statsmodels
E459721
statsmodels is a Python library for statistical modeling and econometrics, providing tools for estimating and interpreting a wide range of statistical models and tests.
All labels observed (1)
| Label | Occurrences |
|---|---|
| statsmodels canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4599950 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: statsmodels Context triple: [Python scientific stack, hasComponent, statsmodels]
-
A.
statistics
Statistics is a Python standard library module that provides functions for calculating mathematical statistics of numeric data, such as means, medians, and variance.
-
B.
PyMC3
PyMC3 is a Python library for probabilistic programming that enables Bayesian statistical modeling and inference using advanced Markov chain Monte Carlo and variational methods.
-
C.
STAT
STAT is a U.S.-based media company and news site focused on in-depth coverage of health, medicine, and life sciences.
-
D.
scikit-learn
scikit-learn is a widely used open-source Python library that provides efficient tools for data mining, data analysis, and implementing a broad range of machine learning algorithms.
-
E.
Frisch–Waugh–Lovell theorem
The Frisch–Waugh–Lovell theorem is a fundamental result in econometrics that shows how the coefficients of a multiple linear regression can be obtained by first partialling out (regressing out) other explanatory variables.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: statsmodels Target entity description: statsmodels is a Python library for statistical modeling and econometrics, providing tools for estimating and interpreting a wide range of statistical models and tests.
-
A.
statistics
Statistics is a Python standard library module that provides functions for calculating mathematical statistics of numeric data, such as means, medians, and variance.
-
B.
PyMC3
PyMC3 is a Python library for probabilistic programming that enables Bayesian statistical modeling and inference using advanced Markov chain Monte Carlo and variational methods.
-
C.
STAT
STAT is a U.S.-based media company and news site focused on in-depth coverage of health, medicine, and life sciences.
-
D.
scikit-learn
scikit-learn is a widely used open-source Python library that provides efficient tools for data mining, data analysis, and implementing a broad range of machine learning algorithms.
-
E.
Frisch–Waugh–Lovell theorem
The Frisch–Waugh–Lovell theorem is a fundamental result in econometrics that shows how the coefficients of a multiple linear regression can be obtained by first partialling out (regressing out) other explanatory variables.
- F. None of above. chosen
Statements (50)
| Predicate | Object |
|---|---|
| instanceOf |
Python library
ⓘ
open-source software ⓘ software package ⓘ |
| compatibleWith | Python 3 NERFINISHED ⓘ |
| genre |
data analysis library
ⓘ
econometrics software ⓘ scientific computing library ⓘ statistical software ⓘ |
| hasComponent |
statsmodels.api
NERFINISHED
ⓘ
statsmodels.discrete NERFINISHED ⓘ statsmodels.formula.api NERFINISHED ⓘ statsmodels.multivariate NERFINISHED ⓘ statsmodels.nonparametric NERFINISHED ⓘ statsmodels.regression NERFINISHED ⓘ statsmodels.sandbox NERFINISHED ⓘ statsmodels.tsa ⓘ |
| license | BSD license NERFINISHED ⓘ |
| programmingLanguage | Python ⓘ |
| provides |
R-style formula interface
ⓘ
diagnostic plots ⓘ extensive model summary output ⓘ statistical result objects ⓘ |
| repository | https://github.com/statsmodels/statsmodels ⓘ |
| supports |
ANOVA
ⓘ
ARIMA models ⓘ Bayesian regression (via wrappers or extensions) ⓘ confidence intervals ⓘ discrete choice models ⓘ generalized linear models ⓘ generalized method of moments ⓘ hypothesis testing ⓘ linear regression ⓘ logit models ⓘ maximum likelihood estimation ⓘ mixed linear models ⓘ nonparametric methods ⓘ panel data models ⓘ prediction intervals ⓘ probit models ⓘ regression diagnostics ⓘ robust linear models ⓘ state space models ⓘ statistical tests ⓘ survival analysis ⓘ time series analysis ⓘ |
| uses |
NumPy
NERFINISHED
ⓘ
SciPy NERFINISHED ⓘ pandas ⓘ |
| website | https://www.statsmodels.org/ ⓘ |
| writtenIn | Python NERFINISHED ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: statsmodels Description of subject: statsmodels is a Python library for statistical modeling and econometrics, providing tools for estimating and interpreting a wide range of statistical models and tests.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.