When to use it
Use to increase statistical power by controlling for baseline covariate variation.
Required Inputs
- Group factor
- Outcome Y
- Continuous Baseline Covariate X
- Alpha level
ANOVA calculator
ANCOVA (Analysis of Covariance) calculator
Evaluate treatment group differences on a continuous outcome while statistically adjusting for a baseline continuous covariate.
Run this test live in StatLab npm i @statlab/coreMathematical Formula
Y = β₀ + β₁X_covariate + β₂Group + εReporting Cautions
- Assumes parallel regression slopes across groups (homogeneity of regression slopes).
- Covariate must be measured prior to treatment intervention.
Code Snippets (Python, R, TypeScript)
Python (SciPy / Statsmodels)
import statsmodels.api as sm
from statsmodels.formula.api import ols
model = ols('post_val ~ baseline_val + C(group)', data=df).fit()
R Language
res <- aov(post_val ~ baseline_val + group, data = df)
summary(res)
TypeScript (@statlab/core)
import { ancova } from '@statlab/core';
const res = ancova(df, 'post_val', 'baseline_val', 'group');Developer Use Cases & Production Integrations
- Evaluating server optimization latency while controlling for pre-test baseline server load.
- Comparing user conversion uplift while controlling for prior account activity level.
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