## 7.11 Prediction: Linear model (Prediction)

$$y_{Leia} = \color{blue}{5.56} + \color{orange}{-0.66} \times x_{1Leia} + \color{orange}{0.12} \times x_{2Leia} + \color{red}{\varepsilon}_{Leia}$$

$$5 = \color{blue}{5.56} + \color{orange}{-0.66} \times 0 + \color{orange}{0.12} \times 1 + \color{red}{-0.69} = \color{green}{5.69} + \color{red}{-0.69}$$

Name $$trust2006$$ $$\boldsymbol{\color{blue}{\beta_{0}}}$$ $$\boldsymbol{\color{orange}{\beta_{1}}}$$ $$victim2006$$ $$\boldsymbol{\color{orange}{\beta_{2}}}$$ $$education2006$$ $$\boldsymbol{\color{red}{\varepsilon}}$$ $$\color{green}{\widehat{y}}$$
Daniel 5 5.56 -0.66 1 0.12 1 -0.02 5.02
Leia 5 5.56 -0.66 0 0.12 1 -0.69 5.69
Harrison 7 5.56 -0.66 0 0.12 5 0.81 6.19
.. .. .. .. .. .. .. .. ..
• Important note on “prediction”
• We can predict outcome values of units that are part of the sample on which the model is based
• Objective of machine learning (ML) is to predict outcome values of units that were NOT part of the sample on which the model is based (trained)