---
title: "statsmodels | Skills We Assign For | Azendo"
description: "statsmodels for statistical inference — diagnostics over prediction, and the Azendo roles assigned for it."
url: "https://azendo.co/skills/statsmodels/"
---

[Skills](https://azendo.co/skills/) Analytics and BI 

# statsmodels.

statsmodels is a Python library for statistical modelling and inference: regression, time series, generalised linear models and hypothesis tests, with the diagnostic output needed to judge whether a model is valid.

## Where statsmodels fits on a long engagement.

statsmodels exists for inference rather than prediction, and that distinction decides when to use it. If the question is "what will happen", a machine learning library is usually better; if the question is "what is the effect of X, and how confident can we be", the coefficients, standard errors and diagnostics are the entire point.

Its regression output is deliberately verbose, and the parts people skip are the important ones. Residual diagnostics, multicollinearity indicators and specification tests tell you whether the model's assumptions hold; a coefficient read without them can be confidently wrong.

## What an assigned team does with statsmodels.

Interpreting statistical output correctly is a genuine skill, distinct from running the model. Confusing statistical significance with practical importance, or reading a coefficient as causal in observational data, are the standard errors and they are made by competent people.

Building that interpretive depth deliberately rather than assuming it is what the [Talent Success and Academy](https://azendo.co/talent-success-academy/) programme exists to do.

## What we use statsmodels for.

* Effects estimated with uncertainty Coefficients and intervals, where the question is about magnitude rather than prediction.
* Assumptions checked before conclusions Residual and specification diagnostics read rather than skipped.
* Results explained to non-specialists Findings stated in business terms without overclaiming what the data supports.

## How statsmodels capacity is assigned.

Statistical analysis capacity is assigned under [managed data services](https://azendo.co/services/data-engineering/), with interpretation treated as part of the work rather than left to the reader.

## Roles we assign statsmodels for

* [Data Scientist Data science](https://azendo.co/services/data-science/data-scientist/)

## Service lines it sits in

* [Data science](https://azendo.co/services/data-science/)

Capacity is agreed as a committed monthly capacity across a discipline, not per skill.

## Related in analytics and bi

* [Tableau — skill we assign for](https://azendo.co/skills/tableau/)
* [semantic layers — skill we assign for](https://azendo.co/skills/semantic-layers/)
* [A/B testing — skill we assign for](https://azendo.co/skills/a-b-testing/)
* [experimentation design — skill we assign for](https://azendo.co/skills/experimentation-design/)
* [causal inference — skill we assign for](https://azendo.co/skills/causal-inference/)
* [cohort analysis — skill we assign for](https://azendo.co/skills/cohort-analysis/)
* [segmentation — skill we assign for](https://azendo.co/skills/segmentation/)
* [metric definition — skill we assign for](https://azendo.co/skills/metric-definition/)

## Tell us what your roadmap needs statsmodels for.

A service delivery manager replies with the disciplines we would assign, the monthly capacity and what the first month looks like.

[All skills we assign for](https://azendo.co/skills/)