6. Consider the hypothesis test for a normal distribution with mean μ at significance level α: H0: μ = μ0 versus H1: μ ≠ μ0. Which of the following is correct?
(A) \( \Pr \) (critical region \( |\mathrm{H}_0\rangle = 1 - \alpha \)
(B) \( \Pr \) (critical region \( |\mathrm{H}_1\rangle = \Pr \) (type II error)
(C) \( \Pr \) (reject \( \mathrm{H}0\mid \mathrm{H}0 \) ) \( = \) p-value
(D) \( \Pr \) (reject \( \mathrm{H}0\mid \mathrm{H}1 \) ) \( = \) power

7. Suppose \( x_1, x_2, \dots, x_n \) is a random sample of size n from the following double exponential distribution \( f(x | \beta) = \frac{1}{2\beta} e^{-\frac{|x|}{\beta}} \). Assume that \( x_1 = -3, x_2 = 2, x_3 = 4, x_4 = -1 \), and \( x_5 = 1 \). First derive the maximum likelihood estimator of \( \beta \), and then evaluate it at the given sample values. What is the resulting estimate? (A) 0.6 (B) 1.67 (C) 2.2 (D) 0.45

8. Suppose $x1, x2, \ldots, xn$ is a random sample of size $n$ from a Poisson distribution with mean $\lambda$, and assume $n = 2k$ for some integer $k$. Consider the following estimator. $\hat{\lambda} = \frac{1}{2k} \sum{i=1}^{k} (x{2i} - x{2i-1})^2$ Which of the following is correct?(A) \( \hat{\lambda} \) is a linear and an unbiased estimator.(B) \( \hat{\lambda} \) is a linear but biased estimator.(C) \( \hat{\lambda} \) is not a linear but an unbiased estimator.(D) \( \hat{\lambda} \) is neither a linear nor an unbiased estimator.

9. Suppose $x1, x2, \ldots, x_n$ is a random sample of size $n$ from a normal distribution with mean $\mu$ and variance $\sigma^2$. What is the Cramér-Rao lower bound for $\sigma^2$?(A) $\frac{\sigma^2}{n}$(B) $\frac{\sigma^2}{n-1}$(C) $\frac{2\sigma^4}{n}$(D) $\frac{2\sigma^4}{n-1}$

10. Let \( \{X_n\} \) be a sequence of random variables indexed by the sample size n. Suppose that \( X_n \) takes two values, 1 and n, with probabilities \( 1 - \frac{1}{n} \) and \( \frac{1}{n} \), respectively. Which of the following is correct? (A) \( X_n \) converges in probability. (B) \( X_n \) converges in mean square. (C) \( X_n \) converges in both probability and mean square. (D) \( X_n \) converges in neither probability nor mean square.

1. Consider the linear model $yi = \beta0 + \beta1 x{1i} + \beta2 x{2i} + ui$, where $E(ui \mid x{1i}, x{2i}) = 0$ and $Var(ui \mid x{1i}, x{2i}) = \sigma^2$ (homoskedasticity). Let $R1^2$ be the $R^2$ from the auxiliary regression of $x{1i}$ on $(1, x{2i})$. Suppose $\hat{\sigma}^2 = 4.00$, $\sum{i=1}^{n} (x{1i} - \bar{x}1)^2 = 200$, and $R1^2 = 0.75$. Using the standard homoskedastic OLS variance formula, what is the implied standard error of $\hat{\beta}_1$?(A) 0.071(B) 0.100(C) 0.283(D) 0.400

2. Consider $yi = \beta0 + \beta1 Di + \beta2 xi + \beta3 (Di xi) + ui$, where $Di \in \{0, 1\}$ and $xi \in \mathbb{R}$, with $E(ui \mid Di, xi) = 0$. Define the conditional mean $\mu(D, x) = E(yi \mid Di = D, xi = x)$. Which statement is correct?(A) \( \beta_{1} = \mu (1,x) - \mu (0,x) \) for all \( x \)(B) The marginal effect of \( x \) when \( D = 1 \) is \( \beta_{2} \)(C) \( \beta{3} = \frac{\partial\mu(1,x)}{\partial x}\Big|{x = 0} - \frac{\partial\mu(0,x)}{\partial x}\Big|_{x = 0} \)(D) \( \beta{2} + \beta{3} \) equals the intercept difference between \( D = 1 \) and \( D = 0 \)

3. The true model is $yi = \beta0 + \beta1 xi + \beta2 zi + ui$, $E(ui \mid xi, zi) = 0$, but you estimate the short regression $yi = \alpha0 + \alpha1 xi + ei$ (omitting $zi$). Assume the standard OVB formula:$$plim(\hat{\alpha}1) = \beta1 + \beta2 \frac{Cov(xi, zi)}{Var(xi)}.$$Suppose $\beta1 = 1.0$, $\beta2 = -2.0$, $Var(xi) = 4.0$, and $Cov(xi, zi) = 1.0$. Which is closest to $plim(\hat{\alpha}1)$?(A) 0.0(B) 0.5(C) 1.0(D)-0.5

4. Consider the simple regression $yi = \beta0 + \beta1 xi + u_i$. Which condition, if violated alone, implies OLS is generally not BLUE but can remain unbiased?(A) \( E(u{i}\mid x{i}) = 0 \)(B) \( \operatorname{Var}(ui \mid xi) = \sigma^2 \) (constant variance)(C) \( \operatorname{Var}(x_i) > 0 \)(D) Linear-in-parameters conditional mean: \( E(y{i} \mid x{i}) = \beta{0} + \beta{1}x_{i} \)

5. In the multiple regression $yi = \beta0 + \beta1 x{1i} + \beta2 x{2i} + ui$, you want to test $H0: \beta1 - \beta2 = 0$ (a single linear restriction).Suppose you have $\hat{\beta}1 = 0.80$, $\hat{\beta}2 = 0.50$, $\widehat{Var}(\hat{\beta}1) = 0.040$, $\widehat{Var}(\hat{\beta}2) = 0.010$, $\widehat{Cov}(\hat{\beta}1, \hat{\beta}2) = 0.005$.Compute the $t$-statistic $t = \frac{(\hat{\beta}1 - \hat{\beta}2) - 0}{\sqrt{\widehat{Var}(\hat{\beta}1 - \hat{\beta}2)}}$, $\widehat{Var}(\hat{\beta}1 - \hat{\beta}2) = \widehat{Var}(\hat{\beta}1) + \widehat{Var}(\hat{\beta}2) - 2\widehat{Cov}(\hat{\beta}1, \hat{\beta}2)$.Which is closest?(A) 1.50(B) 2.12(C) 3.00(D) 4.24

6. Consider the structural model $yi = \beta0 + \beta1 xi + ui$, where $Cov(xi, ui) \neq 0$. You have one instrument $zi$ satisfying $Cov(zi, xi) \neq 0$ (relevance), $Cov(zi, ui) = 0$ (exogeneity).Assume all variables are mean-zero for simplicity. The just-identified IV estimand is$$\beta1^{IV} = \frac{Cov(zi, yi)}{Cov(zi, x_i)}$$Suppose $Cov(zi, yi) = -0.18$ and $Cov(zi, xi) = 0.12$. What is $\beta_1^{IV}$?(A)-0.67(B)-0.5(C) 0.5(D)-1.50

115年 - 115 國立臺北大學_碩士班一般入學考試試題_國際企業研究所：統計學#147120

115年 - 115 國立臺北大學_碩士班一般入學考試試題_統計學系：統計學#147104

115年 - 115 國立臺北大學_碩士班一般入學考試試題_自然資源與環境管理研究所：統計學#146969

115年 - 110 國立臺北大學_碩士班一般入學考試試題_企業管理學系甲組：統計學#146951

115年 - 115 國立臺北大學_碩士班一般入學考試試題_金融與合作經營學系：統計學#146920

115年 - [無官方正解]115 國立臺北大學_碩士班一般入學考試試題_經濟學系：統計學#146534

115年 - [無官方正解]115 國立嘉義大學_碩士班招生考試試題_財務金融學系：統計學#143862

115年 - 115 中央警察大學_碩士班入學考試試題：統計學#139759

114年 - 114 國立臺北大學_碩士班一般入學考試試題_統計學系：統計學#147125

114年 - 114 國立臺北大學_碩士班一般入學考試試題_企業管理學系甲組：統計學#146907