[ Tech stack ]
statsmodels
The Python library for econometrics and inferential statistics, tests and intervals included.
statsmodels covers linear and generalized linear models (OLS, GLM, logit, Poisson), time series (ARIMA, SARIMAX, VAR, state-space models, STL decomposition), hypothesis testing and survival analysis, with an R-style formula syntax. Every fit returns a full summary: coefficients, standard errors, p-values, confidence intervals and diagnostics. It's our choice when an effect has to be explained and defended, not just predicted: impact studies, elasticities, seasonality, reports written for decision-makers.
[ Why statsmodels at Dexon ]
What this technology does well, and why we use it.
Typical usage: Impact studies, price elasticities, seasonal forecasting, survival and churn analysis.
- 01
OLS, GLM, logit, mixed and robust regressions with R-style formulas.
- 02
Time series: ARIMA, SARIMAX, VAR, state-space models, STL decomposition.
- 03
Full inference: p-values, confidence intervals, hypothesis tests and residual diagnostics.
- 04
Report-ready summaries, tight integration with pandas, NumPy and SciPy.
[ Complementary technologies ]
The building blocks we often mobilise alongside.
A stack rarely exists alone. Here are the technologies Dexon most often pairs with this one, through pipeline habits, usage similarity or internal mastery. Click on a building block to see its scope.
[ Reassurance ]
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- custom projects delivered
- 30
- engineers, designers, project managers
- 80 %
- from top French schools
- 24 h
- average reply time
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