Reference

Statistical Machine Learning

Linear Regression

• ground truth: $Y \thickapprox \beta_0+\beta_1 X$

  $\beta_0,\beta_1$ are intercept slope known as the model coefficients or parameters

• prediction: $\hat y \thickapprox \hat \beta_0+\hat \beta_1 x$

  where $\hat y$ indicates a prediction of Y on the basis of X = x

1. calculate R^2 and see correlateness using residual
2. p-value
3. predict y given x

Multiple Regression

1. calculate R^2 and see correlateness