{0i} + \beta_1 x_{1i} + \beta_2 x_{2i} + ... + \varepsilon_i$$`

.hi-purple[**Prediction:**] Want to estimate `\(\color{#6A5ACD}{\hat{Y_{i}}}\)` given observed data `\(x_{0i}, x_{1i}, x_{2i}, ....\)`.
- Does not matter whether your model is "correct" (the true DGP), only whether it *works.*

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The idea is that we will:
1. **Train** a model on data for which we know both `\(X\)` and `\(Y\)`.
2. **Apply** the model to new situations where we know `\(X\)` but not `\(Y\)`.


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# Statistical lea