what do there two refer to? HOw do they differ from just R^2? Thanks
R squared is the coefficient of determination that shows the total variation (not to be confused with variance) in the dependent variable explained by the independent variables. I.e., RSS divided by SST The problem with this measure is that it keeps increasing everytime you add another explanatory variable, even if it is not adding value to the model. To account for this issue, R squared adjusted is calculated which just to be noted can actually be negative in certain cases. R squared in general is very important in seeing whether a model is capturing the variation in what you’re trying to explain! High R squared is typical for a good model (but it isn’t always very high, depending on what type of series you’re dealing with) Hope this helped
Thanks. helps a lot. what is multiple-R^2 ? Thanks. MFIN— Wrote: ------------------------------------------------------- > R squared is the coefficient of determination that > shows the total variation (not to be confused with > variance) in the dependent variable explained by > the independent variables. I.e., RSS divided by > SST > > The problem with this measure is that it keeps > increasing everytime you add another explanatory > variable, even if it is not adding value to the > model. To account for this issue, R squared > adjusted is calculated which just to be noted can > actually be negative in certain cases. > > R squared in general is very important in seeing > whether a model is capturing the variation in what > you’re trying to explain! High R squared is > typical for a good model (but it isn’t always very > high, depending on what type of series you’re > dealing with) > > Hope this helped
multiple r^2 is just a term used to tell you there are many variables, not just your standard regression with an x and y. you only adjust for multiple r^2 when you think too many variable are driving your goodness-of-fit too high.