7.6 Prediction: Mean
- Model = Mathematical equation(s)
- Underlying a model is always a (joint) distribution
- Model summarizes (joint) distribution with fewer parameters
- e.g. intercept/coefficents in linear model
- Simple model: Mean of the distribution of a variable
\[ \begin{aligned} trust2006_{Anna} = 3 = \underbrace{\color{blue}{\overline{y}}}_{\color{green}{\widehat{y}}_{Anna}} \pm \color{red}{\varepsilon}_{Anna} = \color{blue}{6.13} - \color{red}{3.13} \end{aligned} \]
- Mean (= model) predicts Anna’s value with a certain error
- Q: How well does the model (mean = 6.33) predict person’s that have values of 0, of 3 or of 4?
- Important: We could use this model – this mean – to predict trust values of another group of people
- First train model (= calculate mean) on this data (training dataset), then use it to predict outcome in other data (validation dataset)
- Sometime this is called out of sample prediction