Description: Non-linear regression analysis is a statistical technique used to model the relationship between a dependent variable and one or more independent variables when the relationship is not linear. Unlike linear regression, which assumes that this relationship can be represented by a straight line, non-linear regression allows for more complex relationships between variables, using polynomial, exponential, logarithmic, or sigmoid functions, among others. This flexibility is crucial in many fields, as many real-world relationships do not follow a linear pattern. Non-linear regression analysis is based on minimizing the sum of squared errors, adjusting the model parameters to find the best representation of the data. It is particularly useful in situations where data exhibit curvature or patterns that cannot be adequately captured by a linear model. Additionally, it allows for the inclusion of interactions between variables and the modeling of non-constant effects, enriching the analysis and interpretation of results. In summary, non-linear regression analysis is a powerful tool in applied statistics that provides a deeper understanding of complex relationships between variables.
