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What is statsmodels?
Questions in this quiz
What is statsmodels?
What is the purpose of the `statsmodels.formula.api` interface (often imported as `smf`), and how does it differ from the array-based API?
What is the purpose of statsmodels' `OLS` (Ordinary Least Squares) class, and what does calling `.fit()` on it produce?
Why might a data scientist use BOTH statsmodels and scikit-learn in the same project, rather than picking just one?
What is the purpose of statsmodels' `add_constant()` utility function, commonly used before fitting an OLS model with the array-based (non-formula) API?
What is the purpose of statsmodels' statistical power analysis tools (e.g. `TTestPower`, used to determine required sample size for a planned experiment)?
What is the purpose of diagnostic tests provided by statsmodels, like tests for heteroscedasticity (e.g. Breusch-Pagan) or normality of residuals (e.g. Jarque-Bera)?
What does a low p-value (e.g. < 0.05) for a coefficient in a statsmodels regression summary typically indicate, in the standard hypothesis-testing framework?
What is "multicollinearity," and what tool does statsmodels provide to help detect it (like the Variance Inflation Factor, VIF)?
What does a 95% confidence interval on a regression coefficient, as produced by statsmodels' `.conf_int()`, actually represent?
What is the purpose of statsmodels' `robust` standard errors option (e.g. `.fit(cov_type='HC3')`) when heteroscedasticity is detected in a regression?
What does statsmodels' `results.predict()` method do after a model has been fit, and how does its typical use case differ from a purely ML-prediction-focused library?