Compare treatment groups controlling for baseline covariates. Free.
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Running ancova treatment effect analysis analysis...
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Analyze another fileCompares treatment group outcomes while controlling for baseline covariates using ANCOVA. Tests whether the treatment effect remains after accounting for confounding variables like age or pre-treatment scores.
Use this when comparing treatment groups and you have baseline covariates that could confound the comparison.
If you have no covariates, use ANOVA. If you're testing pay equity (not treatments), use Compensation Equity.
Built for: Clinical researcher, program evaluator, health economist, policy analyst
Typical data source: Treatment/control group data with outcome measurements and baseline covariates
Treatment study data with covariates
Minimum 30 rows · Best with 100-1000 participants
Compare treatment group outcomes while controlling for baseline covariates using Analysis of Covariance. Test whether therapy types differ in effectiveness after adjusting for patient age, baseline severity, and sleep quality.
Estimated marginal means by treatment group with confidence intervals
Complete ANCOVA table with F-tests and effect sizes
Tukey-adjusted pairwise group comparisons with effect estimates
Covariate-outcome scatterplot with regression lines by group
QQ plot of ANCOVA residuals for normality assessment
Individual covariate effects and their contribution to the model
Assumption test results: slope homogeneity, normality, homoscedasticity
Plain-English interpretation — what the numbers mean, what's significant, and what to do next.
Need something simpler? Anova — No covariates to control for
Need more power? Elastic Net — Many predictors and need feature selection
Similar: Efficacy Comparison
See our FAQ for details on pricing, data privacy, and how the analysis works. Every report includes a Methodology section showing the statistical test, assumptions checked, and diagnostics run.
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