \)'s are IID with common PMF \[ P(X_1 = j) = \frac{1}{6} \, (j = 1, \ldots, 6). \] Here, \( X_i \) measures the number of up-dots on the \( i^{th} \) rolled die, \( i = 1, 2, 3, 4 \). (a) Compute \( \text{mean}(Y:RE_1) \). Hint: \( E(Y) = E(X_1 + X_2 + X_3 + X_4) \). (b) Compute \( \text{var}(Y:RE_1) \). Hint: \( \text{var}(Y) = \text{var}(X_1 + X_2 + X_3 + X_4) \). I generated data \( Y:S \), where sample \( S \) is an outcome of \( RE_1 (N \equiv 10^6) \). (c) Explain why you would expect the sample distribution \( \text{dist}(Y: S) \) to be approximately equal to the process distribution \( \text{dist}(Y: RE_1) \). That is, what fundamental result leads to this expectation and why is it applicable? (d) I computed the sample mean of my data, \( \text{mean}(Y: S) \). What value [approximately] do you think it was? Explain why your approximate value is likely very reasonable. (e) I computed the sample variance of my data, \( \text{var}(Y: S) \). What value [approximately] do you think it was? Explain why your approximate value is likely very reasonable. (f) I computed the sample proportion of my data values equal to 4, \( \text{prop}_S(Y = 4) \). What value [approximately] do you think it was? Explain why your approximate value is likely very reasonable.
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