The key difference between linear and logistic regression lies in their output and the function they use to achieve this. Linear regression outputs continuous values, which makes it suitable for ...

Linear Regression assumes a linear relationship between predictors and the outcome. It also requires that errors are normally distributed and independent, with constant variance (homoscedasticity).

This resource revises linear regression and linear relations and within linear regression and linear relations there are 2 key concepts: Response and Explanatory Variables and Association; Correlation ...

Linear regression captures the relationship between two variables—for example, the relationship between the daily change in a company's stock prices and the daily change in trading volume.