Summary points

In lecture 2, we

• Defined a linear model

• Defined a simple linear regression

$$y_i= \alpha + \beta x_i + \epsilon_i \quad \quad \mathrm{for} \, i=1, \dots, n$$

• $\alpha + \beta x_i$ is the deterministic part of the model,

• $\epsilon_i$ is the random part and $\beta x_i$ is the part where the explanatory variable is incorporated.

• $\alpha$ and $\beta$ are model parameters to be estimated.