## 18.1 General

• Any statistical model is simply a mathematical equation (or several)
• We can plug in values (e.g. values for our explanatory variables) into this equation
• The equation contains parameters (e.g. $$\beta$$), which we don’t know but we can estimate them
• Normally, we try to estimate the parameters in our model ($$\beta$$s) so that the result, i.e. the values predicted by our equation ($$\hat{y}_{i}$$) are as close as possible to the observed values ($$y_{i}$$) of our outcome variable
• There are various approaches for estimating parameters
• OLS
• Maximum Likelihood
• Bayesian estimation (see Rstan)