Fall 2016
MIE Department
Prof. Ergai
EMGT 6225 Economic Decision Making
Schedule
Date
Activity
Friday, 9 September 2016
Introductions, Basics of Engineering
Economy?
Tuesday, 13 September 2016
Time Va
EngineeringProbabilityandStatistics
IE6200
Introduction Chapters1and6
Prof.Martinez
Whatengineersdo?
An engineer is someone who solves problems of interest to
society with the efficient application o
2: Joint Distributions
Bertille Antoine
(adapted from notes by Brian Krauth and Simon Woodcock)
In econometrics we are almost always interested in the relationship between two or more
random variables
Statistical methods
1.
Pareto diagrams and dot diagrams:
It is essential to collect data to provide the vital information
necessary to solve engineering problems. Graphical
illustration is an effectiv
Section 5.3
5.3.1
Expected Value and Variance
5.3 EXPECTED VALUE AND VARIANCE
def: The expected value of a random variable
X on a probability space (S, p) is the sum
E(X) =
X(s)p(s)
sS
Example 5.3.1:
Proof
P
2
(XX)
n
Is Biased Estimator Of 2
Preliminaries:
is the population mean.
2 is the population variance.
is the sample mean and is a random variable.
X
It is a given that E(X) = and Var(X) =
OR 7230 Homework #3
Due on Monday, February 27 beginning of the class
1. (10 points) For a Poisson process show, for < , that
!
!
= = =
1
, = 0,1, ,
2. (10 points) Let cfw_ , 0 be
Statistics, Intensive Week 2 Assignment.
Chapter 2, Question 23
A) Quantitative variables are when the variable studied can be reported numerically, they are
either discrete or continuous; meaning tha
Version 5/13/2011
Distributions
Using Probability DistributionsPoisson and Exponential
Section 14.0: Poisson and Exponential Distributions
In the previous chapter we developed two discrete distributio