Extrapolation is the process of estimating or predicting values beyond a given set of known data points. It often involves fitting a model to a set of data points and then using that model to estimate values outside the observed range. For instance, observe how the amount of laundry detergent left changes over the course of the month. Notice that the data points follow a line. By fitting a linear model, the day when a bottle of laundry detergent will be empty can be estimated. This is the point at which the line intersects the $x\text{-}$axis, indicating a value of zero. According to the prediction, it will take around $28$ days for the laundry detergent bottle to be empty. Note that in extrapolation, unlike interpolation, the new data point lies outside the range of known observations, which increases the risk of error. and the likelihood of generating meaningless results.