Statsmodels

The statsmodels library enables AI agents to perform comprehensive statistical modeling and econometric analysis directly within Python workflows. It provides tools for hypothesis testing, time series analysis, regression diagnostics, and generalized linear models to derive insights from data.

When to use it

• Validating assumptions and checking residuals in linear regression models to ensure reliability.
• Analyzing trends and seasonality in time-series datasets like sales or stock prices.
• Conducting hypothesis tests (e.g., t-tests, ANOVA) to determine statistical significance between groups.
• Fitting generalized linear models (GLMs) for non-normal data distributions such as counts or binary outcomes.

Key capabilities

• Linear and logistic regression analysis with built-in diagnostic tools.
• Time series forecasting using ARIMA, SARIMA, and exponential smoothing methods.
• Hypothesis testing suite including t-tests, chi-square, and F-tests.
• Generalized linear models (GLM) for diverse data types.
• Non-parametric tests like the Wilcoxon rank-sum test.

Example prompts

• "Run a multiple linear regression on this dataset and output the p-values and R-squared score."
• "Fit an ARIMA model to this time series and forecast the next 12 months of values."
• "Perform a Shapiro-Wilk normality test on these residuals and interpret the result."

Tips & gotchas

Ensure your dataset is clean and properly formatted before running statistical tests, as outliers can skew results significantly. While statsmodels is powerful for inference, it lacks the automated feature engineering capabilities of machine learning libraries like scikit-learn, so manual data preprocessing may be required.