drewlee
别人这么说,我表示怀疑。
rtist
no .
cran
不是
Ron
不好意思,请问楼上几位,如果是直线回归,这个式子成立否?
rtist
[quote]引用第3楼Ron于2007-04-22 04:25发表的“”:
不好意思,请问楼上几位,如果是直线回归,这个式子成立否?[/quote]
no
cran
drewlee
[quote]
引用第5楼cran于2007-04-23 05:39发表的“”:
http://en.wikipedia.org/wiki/R_square
国内能看wiki么?[/quote]
no
Ron
请教E(Yhat)=Ybar?
cran
ok,
R-sq = SS_R/SS_T = 1 - SS_E/SS_T
where
SS_T = sum[(y_i-yhat)^2]
SS_R = sum[(yhat_i-ybar)^2]
That is, SST is the total sum of squares, SSR is the regression sum of squares, and SSE is the sum of squared errors. In some texts, the abbreviations SSE and SSR have the opposite meaning: SSE stands for the explained sum of squares (which is another name for the regression sum of squares) and SSR stands for the residual sum of squares (another name for the sum of squared errors).
R-square is the statistic that will give information about the goodness of fit of the model. It has a drawback: R-square increases as we increase the number of variables in the mode (R-square will not decrease), so the alternative technique is to look for adjusted R-square. The explanation of this statistic is also same as R-square but it penalizes R-square by the number of variables used in the model.
longoR
[quote]引用第7楼Ron于2007-04-23 10:10发表的“”:
请教E(Yhat)=Ybar?[/quote]
this is nonsense! E(Yhat) is a constant; but Ybar is a random variable, i.e., a function!
or, are you thinking of bootstrap by conditioning on the data?
Ron
if X is a random variable, or say, (x,y) is distributed as bivariate gaussian dist, I think the formula is right
longoR
No matter whether X is a RV or not, E(Yhat) is a number, but Ybar is a RV.
If Yhat is unbiased, then E(Yhat)=E(Y) but not equal to Ybar, as E(Y) is a number and Ybar is a function.
Ron
Ye, I C, I mistaked MSE as VAR, Thanks
leffgh
发现这时特多no
weixing
哦,原来如此