Optimize Weights used in a Weighted Average

I want to calculate a weighted average, mostly for illustration purposes. However, I have an outcome variable that is Y/N and would like to optimize the weights relative to this outcome.

What are some basic ways of doing this? Basically, I want/hope to use past data to drive the weights instead of arbitrary assignment.

• Could you give more detail about what you're doing? I'm having a hard time understanding how you are using this weighted average. – dave Dec 14 '12 at 3:04

This sort of thing is always an uphill battle, but as a statistician, it's a HUGE part of your job to fight this fight. Present your results in a simple, concrete manner, that demonstrates the value of what you are doing. Don't be afraid to attach a value to your model (e.g. an incorrect prediction costs \$10 and a correct prediction is worth \$100, so my model saves the company \$10,000/month vs the old one). Start simple, and give them a chance to criticize your work, and then incorporate their feedback into the next version. Before you know it, they have a lot of investment in your model and will start finding ways to help you succeed. Good Luck! /edit 2: Here is an example in R: library(caret) set.seed(42) N <- 100 logit <- function(t){1/(1+exp(-t))} a <- runif(N); b <- runif(N); c <- runif(N) y <- 0.5*a + 5*b + 3*c + runif(N) y <- y-mean(y) y <- round(logit(y), 0) This creates some sample data py <- round(a) + 2*round(b) + 3*round(c) py <- (py-min(py))/(max(py)-min(py)) confusionMatrix(round(py), y, positive='1') >Confusion Matrix and Statistics Reference Prediction 0 1 0 41 30 1 5 24 Accuracy : 0.65 95% CI : (0.5482, 0.7427) No Information Rate : 0.54 P-Value [Acc > NIR] : 0.0169 Some arbitrary weights get us an accuracy of 65%. Not bad, but you have to consider the fact that guessing "1" every time gets us an accuracy of 54%. (That's the no information rate) py <- predict(lm(y~a+b+c)) confusionMatrix(round(py), y, positive='1') >Confusion Matrix and Statistics Reference Prediction 0 1 0 40 3 1 6 51 Accuracy : 0.91 95% CI : (0.836, 0.958) No Information Rate : 0.54 P-Value [Acc > NIR] : 8.791e-16 A linear regression gets us to an accuracy of 91%. Wahoo, you can stop here! py <- predict(glm(y~a+b+c, family=binomial(link = "logit")), type='response') confusionMatrix(round(py), y, positive='1') >Confusion Matrix and Statistics Reference Prediction 0 1 0 43 3 1 3 51 Accuracy : 0.94 95% CI : (0.874, 0.9777) No Information Rate : 0.54 P-Value [Acc > NIR] : <2e-16 Logistic regression gets us to 94% accuracy. In this example it might not be worth the extra effort, but if the sole goal is predictive accuracy it's worth demoing a superior model and evaluating the$ it could make you...