I was trying to understand the concept of increasing the size of the dependent variable on the r-square; that is all. I was not interested in predicting the dependent variable or determining which independent variable was important. Forgive me for coming across as an idiot.
On Fri, 22 Aug 2003, Radford Neal wrote: > > In article <[EMAIL PROTECTED]>, > EAKIN MARK E <[EMAIL PROTECTED]> wrote: > > >I started a multiple regression using a dependent variable whose mean was > >zero and four independent variables. I created four more dependent variables > >by adding 10, 100, 1000, and 10000 to the first dependent. I expected the > >r-square of the no-intercept to always increase since the model is > >explaining why y differs from zero but after initially increasing, the > >r-square started to decrease again. > > I assume that you meant to say "I created more independent variables > by adding 10, 100, 1000 and 10000 to the first independent [variable]". > Creating more dependent (ie, response) variables makes no sense. > Creating more independent variables in this way also makes no sense if > the model has an intercept term, as you maybe realize. If there is no > intercept term, adding one more independent variable equal to another > independent variable plus a constant allows an intercept term to be > simulated by a combination of these two independent variables, which > will likely increase R-squared. Adding more such independent > variables allows nothing new, so R-squared should stay constant > (though "adjusted R-squared" will decrease). > > Your whole procedure makes no sense, really. Either use a model with > an intercept term or a model without an intercept term. That's all > you have to decide. Using a model without an intercept term is > generally a bad idea, unless you have a very good understanding of > what's going on that would indicate that leaving out the intercept is > appropriate. The fact that you're fiddling with these extra > independent variables is a good indication that you don't have such an > understanding. So just use a model with an intercept term and get on > with whatever your trying to accomplish by fitting this model. > > Radford Neal > > ---------------------------------------------------------------------------- > Radford M. Neal [EMAIL PROTECTED] > Dept. of Statistics and Dept. of Computer Science [EMAIL PROTECTED] > University of Toronto http://www.cs.utoronto.ca/~radford > ---------------------------------------------------------------------------- > . > . > ================================================================= > Instructions for joining and leaving this list, remarks about the > problem of INAPPROPRIATE MESSAGES, and archives are available at: > . http://jse.stat.ncsu.edu/ . > ================================================================= > Mark Eakin Associate Professor Information Systems and Management Sciences Department University of Texas at Arlington [EMAIL PROTECTED] or [EMAIL PROTECTED] . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
