What are the main limitations of regression analysis?
Regression analysis has several limitations, including assumptions of linearity, multicollinearity, and sensitivity to outliers.
Can you explain the assumption of linearity?
Sure! The assumption of linearity means that the relationship between the independent and dependent variables should be linear. If this assumption is violated, the model may not accurately represent the data.
What happens if there are outliers in the data?
Outliers can significantly skew the results of regression analysis, leading to misleading coefficients and predictions. It's important to identify and address them.
Are there alternatives to regression analysis?
Yes, alternatives include decision trees, neural networks, and support vector machines, which can handle non-linear relationships better.
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