Perusing various documents is see references to least squares regression that is said to be different from OLS regression(1,2,3), and comparisons between "regression" and standard ANOVA(4).
It appears the comparisons to standard ANOVA are talking about OLS regression, due to the assumption of normality and independence of the residuals, and the assumption of the homogeneity of variance.
I am posting this to check that this is correct.
The references are included to explain why a person browsing documents would ask this question. (As two comments say this is not understood.)
Fomby T.B., Johnson S.R., Hill R.C. (1984) Review of Ordinary Least Squares and Generalized Least Squares. In: Advanced Econometric Methods. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-8746-4_2
"The purpose of this chapter is to review the fundamentals of ordinary least squares and generalized least squares in the context of linear regression analysis. ... In Section 2.4 we introduce the large sample concepts of convergence in probability and consistency. It is shown that convergence in quadratic mean is a sufficient condition for consistency and that the ordinary least squares estimator is consistent. In Section 2.5 the generalized least squares model is defined and the optimality of the generalized least squares estimator is established by Aitken’s theorem..."
"Least-squares problems fall into two categories: linear or ordinary least squares and nonlinear least squares, depending on whether or not the residuals are linear in all unknowns."
"OLS gives the maximum likelihood estimate for β when the parameters have equal variance and are uncorrelated ... Generalized least squares allows this approach to be generalized to give the maximum likelihood estimate when the noise is colored (heteroscedasticity)..."
Multiple Regression as a Flexible Alternative to ANOVA in L2 Research, Studies in Second Language Acquisition, 2017, 39, 579–592. doi:10.1017/S0272263116000231