search for: x_bar

thumbnailer/swfdec-thumbnailer.c | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) New commits: commit a31d0686b78df2da96b9d8d1e3220e63978bba30 Author: Benjamin Otte <otte at gnome.org> Date: Wed Nov 7 20:02:04 2007 +0100 s/swfdec_player_get_image_size/swfdec_player_get_default_size/ diff --git a/thumbnailer/swfdec-thumbnailer.c b/thumbnailer/swfdec-thumbnailer.c index

...0.7254615 predict.lm(model,newdata=data.frame(x=c(10,20)),se.fit=T,interval="prediction")$se.fit 1 2 0.2708064 0.7254615 I was surprised to find that the standard errors returned were in fact the standard errors of the sampling distribution of Y_hat: sqrt(MSE(1/n + (x-x_bar)^2/SS_x)), not the standard errors of Y_new (predicted value): sqrt(MSE(1 + 1/n + (x-x_bar)^2/SS_x)). Is there a reason this quantity is called the "standard error of predicted means" if it doesn't relate to the prediction distribution? Turning to Neter et al.'s Applied Linear...

Hi, I posted this message earlier in "Rmetrics" and I don't know whether I posted in the wrong place, so I'm posting it again in Rhelp. I have a function in x and y and let's call it f(x,y). I need to get the Hessian matrix. i.e I need (d^2f/dx^2), (d^2f/dxdy), (d^2f/dydx), (d^2f/dy^2).I can get these using the D function. now I need to evaluste the hessian matrix for

...ts)) a <- qnorm(alpha/2) d <- alpha*project.mean # verifier plot number n<-length(verification.plots) verifier.plot.number <- c(1:n) #all plots (verifier and project) all.plots.n <- rep(0,n) for(i in 1:n){ all.plots.n[i] <- project.n + verifier.plot.number[i] } #running mean X_bar <- rep(1,n) for(i in 1:n){ X_bar[i]<- mean(verification.plots[1:i]) } #running sd sd2 <- NULL for(i in 2:n){ sd2[i]<-sd(verification.plots[1:i]) } #Tn Tn<-NULL for(i in 2:n){ Tn[i] <- project.mean-X_bar[i] } #n_Threshold n_thresh<-NULL for(i in 2:n){ n_thresh[i] <- ((...

Hi all, I have a data set containing variables LOSS, GDP, HPI and UE. (I have attached it in case it is required). Having renamed the variables as l,g,h and u, I wish to run a Lasso Regression with l as the dependent variable and all the other 3 as the independent variables. data=read.table("data.txt", header=T) l=data$LOSS h=data$HPI u=data$UE g=data$GDP matrix=data.frame(l,g,h,u)

Hi, I am trying to make a sequence and am using a for loop for this. I want to start off with an initial value ie S[0]=0 then use the loop to create other values. This is what I have so far but I just keep getting error messages. #To calculate the culmulative sums: s<-rep(0,207) #as this is the length of the vector I know I will have s<-as.vector(s)