(Some of this content from Nathan Bastian)

## Remember Linear Regression

Linear regression is a simple approach to supervised learning. It assumes as it assumes $Y$ is dependent on $X_1, X_2, \ldots X_p$ and that this dependency is linear.

Most modern machine learning is basically fancier versions of linear regression where the goal is: predict $Y$ from $X$ by $f(X)$, where $f$ is a fancy neural network or something.

#### Linear Regression model

- Input vector: $X^T = (X_1, X_2, \ldots X_p)$
- Output $Y$ is real-valued (quantitative response) and ordered

- We want to predict $Y$ from $X$, but before we actually do the prediction, we have to
*train*the function $f(X)$. - At the end of training, we have a function $f(X)$ to map every $X$ into an estimated $Y$ (we call $\hat{Y}$).

What does $f(X)$ look like as a linear regression model?

$$f(X)=\beta_0+\sum_{j=1}^{p} X_j\beta_j$$

$\beta_0$ is the intercept and $\beta_j$ is the slope for the $j$th variable $X_j$, which is the **average** increase in $Y$ when $X_j$ is increased by one unit and all other $X$’s are held constant.

The job of OLS is to calculate this hyperplane

## Hypothesis Testing

$H_0$: all slopes equal 0 ($\beta_1=\beta_2=\ldots=\beta_p=0$).

$H_a$: at least one slope $\ne$ 0.

We use the ANOVA table to get the *F-statistic* and its corresponding p-value. If p-value < 0.05, reject $H_0$. Otherwise, all of the slopes equal 0 and none of the predictors are useful in predicting the response.