Regression analysis offers several advantages, including the ability to identify relationships between variables, make predictions, and quantify the strength of associations. However, it also has disadvantages, such as the assumption of linearity, which may not always hold true, and sensitivity to outliers, which can skew results. Additionally, regression models can become overly complex if too many variables are included, potentially leading to overfitting. Lastly, correlation does not imply causation, meaning that regression results must be interpreted cautiously.
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