一元线性回归模型Y_t = beta_0 + beta_1 X_t + u_t的经典假设包括( )A E(u_t) = 0B (var)(u_t) = sigma^2C (cov)(u_t, u_z) = 0D (Cov)(x_t, u_t) = 0E u_t sim N(0, sigma^2)

一元线性回归模型$Y_t = \beta_0 + \beta_1 X_t + u_t$的经典假设包括( )

A $E(u_t) = 0$

B $\text{var}(u_t) = \sigma^2$

C $\text{cov}(u_t, u_z) = 0$

D $\text{Cov}(x_t, u_t) = 0$

E $u_t \sim N(0, \sigma^2)$