UPDATE (please check again later)
------

There will be about 8 or 9 questions. The focus may be on the latter 
half of the course.

- estimators
- confidence intervals, assumptions, normal random variables, inequalities
- know the explicit forms of all probability distributions
- random variables, sums of random variables, means, variances, covariances
- marginal distributions, joint distributions
- models we have discussed in class or studied in hw
- transience, steady state, Welch's procedure
- variance reduction methods


General Information for Final
-----------------------------

NOTE: The exam is closed-book, but you are allowed to bring one standard page (8.5x11 ins) of notes. 

I'll post more info on the final when I have it ready. It will be along the
line of the midterm.  As mentioned in class, the topics are: 

    -- Early lectures on how to do event-scheduling simulation, how to compute 
       statistics in simulation
    -- all statistical distributions done in class
    -- memorylessness, independence, dependence, mean, variance, covariance
    -- random numbers, independence, exponential random numbers, uniform random numbers
    -- very simple models (Poisson, simple birth-death, M/M/1 queue) as done in class
    -- operational analysis
    -- Burke's Thm
    -- means, variances, Markov inequality, Chebyshev inequality
    -- Estimators for mean, variance, unbiasedness
    -- confidence intervals, types of confidence intervals 
    -- use of normal random variables and t-distribution for confidence intervals
    -- ways of terminating simulations, stopping conditions, steady-state, Welch's procedure
    -- effect of correlation on confidence intervals
    -- covariance, variance-reduction (anthitetic variates, covariate method)