Index: The Book of Statistical ProofsStatistical Models ▷ Univariate normal data ▷ Simple linear regression ▷ Special case of multiple linear regression

Theorem: Simple linear regression is a special case of multiple linear regression with design matrix $X$ and regression coefficients $\beta$

\[\label{eq:slr-mlr} X = \left[ \begin{matrix} 1_n & x \end{matrix} \right] \quad \text{and} \quad \beta = \left[ \begin{matrix} \beta_0 \\ \beta_1 \end{matrix} \right]\]

where $1_n$ is an $n \times 1$ vector of ones, $x$ is the $n \times 1$ single predictor variable, $\beta_0$ is the intercept and $\beta_1$ is the slope of the regression line.

Proof: Without loss of generality, consider the simple linear regression case with uncorrelated errors:

\[\label{eq:slr} y_i = \beta_0 + \beta_1 x_i + \varepsilon_i, \; \varepsilon_i \sim \mathcal{N}(0, \sigma^2), \; i = 1,\ldots,n \; .\]

In matrix notation and using the multivariate normal distribution, this can also be written as

\[\label{eq:slr-mlr-s1} \begin{split} y &= \beta_0 1_n + \beta_1 x + \varepsilon, \; \varepsilon \sim \mathcal{N}(0, I_n) \\ y &= \left[ \begin{matrix} 1_n & x \end{matrix} \right] \left[ \begin{matrix} \beta_0 \\ \beta_1 \end{matrix} \right] + \varepsilon, \; \varepsilon \sim \mathcal{N}(0, I_n) \; . \end{split}\]

Comparing with the multiple linear regression equations for uncorrelated errors, we finally note:

\[\label{eq:slr-mlr-s3} y = X\beta + \varepsilon \quad \text{with} \quad X = \left[ \begin{matrix} 1_n & x \end{matrix} \right] \quad \text{and} \quad \beta = \left[ \begin{matrix} \beta_0 \\ \beta_1 \end{matrix} \right] \; .\]

In the case of correlated observations, the error distribution changes to:

\[\label{eq:mlr-noise} \varepsilon \sim \mathcal{N}(0, \sigma^2 V) \; .\]

Metadata: ID: P281 | shortcut: slr-mlr | author: JoramSoch | date: 2021-11-09, 07:57.