How would you respond to the following paragraph? Do you agree or disagree with the information?

The main difference between Analysis of Variance and Analysis of Covariance is the analysis of data before irregularities have been removed. With an Analysis of Variance, you will compare the means of the raw data of three or more groups while with an Analysis of Covariance you will first remove the outliers from the data before attempting to analyze or manipulate it in any way. One would use an Analysis of Covariance when there are three or more groups and there is a desire to obtain more accurate results once outliers have been removed from the data.

One example of an Analysis of Covariance could be if a university used three different application processes to solicit new incoming studentsonline application, mobile app application, or telephone application. The covariant could be choosing students in their immediate city as the sample.

With assumptions testing for an Analysis of Covariance, there are a few rules that must be met. The covariate and the factor variable must be independent of each other, the covariant should be continuous data, the variances should be equal, and the data should be normal. Lastly, there should not be any outliers present in the data which is only an assumption unique to Analysis to Covariance.