Lack-of-fit sum of squares

Lack-of-fit sum of squares is a way to measure how well a set of data (like numbers or observations) fits with a model. It's like a test to see if the model fits the data correctly or if something is missing. It works by comparing the model and the data to each other and adding up all the differences, or how much they don't match. The bigger the difference, the worse the model fits, and the higher the number or sum of squares will be.