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.  
 A: It sounds like you want to do a logistic regression.
/edit: In response to your comment: When you said "optimize a weighted average" I thought "aha! that's exactly what a regression does!" I totally sympathize with your situation, as I have been there before.  People will put a surprising amount of trust into "scoring" models that are absolutely worthless when it comes to prediction because anything more complicated is too difficult to understand.  I would say:


*

*Step 1 is a simple linear regression, where the outcome is 0/1.  This
will give you a weighted average, where the weights are your
coefficients. In fact, you don't have to tell them it's a regression
at all.  Just give them your weights, and say you optimized them
using statistical magic.  Calculate the accuracy of your model, vs
the accuracy of the old model to demonstrate that it's better.  Since
they're already using a weighted average, just find better
weights for them!

*Step 2 would be to optimize against accuracy.  The goal
is the most accurate possible classification, so you'd use a linear
solver to find the weights that maximize accuracy. (Linear regression
minimizes the sum of squared errors, so this will give a different
answer).  Again, you should be extremely concrete in explaining your
model as a weighted average with different weights, and demonstrate
that it is more accurate at predicting.

*Step 3 is to get them thinking scholastically.  "Wouldn't it be nice
if we could say there's a 75% chance of event X occurring, and act
accordingly!"  To get here, you simple plug the output from your
weighted average into the logistic function.  This will map
predictions on the scale of (-Inf,Inf) to (0,1), and they can
interpret these predictions as probabilities!

*Step 4 is to realize that the probabilities from step 3 are terrible,
and use a logistic regression, which is designed to give reasonable
probabilities.


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...
