What are the key statistical assumptions in R?
An example of an AI chatbot conversation about statistical assumptions in R programming.
What are the key statistical assumptions I need to check when using R for regression analysis?
The key assumptions include linearity, independence, homoscedasticity, normality, and no multicollinearity.
Can you explain what homoscedasticity means?
Homoscedasticity refers to the assumption that the variance of the residuals is constant across all levels of the independent variable.
How can I test for normality in my residuals?
You can use the Shapiro-Wilk test or visualize the residuals with a Q-Q plot to assess normality.
What should I do if my data violates these assumptions?
If assumptions are violated, consider transforming your data, using robust statistical methods, or applying different modeling techniques.
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