Evaluating the goodness of fit in least-squares regression
In this section, we’ll discuss how to evaluate the goodness of fit in least-squares regression, a critical step in determining the accuracy and effectiveness of our models.
By understanding how well our model fits the data, we can make more informed decisions and improve our predictions. We’ll investigate various examples and introduce key metrics for evaluating the goodness of fit in regression analysis.
The goodness of fit is a measure of how well the regression line represents the relationship between the dependent and independent variables. A model with a high goodness of fit accurately describes the underlying data, while a model with a low goodness of fit may not capture the true relationship between the variables. To evaluate the goodness of fit, we commonly use two key metrics: the coefficient of determination (R-squared) and the root mean square error (RMSE):
- Coefficient of determination...
