Notes by Lex Toumbourou

A Linear Model represents the relationship between dependent variables and independent variables, expressed as:

\begin{equation} Y = \beta_0 + \beta_1 X_1 + \beta_2 X_2 + \ldots + \beta_n X_n + \epsilon \end{equation}

Where:

• $Y$ is the Dependent Variable.
• $\beta_0$ is the intercept.
• *$\beta_1, \beta_2, \ldots, \beta_n$ are the coefficients.
• $X_1, X_2, \ldots, X_n$ are the independent variables.
• $\epsilon$ is the error term.

Linear Regression, Lasso and Variable Subset Selection are examples of coefficient estimation techniques.

Aliases:

• Linear Models