The regression R2 is: a. possible to decrease when an additional explanatory variable is added. b. R S S divided by T S S. c. a measure of the goodness of fit of your regression line. d. a measure of the causal effect of X on Y.

In regression analysis, r2 is the ___.Group of answer choicescoefficient of determinationsum of the squaredestimated regression analysiscoefficient of correlation

Which two of the of the following statements are correct regarding R2 as a tool to evaluate a regression model. Multiple answers allowed.a.The R2 is the proportion of the total variation in X that can be explained by the variation in the dependent variable in the model.b. If the R2 value is greater than 1, then it shows that the regression model is a very good fit.c.The R2 value needs to be reported in presenting regression results as it shows the relative precision of the model for the users to consider.d.R2 reflects the model’s improvement of the predicted value over the estimator of the sample mean (Y-bar).

The square of the correlation coefficient, r2, provides a measure of the _____________.Multiple Choicedifference between positive and negative correlation between two variablesslope of the line for a simple linear regression equationerror in predicting values of y based on a regression equationproportion of variability in the values of y "explained" by an independent variable

. R squared equals 1 minus (ESS/TSS), where ESS represents the explained sum of squares and TSS represents the total sum of squares. R-squared is a measure of the proportion of the total variation in the dependent variable that is explained by the independent variables in a regression model.

When conducting a regression analysis, what does the coefficient of determination (R-squared) represent?  A. The strength of the relationship between variables  B. The predicted values of the dependent variable  C. The slope of the regression line  D. The proportion of variance in the dependent variable explained by the independent variable