Isotonic regression is a statistical technique used to fit a non-decreasing function to a set of data points. It aims to find a piecewise constant function that best represents the data while maintaining the order of the observations. This method is particularly useful when the underlying relationship is expected to be monotonic, meaning it either never decreases or never increases. The process involves adjusting the values of the data points to ensure they follow a non-decreasing pattern, often using algorithms like the Pool-Adjacent-Violators Algorithm (PAVA). Isotonic regression is commonly applied in fields such as economics, biostatistics, and machine learning for tasks like calibration and trend analysis.