A regression line is the line that best fits the data, but this does not mean that the fit is good. In other words, there can still be a lot of variability about the regression line. Which combination describes a regression line that is a good fit for the data?

Larger-sq and small Se.

Explanation:

Regression line is a line that clearly describes the behavior of a given set of data.

Regression lines are very essential for forecasting processes. The importance of the line is to describe the interrelation of a dependent variable (Y variable) with one or many independent variables (X variable).

An analyst can forecast future behaviors of the dependent variable by making use of the equation gotten the regression line. This is done by inputting different values for the independent ones. Regression lines are frequently employed in the financial sector.

Financial analysts make use of linear regressions to forecast stock prices, commodity prices and also to carry out valuations for many different securities. Companies use regressions for the purpose of forecasting sales, inventories and a lot of other variables that are needed for strategy and planning. The regression line formula is represented below:

(Y = a + bX + u)

answer; ///the correct answer is (ftc) federal trade commission;

answer; /// blank(1); increases; blank(2); decreases;