APM395/595: PROBABILITY AND STATISTICS FOR ENGINEERS
PROFESSOR:
TEACHING
ASSISTANT:
Chuck Kroll
(470-6699)
424 Baker Lab
cnkroll@esf.edu
Ethan Bodnaruk
410 Baker Lab
ewbodnar@syr.edu
OFFICE HOURS:
To be announced. In general, will be during the 2 days before a
homework assignment is due, and by appointment.
MAILBOXES:
The course mailbox is located in the hallway outside of Baker 402.
LECTURES:
Tuesdays and Thursdays
2:00 – 3:20 pm, 145 Baker Lab
For APM595 and ERE496 Students: Thurs., 12:30 – 1:25, 437 Baker Lab
TEXT:
J.L. Devore, Probability and Statistics for Engineering and
the Sciences, 8th edition [available for purchase at the SU Bookstore]
Course reader [available for purchase in Bray Hall]
PREREQUISITES:
COURSE
OBJECTIVES:
First-year calculus, computer skills
To provide an introduction to probability and statistical theory, statistical
techniques, and uncertainty analysis with examples drawn from civil,
environmental, agricultural, water resources, geospatial, and related
engineering and environmental disciplines.
TOPICS INCLUDE: Descriptive statistics including visual and numerical data
presentation;
Probability theory, including Bayes Theorem, conditional
probability, independence, and counting;
Theory of discrete and continuous probability distributions,
including the introduction to a variety of commonly implemented
probability distributions, parameter estimation techniques, and
percentile and moment estimation;
Classical hypothesis testing and confidence interval estimation;
Linear regression and model building; and
An introduction to ANOVA (if time is available).
COURSE
OUTCOMES:
By the end of the semester students should be able to:
Understand to need for statistical techniques in aiding with many aspects
of their professional practice
Apply their knowledge of statistical and other mathematical techniques to
better understand, interpret, and solve engineering and environmental
problems
Analyze and interpret data as well as conceptualize the design of
experiments
Effectively communicate highly technical information in a clear and
concise manner
GRADING:
NOTE:
Quizzes
Homework
Semester Projects
Classroom Participation
Exam 1, Oct 13
Exam 2, Nov 10
Final
10%
15%
15%
5%
15%
15%
25%
HOMEWORK ASSIGNMENTS WILL COUNT TOWARDS
YOUR FINAL GRADE! IF YOU DON'T DO THE
HOMEWORK YOUR HIGHEST POSSIBLE GRADE IS A B!
PROBLEM SETS:
Problem sets will be due in class, usually every Thursday. In general, late assignments
will be accepted only with prior permission from the TA or Professor.
For grading purposes, the lowest homework grade will be dropped.
You will be required to use a variety of computer software packages in this class. You
will learn to use the statistical package R (which is located in all computer clusters on
campus and is available for free download online).
ACADEMIC INTEGRITY:
Any acts of plagarism, cheating, etc. will result in a grade of zero for the work
submitted, as well as other possible academic actions.
TENTATIVE COURSE SCHEDULE:
Week of:
Topics:
August 7
Visual displays of data,
Numerical displays of data
Sections in Text:
1.1 – 1.4
September 3
Elements of probability, Conditional
probability, Bayes theorem, Independence
2.1 – 2.2
2.4 – 2.5
September 10
Counting: Combinations and Permutations
2.3
September 17
Discrete random variables, Probability and
cumulative distribution functions, Expectation
and moments of discrete random variables
3.1 – 3.3
September 25
Bernoulli, geometric, and binomial distributions,
Continuous random variables, expectations and
moments of continuous random variables
3.4 – 3.5
4.1 – 4.2
October 1
Uniform and normal distributions
4.1 – 4.3
October 8
Review for Exam 1/Exam 1
October 15
Normal approximation to binomial distribution
Lognormal distribution,
Poisson and Gamma distributions
4.3
4.5
3.6, 4.4
October 22
Joint probability distributions, Covariance and
correlation coefficients, Central limit theorem,
Bias, variance, and mean square error,
Parameter estimation
5.1 – 5.2,
5.3 – 5.4,
6.1 – 6.2
October 29
Hypothesis testing, Type I and II errors
P-values, choice of hypothesis
8.1 – 8.2
8.4 – 8.5
November 5
Review for Exam 2/Exam 2
November 12
Simple and multiple linear regression, Parameter
estimation, Model inferences and predictions
November 19
Thanksgiving Break
November 26
Probability plot correlation coefficients and tests
of normality, Confidence intervals for the mean
14.2
7.1 – 7.3
December 3
Confidence intervals for the variance and standard
deviation, course summary
7.4
12.1 – 12.5
13.4 – 13.5