Let’s compare regression and ANOVA. In simple linear regression, both the response and the predictor are continuous. In ANOVA, the response is continuous, but the predictor, or factor, is nominal. The results are related statistically. In both cases, we’re building a general linear model. But the goals of the analysis are different.
Regression gives us a statistical model that enables us to predict a response at different values of the predictor, including values of the predictor not included in the original data.
ANOVA measures the mean shift in the response for the different categories of the factor. As such, it’s generally used to compare means for the different levels of the factor.