Nonparametric regression is a type of statistical analysis that does not assume a specific form for the relationship between variables. Instead of fitting a predefined equation, it uses the data itself to estimate the relationship, allowing for greater flexibility. This approach is particularly useful when the underlying relationship is complex or unknown. Common methods of nonparametric regression include kernel smoothing and local regression. These techniques can adapt to various data patterns without imposing strict assumptions, making them valuable in fields like economics and biostatistics where relationships may vary widely.