What are the key assumptions of regression analysis?
An example of an AI chatbot conversation about the assumptions of regression analysis.
What are the key assumptions of regression analysis?
The key assumptions of regression analysis include linearity, independence, homoscedasticity, normality, and no multicollinearity.
Can you explain linearity in more detail?
Linearity means that the relationship between the independent and dependent variables should be linear. This can be checked using scatter plots.
What does homoscedasticity mean?
Homoscedasticity refers to the assumption that the variance of the errors is constant across all levels of the independent variable.
How can I check for multicollinearity?
You can check for multicollinearity using Variance Inflation Factor (VIF) or by examining correlation matrices.
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