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

The main difference between the Analysis of Variance (ANOVA) and Analysis of Covariance (ANCOVA) is that ANCOVA controls for a variable, or covariate, that may affect the dependent variable in addition to comparing two ormore groups on the mean of some dependent variable (Mertler & Vannatta, 2016, p. 15). You would use ANCOVA instead of ANOVA when the independent variable has two or more categories, the dependent variable is quantitative, and the effects of one or more covariates need to be removed. An example of a research hypothesis that can be tested using ANCOVA: there is a relationship between race and education, controlling for age. The null hypothesis in this ANCOVA example would state the following: there is no relationship between race and education, controlling for age. Assumptions Testing for ANCOVA involves ensuring that there are no issues with the design of the study that would interfere with random sampling and independent observations; inspecting histograms, boxplots, normal Q-Q plots, skewness values, kurtosis values, and/or the Kolmogorov-Smirnov test to ensure normal distribution of scores on the dependent variable; running Box's test, Hartley's F max test, Cochran's test, or Levene's test to assess homogeneity of variances; inspecting the bivariate scatterplots between the dependent variable and the covariate to ensure a linear relationship exists between the two; accurately recording covariates and all variables to be sure that the covariate(s) are reliable; and running an F test to ensure homogeneity of regression (Mertler & Vannatta, 2016, pp. 103-105). The latter 3 are assumptions that are unique to ANCOVA.