IE 111: Engineering Probability and Statistics
Syllabus: Spring Semester 2009
Course Description
This course is an introductory course to the fields of Probability and Statistics designed
for engineering students. The course focuses primarily on the study of Probability
Theory. We may also cover some Statistics toward the end. Probability Theory is of great
use in all branches of Engineering in understanding and modeling phenomena that exhibit
random behavior. Probability Theory also provides the theoretical and mathematical basis
for statistics, and thus must be studied first.
The field of Statistics pertains to the presentation, analysis and interpretation of
data
.
Engineers will be faced with the need to analyze data on a daily basis in the real world,
and thus a good grounding in the basics of statistics is invaluable. Statistics is inherently
inductive since inference is made about a whole population on the basis of
information/data obtained from a
sample
from the population.
Unlike Statistics, Probability theory is inherently deductive, and has nothing to do with
sample data. Rather it is a field of mathematics from which results and conclusions are
derived from propositions and assumptions. A typical easy problem that one could solve
using probability theory is "given that the probability of a coin flip coming up heads is
0.5, what is the probability that I will get exactly 5 heads if I flip the coin 10 times?" Note
the absence of any sample data in this problem. Given an assumption (probability of a
head is 0.5) one deduces the conclusion (the probability of exactly 5 heads is 0.2461).
Statistics is probably more useful for most engineers than probability. However, the
theory that underlies statistics is probability, which makes its study necessary as well.
The study of Probability Theory can be fun and interesting, but also difficult, confusing
and frustrating. In particular, the use of counting methods to compute probabilities, which
comes early in the class, is likely the most confusing and frustrating part of the course (in
addition to hopefully being fun).
Course Objectives
Upon completion of this course, students will:
•
Know the basic axioms and set theory upon which probability theory is based
including sample spaces and events, mutual exclusivity, conditional probability,
independence, and Bayes theorem.
This preview has intentionally blurred sections. Sign up to view the full version.
View Full Document
This is the end of the preview. Sign up
to
access the rest of the document.
 Spring '08
 Rodriguez
 Statistics, Probability, Probability theory

Click to edit the document details