What are the assumptions of Ordinary Least Squares (OLS) regression?
An example of an AI chatbot conversation about the assumptions of OLS regression.
What are the key assumptions of Ordinary Least Squares regression?
The key assumptions of OLS regression include linearity, independence, homoscedasticity, normality, and no multicollinearity.
Can you explain what homoscedasticity means?
Homoscedasticity refers to the assumption that the variance of the errors is constant across all levels of the independent variable.
What happens if the assumptions are violated?
If the assumptions are violated, it can lead to biased estimates, inefficient predictions, and invalid statistical tests.
How can I check if these assumptions hold in my data?
You can use diagnostic plots, such as residual plots, and statistical tests like the Breusch-Pagan test to check these assumptions.
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