# Conditional independence of the response variable, regression analysis

The book says: for the standard linear regression model, we assume:

$$\begin{equation} y_i= \beta_0 + \beta_1 x_i + \epsilon_i \end{equation}$$

where $$E[\epsilon_i]=0$$ and $$Var[\epsilon_i]=\sigma^2$$. Homoskedasticity implies that the errors are independent of the covariates. For constructing confidence interval and hypothesis testing, we assume $$\epsilon_i$$~$$N(0,\sigma^2)$$. In this case, the observations of the response variable follow a (conditional) normal distribution with $$E[y_i]= \beta_0 +\beta_1 x_i$$ ; $$Var[y_i]=\sigma^2$$ and the $$y_i$$ are (conditionally) independent given covariate values $$x_i$$.

Why $$y_i$$ are conditionally independent from the covariates $$x_i$$? Linear regression should be about estimating the expected value of $$y_i$$ conditioned to a sequence of covariates. How $$y_i$$ is conditionally independent from $$x_i$$?

• First of all, thank you very much for your comment. What do you mean by constant regressors? The book keeps saying that also when it talks about generalized linear models (GLMs): For given covariates $xi= (1,x_{i1},...,x_{ik} )'$, the response variables are (conditionally) independent and the (conditional) density of $y_i$ belongs to an exponential family with: \begin{equation} f(y_i,\theta_i) = exp \left ( \frac{y_i \theta_i - b(\theta_i)}{\phi}w_i + c(y_i, \phi,w_i) \right) \end{equation} Dec 14, 2022 at 15:20
• thank you very much. Does this fact give rise to any further implication? Dec 14, 2022 at 18:54
• I deleted and gathered my comments in an expanded answer below. Dec 15, 2022 at 8:19

In the setup of this question, that does not change too much. Essentially, you would replace all expectations in your question with expectations conditional on $$x_i$$.