Analysis of Covariance (ANCOVA) is a statistical technique used to examine whether population means of a dependent variable are equal across different levels of a categorical independent variable, while statistically controlling for the effects of other continuous variables known as covariates. This helps researchers assess if the group differences are influenced by these covariates. ANCOVA is commonly employed when there’s a need to eliminate confounding effects and increase the accuracy of group comparisons.

See also for more insights:

Statistical notes for clinical researchers: analysis of covariance (ANCOVA)

Analysis of Covariance: Univariate and Multivariate Approaches (including ANOVA and MANOVA)

📊 Looking to dive deeper into the realm of “Analysis of Covariance”? Look no further! Learn Statistics Through Practice has curated fantastic resources with examples for R-users. And for all you SAS programmers out there, detailed insights await you here:

1. University of Delaware: Handbook of Biological Statistics by John H. McDonald []

2. UCLA: SAS library how do I handle interactions of continuous and categorical variables? [] and what statistical analysis should I use (Analysis of covariance) []

3. Purdue University: Analysis of Covariance by Bruce A Craig []

Expand your knowledge and enhance your statistical toolkit! 📚📈