# 4.9 Summary of notation and terminology

- $x_i$ is observation $i$ on variable $x$.
- $y_i=\beta_0+\beta_1x_i+\varepsilon_i$ is the
**simple linear model**with intercept $\beta_0$ and slope $\beta_1$. The error is denoted by $\varepsilon_i$. - $y_i=\hat{\beta}_0+\hat{\beta}_1 x_i+e_i$ is the
**estimated regression model**with intercept $\hat{\beta}_0$ and slope $\hat{\beta}_1$. The estimated error or**residual**is denoted by $e_i$. - $\hat{y}_i=\hat{\beta}_0+\hat{\beta}_1 x_i$ is the fitted
or estimated
**regression line**; $\hat{y}_i$ is the**fitted value**corresponding to observation $y_i$.

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