While in linear regression, the correlation between the independent and the dependent variables is best represented with a straight line, the real-life datasets are more complex and do not represent a linear relationship between cause and effect. The straight line equation does not fit the data points and hence cannot create an effective predictive model.
In such cases, we can consider using a higher-order quadratic equation for the predictor function. Given x as an independent variable and y as a dependent variable, the polynomial function takes the following forms:
These can be visualized with a small set of sample ...
