What is the difference between ANCOVA and Regression?

ANCOVA or Analysis of Covariance is a statistical tool that depends on linear regression, which means the dependent variable must be linear to the independent variable. This statistical method is used to determine the extent of the variability of one variable as a result of the variability of others. Regression, on the other hand, is a statistical method used to determine how one or more independent variables are associated with a dependent variable. It is basically the relationship between one dependent variable and one or more independent variables.

It helps to determine if the values of a dependent variable will be affected due to any changes occurring to any of the independent variables. This statistical method is used in many variants, such as logistic regression, nonparametric regression, simple linear regression, stepwise regression, nonlinear regression, etc. In regression, one independent variable can be used to determine or predict the values of a dependent variable. At times, more independent variables can be used to determine or predict the values of a dependent variable.

Both ANCOVA and regression are statistical techniques and tools. ANCOVA and regression have many similarities, but they also have some marked differences as well. ANCOVA stands for analysis of variance. It handles both categorical and unremitting variables, and it is a specific statistical method used to compare the variance between variables.

ANCOVA is almost the same thing as ANOVA. However, ANCOVA is superior to the former. Regression is the relationship between the dependent variable and the independent variable. In this instance, there are one or more independent variables, and there are two different types of regression: linear regression and multiple regression. In linear, the one independent variable is used to explain or predict the outcome of something. On the other hand, multiple regression uses not one but two or more variables to predict the outcome. This regression is the method to be used for the continuous outcome.