What are the assumptions of linear regression?
An example of an AI chatbot conversation about linear regression assumptions.
What are the assumptions of linear regression?
The main assumptions of linear regression are linearity, independence, homoscedasticity, normality, and no multicollinearity.
Can you explain each of these assumptions?
Sure! Linearity means the relationship between variables is linear. Independence means observations are independent of each other. Homoscedasticity indicates constant variance of errors. Normality means residuals should be normally distributed. No multicollinearity means predictors shouldn't be highly correlated.
Why is it important to check these assumptions?
Checking these assumptions is crucial because violating them can lead to biased estimates, incorrect conclusions, and unreliable predictions.
How can I test these assumptions?
You can use various statistical tests and visualizations, like scatter plots for linearity, residual plots for homoscedasticity, and Q-Q plots for normality.
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