What are the key assumptions in regression analysis?
An example of an AI chatbot conversation about the importance of assumptions in regression analysis.
What are the key assumptions in regression analysis?
The key assumptions in regression analysis include linearity, independence, homoscedasticity, normality, and no multicollinearity.
Why is it important to check these assumptions?
Checking these assumptions ensures the validity of the regression model, leading to reliable predictions and insights.
What happens if these assumptions are violated?
If assumptions are violated, it can result in biased estimates, misleading conclusions, and poor model performance.
How can I test these assumptions?
You can use diagnostic plots, statistical tests, and residual analysis to check if the assumptions hold true for your data.
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