Multiple regression extends traditional linear regression by allowing the analysis of multiple independent variables simultaneously, rather than just one. While traditional linear regression examines the relationship between a single predictor and an outcome, multiple regression evaluates how several predictors collectively influence the dependent variable. The main assumptions for multiple regression include linearity (the relationship between predictors and the outcome should be linear), independence (errors should be independent of each other), normality (errors should be normally distributed), and homoscedasticity (constant variance of errors). Additionally, addressing multicollinearity is crucial when dealing with high-dimensional soil data to prevent predictors from being excessively correlated. Multiple regression is particularly useful in soil science research for understanding complex interactions between various soil properties and their impact on outcomes like soil fertility and plant growth. Before applying multiple regression, researchers should ensure data quality by checking for missing values, outliers, and ensuring the data meets the necessary assumptions to obtain reliable and meaningful results.