The idea of looking at a lot of data samples and trying to predict the dependent variable in a continuous numeric domain is called regression in statistical terms.
In order to perform regression on any dataset, it must satisfy the following assumptions:
- Normality: The erros are assumed to be normally distributed
- Independent: The errors must be independent of each other
- Mean and Variance: They must have zero mean and constant variance (this property of having a constant variance is also called homoscedasticity
These assumptions are usually verified using Q-Q Plots, S-W test etc.
This chapter offers introduction to various kind of regressions and their use cases.