What are some common misconceptions about regression analysis?

What are some common misconceptions about regression analysis?

One common misconception is that regression implies causation. Just because two variables are correlated doesn't mean one causes the other.

Can you explain why correlation doesn't imply causation?

Sure! Correlation indicates a relationship between two variables, but it doesn't account for other factors that might influence them. For example, ice cream sales and drowning incidents are correlated, but that doesn't mean one causes the other.

What about the assumption of linearity in regression?

Another misconception is that regression only works for linear relationships. In reality, regression can be applied to non-linear relationships as well, using techniques like polynomial regression.

Are there any other assumptions I should be aware of?

Yes, there are several assumptions, including homoscedasticity, independence of errors, and normality of residuals. Violating these can affect your model's validity.