If I have N candidate predictors and search all possible to subsets to
find M <= N predictors to use in a multiple linear regression model,
and I also include an intercept, effectively how many parameters do I
have in the model? The coefficients of predictors not selected are set
to zero. Only M+1 coefficients are estimated in the final model, but
there were N+1 potential coefficients in the model.
I want to plug in the # of parameters into an information criterion
such as AIC in order to choose a model, after having found the best
models with 1, 2, ..., N predictors.
Re: linear regression and multicollinearity ... I know about some ways of getting around with the multicollinearity... The "problem" when two models exist with different coefficients... visualize in some way how the predictors data affects the model. ... (sci.stat.math)
Re: Regression ... but it only makes sense if the predictors are ...highly correlated independent variables and one is nonetheless ... the coefficient of one foot to simply double if you removed the other ... but in reality both estimated coefficients would ... (sci.stat.math)
Re: Enter versus forward method for linear regression ...Regression,... present the coefficents and p values of all predictors so that readers ... try Robert Abelson's book "Statistics as Principled Argument." ... and examine the effects on the coefficients.... (sci.stat.edu)
Re: linear regression and multicollinearity ... I know about some ways of getting around with the multicollinearity... problem and a small change in predictors data may cause big changes in ... The "problem" when two models exist with different coefficients... visualize in some way how the predictors data affects the model. ... (sci.stat.math)
Re: Regression ... but it only makes sense if the predictors are ... the coefficient of one foot to simply double if you removed the other ... but in reality both estimated coefficients would ... be wildly inaccurate due to their high degree of collinearity.... (sci.stat.math)
