While standard least methodology (OLS) regression remains a cornerstone in data inference, its assumptions aren't always fulfilled. Consequently, investigating substitutes becomes vital, especially when confronting with non-linear relationships or violating key requirements such as average distribution, constant variance, or independence of errors.

While Ordinary Least Squares (OLS) remains a foundational technique/method/approach in regression analysis, its limitations sometimes/frequently/occasionally necessitate the exploration/consideration/utilization of alternative methods. These alternatives often/may/can provide improved/enhanced/superior accuracy/fit/performance for diverse/varied/un