Fit statistical models and run diagnostics in Python with statsmodels.
npx skills add https://github.com/galeep/plugin-place --skill statsmodels-galeep
Please help me install this Agent Skill. Skill: statsmodels Source: https://github.com/galeep/plugin-place/tree/main/plugins/sci-data-analysis-viz/skills/statsmodels Command: npx skills add https://github.com/galeep/plugin-place --skill statsmodels-galeep
This Skill provides comprehensive statistical modeling and econometrics capabilities in Python, enabling users to perform various analyses including regression, time series, GLMs, and more.
Use the statsmodels skill to fit a linear regression model to your data:
import statsmodels.api as sm
import numpy as np
import pandas as pd
# Prepare data
X = sm.add_constant(X_data)
y = y_data
# Fit model
model = sm.OLS(y, X).fit()
# View results
print(model.summary())
High-intent search queries and answers about installing and using this skill.
You perform linear regression using statsmodels with pandas and numpy to fit models like OLS, view comprehensive summaries, run diagnostics, and validate statistical modeling results.
Time series analysis and forecasting in Python is handled by statsmodels, which offers specialized models for time series alongside GLMs and regression for comprehensive econometric modeling.
Yes, you can use Generalized Linear Models for econometrics with pandas and numpy through statsmodels, which supports fitting GLMs, running model diagnostics, and validating complex datasets.
Statsmodels supports model diagnostics and validation for data analysis by providing built-in tools to evaluate regression, time series, and GLM fits, ensuring robust statistical modeling results.
Statsmodels focuses specifically on statistical modeling and econometrics rather than general machine learning, making it ideal for inference, diagnostics, and time series forecasting but less suited for predictive scalability.
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