Least Squares Mean. This is a mean estimated from a linear model. In contrast, a raw or arithmetic mean is a simple average of your values, using no model. Least squares means are adjusted for other terms in the model (like covariates), and are less sensitive to missing data.
Then, what is the principle of least squares?
The least squares principle states that the SRF should be constructed (with the constant and slope values) so that the sum of the squared distance between the observed values of your dependent variable and the values estimated from your SRF is minimized (the smallest possible value).
What is least square analysis?
The method of least squares is a standard approach in regression analysis to approximate the solution of overdetermined systems, i.e., sets of equations in which there are more equations than unknowns.
What does it mean when the regression line is the least squares?
A regression line (LSRL - Least Squares Regression Line) is a straight line that describes how a response variable y changes as an explanatory variable x changes. The line is a mathematical model used to predict the value of y for a given x. Regression requires that we have an explanatory and response variable.