By the end of this course, students will have an understanding 
of:
(1)  The uncertainty inherent to collection of environmental 
data 
(2)  Simulation and analysis within uncertain environmental 
problem spaces
(3)  The principles and concepts of probability and statistics 
in analyzing and designing environmental engineering systems
(4)  A systematic technique for describing uncertainty and 
realizing risk in environmental applications
(5)  Quantitative methods for describing direct and indirect 
relationships among environmental variables

1)  Descriptive statistics
    a)	Means, medians, variance, co-variance, percentiles
2)  Programming in MATLAB
    a)	MATLAB language
    b)	Vectors and matrices in MATLAB
    c)	Variables, arrays and scripts
    d)	Loops
3)  Probability
    a)	Sample spaces
    b)	Assigning probabilities
    c)	Outcomes
    d)	Distributions
    e)	Random variables in environmental applications
    f)	Independent events
    g)	Joint and conditional probabilities
    h)	Repeated trials
4)  Combinatorics
    a)	Combinatorial methods
    b)	Combinatorial techniques for evaluating probability
5)  Simulation
    a)	Stochastic
    b)	Monte Carlo
    c)	Deterministic
    d)	Probability distributions
    e)	Expectation
    f)	Population mean and variance
6)  Evaluating risk within uncertain domains
7)  Data fitting
8)  Error associated with measurements
9)  Error propagation
10) Time series
11) Central Limit Theorem
12) Sampling
    a)	Populations, samples, random sampling
    b)	Error associated with sampling
    c)	Confidence intervals
13) Hypothesis formulation
14) Analysis of variance
    a)	T-test
    b)	Chi-squared
    c)	F statistics
    d)	ANOVA
    e)	Linear regression
    f)	Significance testing