the act of going back
Regression means the act of going back to a previous place or state, return or reversion.
Goes back, reverts
(mean x, mean y) is always on the regression line.
Regression Analysis:The average relationship between two or more variable.In English: Regression Mean Stepping back or moving toward the average.
8.7.4 Properties of Regression Coefficients:(a) Correlation coefficient is the geometric mean between the regression coefficients. (b) If one of the regression coefficients is greater than unity, the other must be less than unity.(c) Arithmetic mean of the regression coefficients is greater than the correlation coefficient r, providedr > 0.(d) Regression coefficients are independent of the changes of origin but not of scale.
(a) Correlation coefficient is the geometric mean between the regression coefficients. (b) If one of the regression coefficients is greater than unity, the other must be less than unity. (c) Arithmetic mean of the regression coefficients is greater than the correlation coefficient r, provided r > 0. (d) Regression coefficients are independent of the changes of origin but not of scale.
Regression mean squares
regression is calculation mean.
of, pertaining to, or determined by regression analysis: regression curve; regression equation. dictionary.com
Unit regression testing Regional regression testing Full regression testing
The F-statistic is a test on ratio of the sum of squares regression and the sum of squares error (divided by their degrees of freedom). If this ratio is large, then the regression dominates and the model fits well. If it is small, the regression model is poorly fitting.
Errors are normally distributed with mean 0 .
The F-statistic is a test on ratio of the sum of squares regression and the sum of squares error (divided by their degrees of freedom). If this ratio is large, then the regression dominates and the model fits well. If it is small, the regression model is poorly fitting.
Simple regression is used when there is one independent variable. With more independent variables, multiple regression is required.