Interesting conversation regarding multiple linear regression which engages an important aspect known as the normality assumption, which states that the "residuals" (the differences between predicted and actual values of the dependent variable) should follow a normal distribution. This assumption is crucial because it enables us to accurately estimate standard errors, confidence intervals, and p-values. Adhering to this assumption ensures that we draw valid conclusions about the relationships between variables when interpreting the results of regression analysis. When the assumptions are not met, this results from various factors, such as the presence of outliers, skewed data, or inadequate model representation of the relationships between variables, which may lead to unreliable analysis outcomes. When residuals are not normally distributed, potential causes include outliers, data skewness, or a small sample size. Outliers represent extreme data points that deviate significantly from the overall trend, potentially resulting in non-normality. Additionally, with a small sample size, even datasets that are normally distributed may yield residuals that do not appear perfectly normal due to sampling variability