What are the assumptions of linear regression in NumPy?

What are the assumptions of linear regression in NumPy?

The main assumptions of linear regression include linearity, independence, homoscedasticity, normality, and no multicollinearity.

Can you explain each of these assumptions?

Sure!

1. Linearity: The relationship between the independent and dependent variables should be linear.
2. Independence: Observations should be independent of each other.
3. Homoscedasticity: The residuals should have constant variance at all levels of the independent variable.
4. Normality: The residuals should be normally distributed.
5. No Multicollinearity: Independent variables should not be too highly correlated with each other.

How can I check these assumptions in my dataset?

You can use various diagnostic plots and statistical tests. For example, scatter plots for linearity, residual plots for homoscedasticity, and the Shapiro-Wilk test for normality.

What if my data doesn't meet these assumptions?

If your data doesn't meet the assumptions, you might consider transforming your variables, using robust regression techniques, or applying different modeling approaches.