Stata 18 Official

Stata has always been the gold standard for survey data analysis. Stata 18 extends this with:

For users of large-scale surveys like NHANES, BRFSS, or the European Social Survey (ESS), Stata 18 reduces computation time from minutes to seconds.

Imagine running a complex probit regression in Stata, then immediately passing the predicted probabilities to a Python machine learning library (like scikit-learn) for cluster analysis, and then bringing the results back into Stata for a publication-ready table. This workflow, previously cumbersome, is now seamless. Stata 18

Example use case:

python:
import pandas as pd
data = pd.DataFrame('x': [1,2,3], 'y': [4,5,6])
print(data.describe())
end

For organizations that rely on both Stata’s econometric rigor and Python’s deep learning ecosystem, Stata 18 is a game-changer. Stata has always been the gold standard for

For biostatisticians, Stata 18 adds Bayesian parametric survival models (exponential, Weibull, Gompertz) via bayes: streg. This is a game-changer for clinical trial analysis, where prior information from historical trials can be incorporated as informative priors.

The Graph Editor now includes:

Why it matters: You should not need to export to Excel or Adobe Illustrator just to make a graph presentation-ready. Stata 18 closes that gap.

* Best linear regression with robust SE
reghdfe y x1 x2, absorb(fe1 fe2) vce(cluster id)