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COURSE
NUMBER
AEM2100
AEM4100
AEM4110
AEM4160
SEMESTER
Spring
Fall
Fall
Spring
CREDITS
4
3
3
3
COURSE
NAME
DESCRIPTION
PROFESSOR
PREREQUISITE
Introductory
Statistics
Introduces statistical methods. Topics include the
descriptive analysis of data, probability concepts and
distributions, estimation and hypothesis testing,
regression, and correlation analysis. Includes an
introduction to Minitab, a statistical software package.
C. L. van Es.
college algebra.
Business
Statistics
Focuses on techniques used to analyze data from
marketing research, business, and economics. Topics
include experimental design and ANOVA,
contingency-table analysis, quality-control methods,
time-series analysis, and forecasting. Also includes
brief introductions to nonparametric methods and
multivariate analysis. Involves a research project
designed to give experience in collecting and
interpreting data.
C. L. van Es
AEM 2100 or
equivalent.
Introduction
to
Econometrics
Introduces students to basic conometric principles and
the use of statistical procedures in empirical studies of
economic models. Assumptions, properties, and
problems encountered in the use of multiple regression
are discussed as are simultaneous equation models,
simulation, and forecasting techniques.
D. R. Just.
AEM 2100 and
either ECON
3130 or PAM
2000 or
equivalents.
Strategic
Pricing
This quantitative course explores various pricing
strategies by taking into consideration the role of
consumer behavior, economics, statistics, and
management science. Topics include product tying and
bundling, peak load pricing, price matching, warranty
pricing, advanced booking, and the 99-cent pricing
perceptions.
J. Liaukonyte.
ECON 3130,
AEM 2100, or
equivalent.
NOTES
AEM4170
AEM6120
AEM7100
AEM7110
Fall
Fall
Spring
Fall
3
1
3
3
Decision
Models for
Small and
Large
Businesses
Focuses on economic and statistical models of decision
analysis and their applications in large and small
business settings. Demonstrates how use of models can
improve the decision-making process by helping the
decision maker. Emphasizes the importance of
sensitivity analysis and the need to combine both
quantitative and qualitative considerations in decision
making. Draws cases from small business scenarios,
the public policy arena, and corporate settings. Lab
sessions focus on implementing decision models with
computers.
C. L. van Es.
AEM 2100 or
equivalent.
Applied
Econometrics
Designed for M.S. and Ph.D. students who do not meet
the prerequisites for other graduate-level econometrics
courses. Complements AEM 4110, providing greater
depth of understanding of econometric methods and
exposure to applied econometric literature. Focuses on
preparing students to conduct their own applied
economic research.
D. R. Just.
Co-requisite:
AEM 4110.
Econometrics
I
This is an applied econometrics course with an
extensive “hands-on” approach. It provides (together
with AEM 7110) a graduate sequence in applied
econometrics that is suitable for M.S. and PhD
students. Covers linear and discrete choice models and
estimation methods such as GMM and MLE.
Programming using Stata or Matlab is expected.
S. Li.
matrix algebra
and statistical
methods courses
at level of
ILRST 3110 or
ECON 6190
Econometrics
II
Coverage beyond AEM 7100 of dynamic models,
including single-equation ARIMA, vector ARIMA,
Kalman filtering, structural dynamic models, and
regime switching. Topics include endogeneity,
stability, causality, and cointegration.
T. D. Mount.
AEM 7100 or
equivalent.
Enrollment is
limited to:
juniors or
seniors
(priority
given to
AEM
majors). No F
lec in weeks
labs are held.
AEM7120
ASTRO6523
Fall
Spring
4
4
Quantitative
Methods I
Comprehensive treatment of linear programming and
its extensions, including postoptimality analysis.
Topics include nonlinear programming, including
separable, spatial equilibrium, and risk programming
models. Discusses input-output models and their role in
social accounting matrices and computable general
equilibrium models. Makes applications to agricultural,
resource, and regional economic problems.
R. N. Boisvert.
Signal
Modeling,
Statistical
Inference,
and Data
Mining in
Astronomy
Aims to provide tools for modeling and detection of
various kinds of signals encountered in the physical
sciences and engineering. Data mining and statistical
inference from large and diverse databases are also
covered. Experimental design is to be discussed. Basic
topics include probability theory; Fourier analysis of
continuous and discrete signals; digital filtering;
matched filtering and pattern recognition; spectral
analysis; Karhunen-Loeve analysis; wavelets;
parameter estimation; optimization techniques;
Bayesian statistical inference; deterministic, chaotic,
and stochastic processes; image formation and
analysis; maximum entropy techniques. Specific
applications are chosen from current areas of interest in
astronomy, where large-scale surveys throughout the
electromagnetic spectrum and using nonelectromagnetic signals (e.g., neutrinos and
gravitational waves) are ongoing and anticipated.
Applications are also chosen from topics in geophysics,
plasma physics, electronics, artificial intelligence,
expert systems, and genetic programming. The course
is self-contained and is intended for students with
thorough backgrounds in the physical sciences or
engineering.
J. Cordes.
some formal
training in matrix
algebra.
BEE4600
BIOMG4870
BIOMG6300
Fall
Fall
Spring
Deterministic
and
Stochastic
Modeling in
Biological
Engineering
Covers modeling biological systems from an
engineering standpoint. Starting with deterministic
approaches, the course functionally decomposes and
mathematically models systems important to biological
engineers (including bioprocessing, biomedicine, and
microbial ecology). Mechanistic aspects of biology are
handled using stochastic (probabilistic) approaches in
the second half of the semester.
J. C. March.
MATH 2930,
MATH 2940,
BEE 3500 or
equivalent, Mass
and Energy
Balances, or
permission of
instructor.
Satisfies BE
capstone design
requirement.
3
Human
Genomics
Applies fundamental concepts of transmission,
population, and molecular genetics to the problem of
determining the degree to which familial clustering of
diseases in humans has a genetic basis. Emphasizes the
role of full genome knowledge in expediting this
process of gene discovery. Stresses the role of
statistical inference in interpreting genomic
information. Population genetics, and the central role
of understanding variation in the human genome in
mediating variation in disease risk, are explored in
depth. Methods such as homozygosity mapping,
linkage disequilibrium mapping, and admixture
mapping are examined. The format is a series of
lectures with classroom discussion. Assignments
include a series of problem sets and a term paper
A. Clark.
BIOMG2810
3
Mathematical
Analysis and
Computationa
l Statistics of
the Molecular
Cell
Using case studies, we will explore how mathematical
models and statistics can be used to generate and test
biological hypotheses using Excel and Mathematica
(no prior experience needed). One term of calculus,
one term of statistics, familiarity with ordinary
differential equations and linear algebra, and a laptop
computer are required.
D. Shalloway.
3
BIOMS7070
Spring
1
Current
Research in
Genomics
This course will present students with faculty
perspectives on current research in genomics. Lectures
and/or practical exercises will be given by faculty with
expertise in specific areas of genomics. The goal is to
provide students with an overview of major questions
in genomics that are being addressed in different areas
of study.
D. Lin.
BME5400
Fall
3
Biomedical
Computation
The application of numerical and statistical methods to
model biological systems and interpret biological data,
using the MATLAB programming language.
M. R. King.
MATH 2930 and
MATH 2940 (or
equivalent), and
introductory
computer
programming
course.
BTRY3010
Fall
4
Biological
Statistics I
See NTRES 3130.
Staff.
one semester of
calculus.
Biological
Statistics II
Applies linear statistical methods to quantitative
problems addressed in biological and environmental
research. Methods include linear regression, inference,
model assumption evaluation, the likelihood approach,
matrix formulation, generalized linear models, singlefactor and multifactor analysis of variance (ANOVA),
and a brief foray into nonlinear modeling. Carries out
applied analysis in a statistical computing environment.
Staff.
BTRY 3010 or
BTRY 6010.
See STSCI3080.
M. T. Wells.
See STSCI3100.
Staff.
two semesters of
statistics.
G. Hooker.
BTRY 3080,
enrollment in
MATH 2220 and
MATH 2240 or
equivalents.
BTRY3020
Spring
4
BTRY3080
Fall
4
BTRY3100
Fall
4
BTRY3520
Spring
4
Probability
Models and
Inference
Statistical
Sampling
Statistical
Computing
This course is designed to provide students with an
introduction to statistical computing. The class will
cover the basics of programming; numerical methods
for optimization and linear algebra and their
application to statistical estimation, generating random
variables, bootstrap, jackknife and permutation
methods, Markov Chain Monte Carlo methods,
Bayesian inference and computing with latent
variables.
BTRY4030
BTRY4090
BTRY4100
BTRY4140
BTRY4270
Fall
Spring
Spring
Spring
Fall, spring
3
4
Applied
Linear
Statistical
Models via
Matrices
See STSCI 4030.
Theory of
Statistics
Introduction to classical theory of parametric statistical
inference that builds on the material covered in BTRY
4080. Topics include sampling distributions, principles
of data reduction, likelihood, parameter estimation,
hypothesis testing, interval estimation, and basic
asymptotic theory.
J. G. Booth.
a second noncalculus course
in statistics,
preferably on
multiple
regression, and
at least one
semester of basic
matrix (linear)
algebra.
Staff.
BTRY 3080 or
equivalent and at
least one
introductory
statistics course.
4
Multivariate
Analysis
See STSCI 4100.
Staff.
ILRST 3120,
STSCI 2200, or
equivalent; some
knowledge of
matrix-based
regression
analysis.
4
[Statistical
Methods IV:
Applied
Design]
See STSCI 4120.
Staff.
STSCI 3200 or
permission of
instructor.
Introduction
to Survival
Analysis
Develops and uses statistical methods appropriate for
analyzing right-censored (i.e., incomplete) time-toevent data. Topics covered include nonparametric
estimation (e.g., life table methods, Kaplan Meier
estimator), nonparametric methods for comparing the
survival experience of two or more populations, and
semiparametric and parametric methods of regression
for censored outcome data. Substantial use is made of
the R statistical software package.
R. Strawderman.
BTRY 4090,
MATH 4720, or
equivalent
preparation; 3
semester of
calculus.
3
BTRY4820
BTRY4830
BTRY4840
BTRY4940
Spring
Spring
Fall
Fall, spring
A. Keinan.
MATH 1110 or
equivalent. At
least one
previous course
in statistical
methods and at
least one
involving
programming, or
permission of
instructor.
Staff.
BTRY 3080 and
introductory
statistics or
equivalent.
Computationa
l Genomics
Computational methods for genomic data, emphasizing
comparative and evolutionary genomics. Topics
include sequence alignment, gene and motif finding,
phylogeny reconstruction, and gene regulatory
networks. Meets concurrently with BTRY 6840.
Staff.
BTRY 3080 and
at least one
course in
statistical
methods and at
least one in
algorithms.
Undergraduat
e Special
Topics in
Biometry and
Statistics
Course of lectures selected by the faculty. Because
topics usually change from year to year, this course
may be repeated for credit.
Staff.
Statistical
Genomics
Statistical methods of genomic data, emphasizing
coalescent theory and molecular population genetics
and genomics. Topics include derivation of coalescent
theory, tests of natural selection, population structure,
and statistical inference, with a focus on the population
genomics of human populations.
Quantitative
Genomics
and Genetics
A rigorous treatment of analysis techniques used to
understand complex genetic systems. This course
covers both the fundamentals and advances in
statistical methodology used to analyze disease and
agriculturally relevant and evolutionarily important
phenotypes. Topics include mapping quantitative trait
loci (QTLs), application of microarray and related
genomic data to gene mapping, and evolutionary
quantitative genetics. Analysis techniques include
association mapping, interval mapping, and analysis of
pedigrees for both single and multiple QTL models.
Application of classical inference and Bayesian
analysis approaches is covered and there is an
emphasis on computational methods. Meets
concurrently with BTRY 6830.
4
1-3
4
4
Co-meets
with BTRY
6820.
BTRY4970
Fall, spring
1-3
Undergraduat
e Individual
Study in
Biometry and
Statistics
Consists of individual tutorial study selected by the
faculty. Because topics usually change from year to
year, this course may be repeated for credit.
Staff.
Students assist in teaching a course appropriate to their
previous training. Students meet with a discussion or
laboratory section and regularly discuss objectives with
the course instructor.
Staff.
BTRY4980
Fall, spring
1-3
Undergraduat
e Supervised
Teaching
BTRY4990
Fall, spring
1-3
Undergraduat
e Research
BTRY5080
Fall
4
Probability
Models and
Inference
Staff.
See STSCI 5080.
M. T. Wells.
Students must
register using
independent
study form
(available in
140 Roberts
Hall).
Students must
register using
independent
study form
(available in
140 Roberts
Hall).
statistics and
biometry
undergraduat
e students.
Permission of
faculty
member
directing
research is
required.
Students must
register using
independent
study form
(available in
140 Roberts
Hall).
BTRY6010
BTRY6020
BTRY6030
BTRY6070
Fall
Spring
Spring
Fall
Statistical
Methods I
Develops and uses statistical methods to analyze data
arising from a wide variety of applications. Topics
include descriptive statistics, point and interval
estimation, hypothesis testing, inference for a single
population, comparisons between two populations,
one- and two-way analysis of variance, comparisons
among population means, analysis of categorical data,
and correlation and regression analysis. Introduces
interactive computing through statistical software.
Emphasizes basic principles and criteria for selection
of statistical techniques.
4
4
4
4
Staff.
Permission of
instructor or
graduate
standing is
required.
Statistical
Methods II
Continuation of BTRY 6010. Emphasizes the use of
multiple regression analysis, analysis of variance, and
related techniques to analyze data in a variety of
situations. Topics include an introduction to data
collection techniques; least squares estimation;
multiple regression; model selection techniques;
detection of influential points, goodness-of-fit criteria;
principles of experimental design; analysis of variance
for a number of designs, including multi-way factorial,
nested, and split plot designs; comparing two or more
regression lines; and analysis of covariance.
Emphasizes appropriate design of studies before data
collection, and the appropriate application and
interpretation of statistical techniques. Practical
applications are implemented using a modern, widely
available statistical package.
Staff.
BTRY 6010 or
equivalent.
Permission of
instructor or
graduate
standing is
required.
Statistical
Methods III:
Categorical
Data
See STSCI4110.
Staff.
ILRST 3120,
STSCI 2200, or
equivalent.
Principles of
Probability
and Statistics
Topics include combinatorial probability, conditional
probability and independence, random variables,
standard distributions, maximum likelihood and
Bayesian approaches. Emphasizes computational
methods using R programming language.
Staff.
one year of
calculus.
Recommended
prerequisite:
some knowledge
of multivariate
statistics.
BTRY6150
BTRY6520
BTRY6700
BTRY6790
Fall
Spring
Fall
Fall
3
Applied
Functional
Data Analysis
Functional data analysis studies data that may be
thought of as continuously sampled smooth curves.
The course focuses on extensions of standard statistical
techniques to these data.
Staff.
4
Computationa
lly Intensive
Statistical
Methods
See STSCI6520.
Staff.
Applied
Bioinformatic
s and Systems
Biology
An introductory course on tools and techniques for the
analysis of molecular biological data, including
biosequences, microarrays, and networks. This course
emphasizes practical skills, as well as basic
understanding of theories and algorithms for proper
application of various techniques. Two different
computer languages (R and Perl) will be introduced
and used throughout the lectures and homework.
Possible topics include sequence alignment, gene and
motif finding, genome assembly, variant detection,
demographic inference, detection of natural selection,
association mapping, phylogeny reconstruction,
microarray analysis, and methods for inferring and
analyzing regulatory, protein-protein interaction, and
metabolite networks.
4
4
Probabilistic
Graphical
Models
A thorough introduction to probabilistic graphical
models, a flexible and powerful graph-based
framework for probabilistic modeling. Covers directed
and undirected models, exact and approximate
inference, and learning in the presence of latent
variables. Hidden Markov models, conditional random
fields, and Kalman filtering are explored in detail.
BTRY 6010 and
BTRY 6020 or
permission of
instructor.
ORIE 6700 (or
equivalent) and
at least one
course in
probability, or
approval of
instructor.
H. Yu, A. Keinan,
and A. Siepel.
introductory
courses in
computer
programming
and statistical
methods are
highly
recommended.
For those who do
not have prior
programming
experience,
please discuss
with Dr. Yu
about taking the
course.
Staff.
probability
theory (BTRY
3080 or
equivalent),
programming
and data
structures (CS
2110 or
equivalent).
Recommended
prerequisite:
course in
statistical
methods (BTRY
4090 or
equivalent).
Offered
alternate
years
BTRY6820
BTRY6830
BTRY6840
BTRY6890
Spring
Spring
Fall
Fall, spring
4
Statistical
Genomics
4
Quantitative
Genomics
and Genetics
4
1
BTRY6940
Fall, spring
1-3
BTRY6970
Fall, spring
1-3
See BTRY4820.
See BTRY4830.
A. Keinan.
MATH 1110 or
equivalent. At
least one
previous course
in statistical
methods and at
least one
involving
programming, or
permission of
instructor.
Staff.
BTRY 3080 and
introductory
statistics course
or equivalent.
Computationa
l Genomics
See BTRY4840.
Staff.
BTRY 3080 and
at least one
previous course
in statistical
methods and at
least one in
algorithms.
Topics in
Population
Genetics and
Genomics
Graduate seminar on current topics in population
genetic data analysis. Topics this semester may include
detecting signatures of natural selection, estimating
demographic parameters, and recombination rate
variation from whole-genome data; statistical methods
for association mapping; efficient methods for disease
gene mapping; and use of comparative genomic data
for population genetic inference. Readings are chosen
primarily from current literature.
Staff.
BTRY 6820 or
permission of
instructor.
Course of lectures selected by the faculty. Because
topics usually change from year to year, this course
may be repeated for credit.
Staff.
Individual tutorial study selected by the faculty.
Because topics usually change from year to year, this
course may be repeated for credit.
Staff.
Graduate
Special
Topics in
Biometry and
Statistics
Individual
Graduate
Study in
Biometry and
Statistics
Co-meets
with BTRY
4820.
(May be
repeated for
credit)
BTRY7170
BTRY7180
BTRY7200
BTRY7210
BTRY7270
BTRY7900
Fall
Fall
Spring
Fall
Spring
Fall, spring
3
Theory of
Linear
Models
Properties of the multivariate normal distribution.
Distribution theory for quadratic forms. Properties of
least squares and maximum likelihood estimates.
Methods for fixed-effect models of less than full rank.
Analysis of balanced and unbalanced mixed-effects
models. Restricted maximum likelihood estimation.
Some use of software packages and illustrative
examples
3
Generalized
Linear
Models
A theoretical development of generalized linear models
and related topics including generalized estimating
equations, and generalized linear mixed models.
1
Topics in
Computationa
l Genomics
Weekly seminar series on recent advances in
computational genomics. A selection of the latest
papers in the field are read and discussed. Methods are
stressed, but biological results and their significance
are also addressed.
1
Topics in
Quantitative
Genomics
Weekly seminar series on recent advances in
quantitative genomics. A selection of the latest papers
in the field is read and discussed. Methods are stressed,
but biological results and their significance are also
addressed.
3
Advanced
Survival
Analysis
1-9
GraduateLevel
Dissertation
Research
Focuses on the rigorous development of nonparametric,
semiparametric, and parametric modeling and
statistical inference procedures appropriate for
analyzing right censored data.
Research at the Ph.D. level.
Staff.
BTRY 4090,
BTRY 6020, or
equivalents.
G. Hooker.
BTRY 6020,
BTRY 4090, or
equivalent.
Staff.
BTRY
4840/BTRY
6840 or
permission of
instructor.
Staff.
BTRY
4830/BTRY
6830 or
permission of
instructor.
Staff.
at least one
graduate-level
course in
probability,
mathematical
statistics, and
regression
modeling.
Staff.
Permission of
graduate field
member
concerned is
required.
Primarily for
Ph.D.
students in
statistics.
Enrollment is
limited to:
Ph.D.
candidates.
BTRY7950
BTRY7980
BTRY8900
BTRY9900
CEE3040
Fall, spring
Fall, spring
Fall, spring
Fall, spring
Fall
2-3
Statistical
Consulting
2-4
Graduate
Supervised
Teaching
1-9
Master’sLevel Thesis
Research
1-9
DoctoralLevel
Dissertation
Research
4
Uncertainty
Analysis in
Engineering
Participation in the Cornell Statistical Consulting Unit:
faculty-supervised statistical consulting with
researchers from other disciplines. Discussion sessions
are held for joint consideration of literature and
selected consultations encountered during previous
weeks.
Students assist in teaching a course appropriate to their
previous training. Students meet with a discussion
section, prepare course materials, and assist in grading.
Credit hours are determined in consultation with the
instructor, depending on the level of teaching and the
quality of work expected.
Research at the M.S. level.
Staff.
Prerequisite or
co-requisite:
BTRY 6020 and
BTRY 4090.
Permission of
instructor is
required.
Staff.
at least two
advanced
courses in
statistics and
biometry.
Permission of
instructor and
chair of special
committee is
required.
Staff.
Permission of
graduate field
member
concerned is
required.
Staff.
Introduction to probability theory and statistical
techniques, with examples from civil, environmental,
biological, and related disciplines. Covers data
presentation, commonly used probability distributions
describing natural phenomena and material properties,
parameter estimation, confidence intervals, hypothesis
testing, simple linear regression, and nonparametric
statistics. Examples include structural reliability,
windspeed/flood distributions, pollutant concentrations,
and models of vehicle arrivals.
J. R. Stedinger.
first-year
calculus
Enrollment is
limited to:
M.S.
candidates.
CEE5290
CEE7710
COMM2820
Fall
Fall
Fall
##
Heuristic
Methods for
Optimization
(also CS
5722, ORIE
5340)
Teaches heuristic search methods including simulated
annealing, tabu search, genetic algorithms,
derandomized evolution strategy, and random walk
developed for optimization of combinatorial- and
continuous-variable problems. Application project
options include wireless networks, protein folding, job
shop scheduling, partial differential equations,
satisfiability, or independent projects. Statistical
methods are presented for comparing algorithm results.
Advantages and disadvantages of heuristic search
methods for both serial and parallel computation are
discussed in comparison with other optimization
algorithms.
C. A. Shoemaker.
graduate
standing or CS
2110/ENGRD
2110; ENGRD
3200 or
permission of
instructor.
3
Stochastic
Problems in
Science and
Engineering
Review of probability theory, stochastic processes, and
Ito formula with illustrations by Monte Carlo
Simulation.
M. D. Grigoriu
permission of
instructor
Research
Methods in
Communicati
on Studies
(SBA)
Covers social scientific methods to solve
communication research problems empirically. Topics
include basic principles of social scientific research,
random sampling, questionnaire design, experimental
research design, focus group techniques, content
analysis, and basic descriptive and inferential statistics.
Students also learn basic data manipulation,
presentation, and analysis techniques using SPSS and
EXCEL. The course covers social scientific methods
to solve communication research problems empirically.
Topics include basic principles of social scientific
research, random sampling, questionnaire design,
experimental research design, focus group techniques,
content analysis, and basic descriptive and inferential
statistics. Students will also learn basic data
manipulation, presentation. and analysis techniques
using SPSS and EXCEL.
J. Niederdeppe.
Enrollment is
limited to:
sophomores.
3
CRP5450
Fall or Spring
3
Inferential
Statistics for
Planning and
Public Policy
This course is an introduction to the inferential
statistical methods and econometrics/regression
analysis needed to understand empirical public policy
and planning research and to do basic applied public
policy analysis. The statistical concepts are illustrated
using data and examples primarily from the fields of
public policy and planning.
N. Brooks.
I. Azis.
-
CRP6220
Spring
3
Planning
Policy and
Analysis
The course is designed to familiarize students with the
essence of planning models and equip them with
analytical tools to undertake a practical quantitative
policy and planning analysis. Two categories of models
to be discussed are: (1) economy-wide models that
capture complete interactions between economic and
social indicators such as income distribution and
poverty; and (2) non-Bayesian decision-making models
that combine intangibles and subjective judgments with
statistical data and other tangible actors, and that can
also capture feedback influences.
CRP6290
-
-
Quantitative
Methods
Analysis
Topics TBA.
CS6782
Fall
4
Probabilistic
Graphical
Models
see BTRY6790
Staff.
probability
theory (BTRY
3080 or
equivalent),
programming
and data
structures (CS
2110 or
equivalent); a
course in
statistical
methods is
recommended
but not required
(BTRY 4090 or
equivalent).
CSS6200
CSS6210
DSOC3140
Spring
Spring
Fall
3
Spatial
Modeling and
Analysis
Theory and practice of applying geo-spatial data for
resource inventory and analysis, biophysical process
modeling, and land surveys. Emphasizes use and
evaluation of spatial analytical methods applied to
agronomic and environmental systems and processes.
Laboratory section is used to process, analyze, and
visualize geo-spatial data of interest to the student,
ending in a comprehensive student project.
2
Applications
of Space–
Time
Statistics
Spatial
Thinking,
GIS, and
Related
Methods
(SBA)
(KCM)
4
D. G. Rossiter.
CSS 4110 or
CSS 4200, or
equivalent or
permission of
instructor.
Introduction to space-time statistics with applications
in agriculture and environmental management. Topics
include geostatistics, temporal statistics, sampling,
experimental design, state-space analysis, data mining,
and fuzzy logic.
H. Van Es.
BTRY 6010 or
equivalent.
Everything occurs in space. Knowing where
organizations are located and events occur in space
provides clues to understanding social order and
processes not revealed by traditional social analysis
techniques. At the same time, spatial thinking and
methods are becoming increasingly used in the social
sciences. The purpose of this course is to introduce the
undergraduate to both aspects of spatial patterns,
trends, and themes but also to methodologies for
bringing spatial considerations into their research. The
course provides a practical introduction to GIS via lab
assignments.
J. Francis.
S-U grades
only. Offered
alternate
years.
Letter grades
only.
DSOC5600
DSOC6190
Spring
Fall
4
4
Analytical
Mapping and
Spatial
Modeling
(also CRP
6290) (SBA)
The goal of this course is to introduce students in the
social sciences and related fields to geographic
information systems and spatial statistics as a set of
tools to complement traditional analysis methods.
Spatial relationships have become increasingly
recognized as important in socioeconomic, political
and demographic analysis. Recent research in these
fields have demonstrated that understanding spatial
relationships, in addition to other factors that account
for differences and similarities between people and
organizations, significantly increase our explanatory
power. The first part of the course focuses on various
features of GIS which are most useful to social
scientists in their endeavors. The second part of the
course introduces spatial statistics which further this
understanding as well as control for spatial
autocorrelation when it exists.
J. Francis.
Quantitative
Research
Methods
Graduate-level course in measurement and analysis of
survey, demographic, and observational data. Topics
include linear regression, analysis of variance, and
analysis of covariance with both continuous and
categorically coded variables. Introduces logistic
regression and some nonlinear models. Gives special
attention to handling ordered and unordered categorical
data as these are prevalent in social/demographic data
sets. Analyzes data from real surveys like the American
National Election Studies and the General Social
Surveys using programs like SAS and SPSS. Includes
labs and writing programs to analyze these data.
Students familiarize themselves with data cleaning,
missing data estimation, transformations, subsetting,
and other data handling procedures.
D. Gurak.
statistics course.
Letter grades
only.
DSOC7190
EAS4350
Spring
Fall
4
3
Advanced
Regression
and Spatial
Statistics
This course will cover two topics, logistic regression
and spatial linear regression. The course opens with a
brief review of multiple regression theory and
procedures. Then a little more than half the semester is
devoted to logistic regression modeling. Spatial linear
regression will be covered in five weeks of the
semester. As both of these techniques are based on
maximum likelihood procedures, some time will be
devoted to an overview of maximum likelihood
procedures.
Statistical
Methods in
Meteorology
and
Climatology
Statistical methods used in climatology, operational
weather forecasting, and selected meteorological
research applications. Statistical characteristics of
meteorological data, including probability
distributions, correlation structures and their
implications for statistical inference. Covers
operational forecasts derived from multiple regression
models, including the MOS system; and forecast
evaluation techniques.
J. Francis.
D. Wilks.
one introductory
course each in
statistics (e.g.,
AEM 2100) and
calculus.
Co-meets
with EAS
5350.
ECE3100
ECE4110
ECE5640
Fall, summer
Fall
Fall
4
Introduction
to Probability
and Inference
for Random
Signals and
Systems
Introduction to probabilistic techniques for modeling
random phenomena and making estimates, inferences,
predictions, and engineering decisions in the presence
of chance and uncertainty. Probability measures,
classical probability and combinatorics, countable and
uncountable sample spaces, random variables,
probability mass functions, probability density
functions, cumulative distribution functions, important
discrete and continuous distributions, functions of
random variables including moments, independence
and correlation, conditional probability, Total
Probability and Bayes’ rule with application to random
system response to random signals, characteristic
functions and sums of random variables, the
multivariate Normal distribution, maximum likelihood
and maximum a posteriori estimation, NeymanPearson and Bayesian statistical hypothesis testing,
Monte Carlo simulation. Applications in
communications, networking, circuit design, device
modeling, and computer engineering.
Staff.
MATH 2940,
PHYS 2213, or
equivalents.
4
Random
Signals in
Communicati
ons and
Signal
Processing
Introduction to models for random signals in discrete
and continuous time; Markov chains, Poisson process,
queuing processes, power spectral densities, Gaussian
random process. Response of linear systems to random
signals. Elements of estimation and inference as they
arise in communications and digital signal processing
systems.
Staff.
ECE 2200 and
ECE 3100 or
equivalent.
Statistical
Inference and
Decision
Graduate-level introduction to fundamentals of signal
detection and estimation with applications in
communications. Elements of decision theory.
Sufficient statistics. Signal detection in discrete and
continuous time. Multiuser detection. Parameter
estimations. Applications in wireless communications.
Staff.
ECE 3100, ECE
4110, or
permission of
instructor.
4
ECE5650
ECON3190
ECON3200
ECON3210
Fall
Fall
Spring
Fall, spring,
summer
4
4
4
4
Statistical
Signal
Processing
and Learning
This course introduces fundamental theories and
practical ideas in statistical signal processing and
learning. Specific topics include Bayesian inference,
Wiener and Kalman filters, predictions, graphical
models, point estimation theory, maximum likelihood
methods, moment methods, Cram´er-Rao bound, least
squares and recursive least squares, supervised and
unsupervised learning techniques.
Introduction
to Statistics
and
Probability
Provides an introduction to statistical inference and to
principles of probability. It includes descriptive
statistics, principles of probability, discrete and
continuous distributions, and hypothesis testing (of
sample means, proportions, variance). Regression
analysis and correlation are introduced.
Introduction
to
Econometrics
Introduction to the theory and application of
econometric techniques. How econometric models are
formulated, estimated, used to test hypotheses, and
used to forecast; understanding economists’ results in
studies using regression model, multiple regression
model, and introduction to simultaneous equation
models.
Applied
Econometrics
Provides an introduction to statistical methods and
principles of probability. Topics include analysis of
data, probability concepts and distributions, estimation
and hypothesis testing, regression, correlation and time
series analysis. Applications from economics are used
to illustrate the methods covered in the course.
Staff.
Staff.
Staff.
Staff.
ECE 3100
or ECE 3250
ECON 1110–
ECON 1120 and
MATH 1110–
MATH 1120.
Forbidden
Overlap:
Students who
take ECON
3190 may not
receive credit
for MATH
4710, MATH
4720, BTRY
3080/ILRST
3080/STSCI
3080, BTRY
4090/STSCI
4090.
ECON 1110–
ECON 1120,
ECON 3190, or
equivalent.
Forbidden
Overlap:
Students may
not receive
credit for
both ECON
3200 and
ECON 3210.
ECON 1110–
ECON 1120 and
calculus.
Forbidden
Overlap:
Students may
not receive
credit for
both ECON
3200 and
ECON 3210.
ECON6190
ECON6200
ECON7190
ECON7200
Fall
Spring
Fall
Spring
4
4
4
4
Econometrics
I
Gives the probabilistic and statistical background for
meaningful application of econometric techniques.
Topics include probability theory probability spaces,
random variables, distributions, moments,
transformations, conditional distributions, distribution
theory and the multivariate normal distribution,
convergence concepts, laws of large numbers, central
limit theorems, Monte Carlo simulation; statistics:
sample statistics, sufficiency, exponential families of
distributions. Further topics in statistics are considered
in ECON 6200.
Staff.
ECON 3190–
ECON 3200 or
permission of
instructor.
Econometrics
II
A continuation of ECON 6190 (Econometrics I)
covering statistics: estimation theory, least squares
methods, method of maximum likelihood, generalized
method of moments, theory of hypothesis testing,
asymptotic test theory, and nonnested hypothesis
testing; and econometrics: the general linear model,
generalized least squares, specification tests,
instrumental variables, dynamic regression models,
linear simultaneous equation models, nonlinear models,
and applications.
Staff.
ECON 6190.
Advanced
Topics in
Econometrics
I
Covers advanced topics in econometrics, such as
asymptotic estimation and test theory, robust
estimation, Bayesian inference, advanced topics in
time-series analysis, errors in variable and latent
variable models, qualitative and limited dependent
variables, aggregation, panel data, and duration
models.
Staff.
ECON 6190–
ECON 6200 or
permission of
instructor.
Advanced
Topics in
Econometrics
II
Covers advanced topics in econometrics, such as
asymptotic estimation and test theory, robust
estimation, Bayesian inference, advanced topics in
time-series analysis, errors in variable and latent
variable models, qualitative and limited dependent
variables, aggregation, panel data, and duration
models.
Staff.
ECON 6190–
ECON 6200 or
permission of
instructor.
EDUC5630
ENGRD2700
GOVT6019
Fall
Fall, spring,
summer
Fall
3
3
4
Using
Statistics to
Explore
Social Policy
Builds on students’ statistical knowledge to
collaboratively design and carry out studies using a
national dataset. Students combine their knowledge
with readings and guest speakers to better understand
the purposes and limitations of various methods. This
course is for students who struggle to use their
statistical knowledge in a practical and valuable way.
J. Sipple.
Basic
Engineering
Probability
and Statistics
Gives students a working knowledge of basic
probability and statistics and their application to
engineering. Includes computer analysis of data and
simulation. Topics include random variables,
probability distributions, expectation, estimation,
testing, experimental design, quality control, and
regression.
Staff.
Introductory,
Probability
and Applied
Statistics
The goal of this course is to introduce probability and
statistics as fundamental building blocks for
quantitative political analysis, with regression
modeling as a focal application. We will begin with a
brief survey of probability theory, types of
measurements, and descriptive statistics. The bulk of
the course then addresses inferential statistics, covering
in detail sampling, methods for estimating unknown
quantities, and methods for evaluating competing
hypotheses. We will see how to formally assess
estimators, and some basic principles that help to
ensure optimality. Along the way, we will introduce
the use of regression models to specify social scientific
hypotheses, and employ our expanding repertoire of
statistical concepts to understand and interpret
estimates based on our data. Weekly homework
assignments require students to deploy the methods
both ‘by hand’ so they can grasp the basic
mathematics, and by computer to meet the conceptual
demands of non-trivial examples and prepare for
independent research. Some time will be spent
reviewing algebra, calculus, and elementary logic, as
well as introducing computer statistical packages.
B. Corrigan
minimum one
and preferably
two statistics
courses (second
course may be
taken
concurrently) or
permission of
instructor.
MATH 1910 and
MATH 1920.
MATH 2940
should be
completed before
or concurrently
with ENGRD
2700.
GOVT6029
HADM2010
Spring
Fall, spring
4
3
Methods of
Political
Analysis II
This course builds upon 6019, covering in detail the
interpretation and estimation of multivariate linear
regression models. We derive the Ordinary Least
Squares estimator and its characteristics using matrix
algebra and determine the conditions under which it
achieves statistical optimality. We then consider the
circumstances in social scientific contexts which
commonly lead to assumption violations, and the
detection and implications of these problems. This
leads to modified regression estimators that can offer
limited forms of robustness in some of these cases.
Finally, we briefly introduce likelihood-based
techniques that incorporate assumptions about the
distribution of the response variable, focusing on
logistic regression for binary dependent variables.
Students are expected to produce a research paper built
around a quantitative analysis that is suitable for
presentation at a professional conference. Some time
will be spent reviewing matrix algebra, and discussing
ways to implement computations using statistical
software.
B. Corrigan
Hospitality
Quantitative
Analysis
This introductory statistics course is taught from the
perspective of solving problems and making decisions
within the hospitality industry. Students learn
introductory probability, as well as how to gather data,
evaluate the quality of data, graphically represent data,
and apply some fundamental statistical methodology.
Statistical methods covered include estimation and
hypothesis testing relating to one- and two-sample
problems of means, simple linear regression, and
multiple regression. Problems involving multiple
means (one-way ANOVA) are covered as a special
case of multiple regression, time allowing. Excel is
used as the statistical computing software.
R. Lloyd.
high school
algebra.
Required.
Letter grades
only.
HADM3010
HADM9980
HD2830
Fall, spring
Fall
Fall
3
3
3
Service
Operations
Management
Students are introduced to statistical and operations
research methods that are appropriate for the
hospitality industry. The goal of the course is to
provide students with the skills and understanding
necessary for making decisions using quantitative data.
Students use computer spreadsheet software
extensively. A key requirement of the course is an
ability to communicate the results of analyses in a clear
manner. Topics include probability, decision analysis,
modeling, forecasting, quality management, process
design, waiting lines, and project management.
C. Anderson, S.
Kimes, and G.
Thompson.
Letter grades
only.
Required.
Limited to 70
Hotel
students per
lecture.
Real
Research and
Fake Data
This course is a doctoral seminar about using
simulation to conduct research. The purpose of the
course is to provide students with the skills, ability, and
motivation to conduct research using computer
simulation. Students will learn how to conduct both
theoretical and methodological research using
simulation. The course will focus on the use of microanalytic simulation (and not agent-based modeling).
Students should be capable of writing and publishing a
paper using this research design and methodology upon
completion of the course
M. Sturman.
Elective.
Research
Methods in
Human
Development
This course will introduce students to the basics of
research design and will review several methodologies
in the study of human development. The focus of the
course will be on descriptive and experimental
methods. Students will learn the advantages and
challenges to different methodological approaches. The
course also places an emphasis on developing students’
scientific writing and strengthening their understanding
of statistics.
M. Casasola
Recommended
prerequsite: HD
1150.
Priority given
to HD
majors.
HD6130
ILRHR9630
Spring
Fall, spring.
3
Hierarchical
Linear
Modeling
This is a graduate seminar designed to provide students
with an introductory background in the basic principles
and applications of hierarchical linear modeling (HLM)
in developmental research. HLM is a class of models
that allows researchers to study a variety of phenomena
at different conceptual levels, including individual
outcomes nested within classrooms, schools, or other
groups (two-level models, and growth in outcomes
over time nested within individuals and within
classrooms, schools, or other groups (three-level
models).
3
Research
Methods in
HRM/Strategi
c Human
Resource
Management
Designed to build social science research skills,
particularly in the area of human resource studies
(HRS). Topics include measurement reliability,
construct validity, design of studies, external validity,
meta-analysis, critiquing/reviewing HRS research,
publishing HRS research, and applications of statistical
models of HRS issues.
A. Ong.
Staff.
Letter grades
only.
Ph.D.
Candidates.
ILRLE7400
ILRLE7410
Spring
Fall
4
4
Social and
Economic
Data
Teaches the basics required to acquire and transform
raw information into social and economic data.
Graduate materials emphasize methods for creating and
certifying laboratories in which data privacy and
confidentiality concerns can be controlled and audited.
Legal, statistical, computing, and social science aspects
of the data “manufacturing” process are treated. The
formal U.S., Eurostat, OECD, and UN statistical
infrastructure are covered as are major private data
sources. Topics include basic statistical principles of
populations and sampling frames; acquiring data via
samples, censuses, administrative records, and
transaction logging; the law, economics, and statistics
of data privacy and confidentiality protection; data
linking and integration techniques (probabilistic record
linking; multivariate statistical matching); analytic
methods in the social sciences. Graduate students are
assumed to be interested in applying these techniques
to original research in an area of specialization, and are
required to do individual projects. This class may be
taught to students at Cornell and other universities
whose emphasis is placed on U.S. Census Bureau
procedures.
Applied
Econometrics
I
Considers methods for the analysis of longitudinal
data, that is, data in which a set of individual units are
followed over time. Focuses on both estimation and
specification testing of these models. Students consider
how these statistical models are linked to underlying
theories in the social sciences. Course coverage
includes panel data methods (e.g., fixed, random,
mixed effects models), factor analysis, measurement
error models, and general moment structure methods.
J. Abowd.
G. Jakubson
graduate Ph.D.level sequence in
econometrics or
permission of
instructor.
ILRLE7420
ILRST2100
ILRST2110
Spring
Fall, spring,
summer
Fall, spring
Applied
Econometrics
II (also
ECON 7492)
Continues from ILRLE 7410 and covers statistical
methods for models in which the dependent variable is
not continuous. Covers models for dichotomous
response (including probit and logit); polychotomous
response (including ordered response and multinomial
logit); various types of censoring and truncation (e.g.,
the response variable is only observed when it is
greater than a threshold); and sample selection issues.
Includes an introduction to duration analysis. Covers
not only the statistical issues but also the links between
behavioral theories in the social sciences and the
specification of the statistical model.
4
Introductory
Statistics
Statistics is about understanding the world through
data. We are surrounded by data, so there is a lot to
understand. Covers data exploration and display, data
gathering methods, probability, and statistical inference
methods through contingency tables and linear
regression. The emphasis is on thinking scientifically,
understanding what is commonly done with data (and
doing some of it for yourself), and laying a foundation
for further study. Students learn to use statistical
software and simulation tools to discover fundamental
results. They use computers regularly; the test includes
both multimedia materials and a software package.
This course does not focus on data from any particular
discipline, but will use real-world examples from a
wide variety of disciplines and current events.
3
Statistical
Methods for
the Social
Sciences II
A second course in statistics that emphasizes
applications to the social sciences. Topics include
simple linear regression, multiple linear regression
(theory, model building, and model diagnostics), and
the analysis of variance. Computer packages are used
extensively.
4
G. Jakubson
ILRLE 7410 or
permission of
instructor.
L. Karns, P.
Velleman, and M.
Wells.
introductory
algebra.
T. Diciccio.
ILRST 2100 or
equivalent
introductory
statistics course.
Forbidden
Overlap:
Students may
receive credit
for only one
course in the
following
group: AEM
2100, ILRST
2100/STSCI
2100, MATH
1710, PAM
2100,
PSYCH
3500, SOC
3010.
ILRST2130
ILRST2150
Fall
Fall
3
4
ILRST2200
Fall
3
ILRST3080
Fall
4
ILRST3100
Fall
4
Regression
Methods
Overview
Builds on the introduction to statistics course by
considering multivariate regression methods.
Application of the methods is explored through the
analysis of data found by each student. Topics include:
regression inference, indicator variables, analysis of
outliers, interaction terms, interpretation, and
presentation. Analysis process and interpretation will
be emphasized rather than specific research results.
Students will present their final models in class.
L. Karns.
ILRST 2100 or
equivalent.
Limited to 20
students.
Statistical
Applications
in Law and
Policy
Covers the practical aspects of quantitative research in
law and policy (occupational and environmental health,
product liability, and employment discrimination).
Students evaluate the existing literature on a topic,
analyze statistical merits, and make quantitative
arguments. Standards of evidence will be considered.
Required weekly writing assignments, a preliminary
paper, and a final paper. Final oral presentations.
L. Karns.
ILRST 2100.
Sophomore
writing
course.
Occupational
Epidemiology
Occupational epidemiology is the investigation of
workplace health issues requiring knowledge of
medicine, organizational structures, industrial hygiene,
and human behavior. An introduction to occupational
epidemiology through exploration of research design
(cohort, case-control, and crosssectional), exposure
assessment, and statistical evaluation of the health
issue. Students will use odds ratios, relative risk, and
logistic regression models to measure the relationship
between exposure and outcome. All students will select
a topic area of interest, summarize current knowledge,
and develop a research design protocol for future
implementation.
L. Karns.
ILRST 2100 or
equivalent.
See STSCI3080.
Staff.
See STSCI3100.
J. Bunge.
Probability
Models and
Inference
Statistical
Sampling
two semesters of
statistics.
ILRST3110
ILRST3120
ILRST4100
ILRST4110
ILRST4140
Fall
Spring
Spring
Spring
Spring
4
4
4
4
4
Practical
Matrix
Algebra
Matrix algebra is a necessary tool for statistics courses
such as regression and multivariate analysis and for
other “research methods” courses in various other
disciplines. This course provides students in various
fields of knowledge with a basic understanding of
matrix algebra in a language they can easily
understand. Topics include special types of matrices,
matrix calculations, linear dependence and
independence, vector geometry, matrix reduction
(trace, determinant, norms), matrix inversion, linear
transformation, eigenvalues, matrix decompositions,
ellipsoids and distances, and some applications of
matrices.
J. Bunge.
Applied
Regression
Methods
Reviews matrix algebra necessary to analyze
regression models. Covers multiple linear regression,
analysis of variance, nonlinear regression, and linear
logistic regression models. For these models, least
squares and maximum likelihood estimation,
hypothesis testing, model selection, and diagnostic
procedures are considered. Illustrative examples are
taken from the social sciences. Computer packages are
used.
P. Velleman.
ILRST 2100 or
equivalent.
See STSCI4100.
Staff.
ILRST 3120,
STSCI 2200, or
equivalent; some
knowledge of
matrix-based
regression.
See STSCI4110.
T. Diciccio.
ILRST 3120,
STSCI 2200, or
equivalent.
Staff.
BTRY 6010 and
BTRY 6020 or
permission of
instructor.
Multivariate
Analysis
Statistical
Methods III:
Categorical
Data
Statistical
Methods IV:
Applied
Design
See STSCI4120.
ILRST4550
ILRST4950
ILRST4970
Spring
Fall, spring
Fall, spring
4
4
4
Applied Time
Series
Analysis
See STSCI4550.
Staff.
Honors
Program
Students are eligible for ILR senior honors program if
they (1) earn a minimum 3.700 cumulative gpa at end
of junior year; (2) propose an honors project, entailing
research leading to completion of a thesis, to an ILR
faculty member who agrees to act as thesis supervisor;
and (3) submit project, endorsed by proposed faculty
sponsor, to Committee on Academic Standards and
Scholarships. Accepted students embark on a twosemester sequence. The first semester consists of
determining a research design, familiarization with
germane scholarly literature, and preliminary data
collection. The second semester involves completion of
the data collection and preparation of the honors thesis.
At the end of the second semester, the candidate is
examined orally on the completed thesis by a
committee consisting of the thesis supervisor, a second
faculty member designated by the appropriate
department chair, and a representative of the Academic
Standards and Scholarship Committee.
Staff.
Field
Research
All requests for permission to register for an internship
must be approved by the faculty member who will
supervise the project and the chairman of the faculty
member’s academic department before submission for
approval by the director of off-campus credit programs.
Upon approval of the internship, the Office of Student
Services will register each student for 4970, for 4
credits graded A+ to F for individual research, and for
ILRST 4980 , for 8 credits graded S–U, for completion
of a professionally appropriate learning experience,
which is graded by the faculty sponsor.
Staff.
STSCI 3080,
STSCI 4030 (or
equivalent) or
permission of
instructor.
Letter grades
only.
ILRST4980
Fall, spring
8
Field
Research,
Internship
All requests for permission to register for an internship
must be approved by the faculty member who will
supervise the project and the chairman of the faculty
member’s academic department before submission for
approval by the director of off-campus credit programs.
Upon approval of the internship, the Office of Student
Services will register each student for ILRST 4970 , for
4 credits graded A+ to F for individual research, and
for 4980, for 8 credits graded S–U, for completion of a
professionally appropriate learning experience, which
is graded by the faculty sponsor.
Staff.
Staff.
Staff.
ILRST4990
Fall, spring
1-4
Directed
Studies
Students are eligible for ILR senior honors program if
they (1) earn a minimum 3.700 cumulative gpa at end
of junior year; (2) propose an honors project, entailing
research leading to completion of a thesis, to an ILR
faculty member who agrees to act as thesis supervisor;
and (3) submit project, endorsed by proposed faculty
sponsor, to Committee on Academic Standards and
Scholarships. Accepted students embark on a twosemester sequence. The first semester consists of
determining a research design, familiarization with
germane scholarly literature, and preliminary data
collection. The second semester involves completion of
the data collection and preparation of the honors thesis.
At the end of the second semester, the candidate is
examined orally on the completed thesis by a
committee consisting of the thesis supervisor, a second
faculty member designated by the appropriate
department chair, and a representative of the Academic
Standards and Scholarship Committee.
ILRST5080
Fall
4
Probability
Models and
Inference
See STSCI5080.
S-U grades
only.
ILRST5100
ILRST5110
ILRST5150
ILRST6100
Fall, spring
Fall, spring
Fall, spring
Fall
3
Statistical
Methods for
the Social
Sciences I
A first course in statistics for graduate students in the
social sciences. Descriptive statistics, probability and
sampling distributions, estimation, hypothesis testing,
simple linear regression, and correlation. Students are
instructed on the use of a statistics computer package at
the beginning of the term and use it for weekly
assignments.
T. DiCiccio.
3
Statistical
Methods for
the Social
Sciences II
Second course in statistics that emphasizes applications
to the social sciences. Topics include simple linear
regression, multiple linear regression (theory, model
building, and model diagnostics), and the analysis of
variance. Computer packages are used extensively.
T. DiCiccio.
Statistical
Research
Methods
Students learn basic skills for conducting qualitative
and survey research. They work through an
introductory review course at home on their own time.
After passing an exam, they attend a two-week
immersion course in Ithaca taught by the on-campus
faculty in July. Topics include an introduction to
surveys and discrete analysis, basic regression, and
integration of qualitative and quantitative research
methods.
Statistical
Methods I
Develops and uses statistical methods to analyze data
arising from a wide variety of applications. Topics
include descriptive statistics, point and interval
estimation, hypothesis testing, inference for a single
population, comparisons between two populations,
one-and two-way analysis of variance, comparisons
among population means, analysis of categorical data,
and correlation and regression analysis. Introduces
interactive computing through statistical software.
Emphasizes basic principles and criteria for selection
of statistical techniques.
4
4
Offered only
in New York
City for
M.P.S.
Program.
Staff.
Staff.
Permission of
instructor or
graduate
standing is
required.
ILRST6140
ILRST6190
Spring
Fall
3
3
ILRST7100
Spring
3
ILRST7990
Fall, spring
1-9
INFO2950
Spring
4
Structural
Equations
with Latent
Variables
Provides a comprehensive introduction to the general
structural equation system, commonly known as the
“LISREL model.” One purpose of the course is to
demonstrate the generality of this model. Rather than
treating path analysis, recursive and nonrecursive
models, classical econometrics, and confirmatory
factor analysis as distinct and unique, the instructor
treats them as special cases of a common model.
Another goal of the course is to emphasize the
application of these techniques.
Topics in
Social
Statistics
Special
Topics in
Social
Statistics
Directed
Studies
Mathematical
Methods for
Information
Science
J. Bunge.
ILRST 2100,
ILRST 5110,
ILRST 5100 or
equivalent.
The areas of study are determined each semester by the
instructor offering the seminar. Topics may include
hierarchical linear models, the multivariate normal and
Wishart distributions, multivariate sampling, tests of
mean and covariance, multivariate regression, principal
components, factor analysis, canonical correlation,
robustness, and bootstrap confidence regions and tests.
J. Bunge.
A second course
in (non-calculusbased) statistics
such as multiple
regression.
Areas of study are determined each semester by the
instructor offering the seminar.
M. Wells.
For individual research conducted under the direction
of a member of the faculty.
Staff.
Teaches basic mathematical methods for information
science. Topics include graph theory, discrete
probability, Bayesian methods, finite automata,
Markov models, and hidden Markov models. Uses
examples and applications from various areas of
information science such as the structure of the web,
genomics, natural language processing, and signal
processing.
Staff.
Graduate
students only.
MATH 2310 or
equivalent.
MATH1102
MATH1710
Fall
Fall, spring,
summer
1
4
Introduction
to Statistical
Methods
MATH 1102 is a preparatory course for finite
mathematics and applied introductory-level statistics
courses. The course introduces basic probability laws,
descriptive statistics, linear regression, and probability
distributions. The probability and statistics content of
the course is similar to 1/3 of the content covered in the
above-mentioned courses. In addition, MATH 1102
includes a variety of topics of algebra, with emphasis
on the development of linear, power, exponential and
logarithmic functions and their applications to curve
fitting.
Statistical
Theory and
Application
in the Real
World
(MQR)
Introductory statistics course discussing techniques for
analyzing data occurring in the real world and the
mathematical and philosophical justification for these
techniques. Topics include population and sample
distributions, central limit theorem, statistical theories
of point estimation, confidence intervals, testing
hypotheses, the linear model, and the least squares
estimator. The course concludes with a discussion of
tests and estimates for regression and analysis of
variance (if time permits). The computer is used to
demonstrate some aspects of the theory, such as
sampling distributions and the Central Limit Theorem.
In the lab portion of the course, students learn and use
computer-based methods for implementing the
statistical methodology presented in the lectures.
Staff.
Staff.
high school
mathematics. No
previous
familiarity with
computers
presumed.
Due to an
overlap in
content,
students will
forfeit credit
for MATH
1102 upon
completion of
MATH 1105
or an
introductory
statistics
course (AEM
2100, BTRY
3010, HADM
2010
(formerly
2201), ILRST
2100/STSCI
2100, MATH
1710, PAM
2100, or
PSYCH
3500).
No credit for
MATH 1710
if taken after
ECON 3190,
ECON 3200,
ECON 3210,
MATH 4720,
or any other
upper-level
course
focusing on
the statistical
sciences (e.g.,
those
counting
toward the
statistics
concentration
for the math
major).
MATH4410
MATH4420
Fall
Spring
4
4
Introduction
to
Combinatoric
s I (MQR)
Combinatorics is the study of discrete structures that
arise in a variety of areas, particularly in other areas of
mathematics, computer science, and many areas of
application. Central concerns are often to count objects
having a particular property (e.g., trees) or to prove that
certain structures exist (e.g., matchings of all vertices
in a graph). The first semester of this sequence covers
basic questions in graph theory, including extremal
graph theory (how large must a graph be before one is
guaranteed to have a certain subgraph) and Ramsey
theory (which shows that large objects are forced to
have structure). Variations on matching theory are
discussed, including theorems of Dilworth, Hall,
König, and Birkhoff, and an introduction to network
flow theory. Methods of enumeration
(inclusion/exclusion, Möbius inversion, and generating
functions) are introduced and applied to the problems
of counting permutations, partitions, and triangulations.
Introduction
to
Combinatoric
s II (MQR)
Continuation of MATH 4410, although formally
independent of the material covered there. The
emphasis here is the study of certain combinatorial
structures, such as Latin squares and combinatorial
designs (which are of use in statistical experimental
design), classical finite geometries and combinatorial
geometries (also known as matroids, which arise in
many areas from algebra and geometry through
discrete optimization theory). There is an introduction
to partially ordered sets and lattices, including general
Möbius inversion and its application, as well as the
Polya theory of counting in the presence of
symmetries.
Staff.
MATH 2210,
MATH 2230,
MATH 2310, or
MATH 2940.
Staff.
MATH 2210,
MATH 2230,
MATH 2310, or
MATH 2940.
MATH4710
MATH4720
MATH4740
Fall
Spring
Spring
4
4
4
Basic
Probability
(MQR)
Introduction to probability theory, which prepares the
student to take MATH 4720. The course begins with
basics: combinatorial probability, mean and variance,
independence, conditional probability, and Bayes
formula. Density and distribution functions and their
properties are introduced. The law of large numbers
and the central limit theorem are stated and their
implications for statistics are discussed.
Statistics
Statistics have proved to be an important research tool
in nearly all of the physical, biological, and social
sciences. This course serves as an introduction to
statistics for students who already have some
background in calculus, linear algebra, and probability
theory. Topics include parameter estimation,
hypothesis testing, and linear regression. The course
emphasizes both the mathematical theory of statistics
and techniques for data analysis that are useful in
solving scientific problems.
Stochastic
Processes
A one-semester introduction to stochastic processes
which develops the theory together with applications.
The course will always cover Markov chains in
discrete and continuous time and Poisson processes.
Depending upon the interests of the instructor and the
students, other topics may include queuing theory,
martingales, Brownian motion, and option pricing.
Staff.
one year of
calculus.
Recommended:
some knowledge
of multivariate
calculus.
Staff.
MATH 4710 and
knowledge of
linear algebra
(e.g., MATH
2210).
Recommended:
some knowledge
of multivariable
calculus.
Staff.
MATH 4710,
BTRY 3080,
ORIE 3500, or
ECON 3190 and
some knowledge
of matrices
(multiplication
and inverses).
This course may
be useful to
graduate students
in the biological
sciences or other
disciplines who
encounter
stochastic
models in their
work but who do
not have the
background for
more advanced
courses such as
ORIE 6500.
Forbidden
Overlap:
Students will
receive credit
for only one
course among
BTRY
3080/ILRST
3080/STSCI
3080, ECON
3190, MATH
4710.
Forbidden
Overlap:
Students will
receive credit
for only one
course among
BTRY
4090/STSCI
4090, ECON
3190, MATH
4720
MATH6410
MATH6710
MATH6720
MATH6740
Spring
Fall
Spring
Spring
4
4
4
4
Enumerative
Combinatoric
s
An introduction to enumerative combinatorics from an
algebraic, geometric and topological point of view.
Topics include, but are not limited to, permutation
statistics, partitions, generating functions, various types
of posets and lattices (distributive, geometric, and
Eulerian), Möbius inversion, face numbers, shellability,
and relations to the Stanley-Reisner ring.
Staff.
Probability
Theory I
A mathematically rigorous course in probability theory
which uses measure theory but begins with the basic
definitions of independence and expected value in that
context. Law of large numbers, Poisson and central
limit theorems, and random walks.
Staff.
knowledge of
Lebesgue
integration
theory, at least
on real line.
Students can
learn this
material by
taking parts of
MATH 4130–
MATH 4140 or
MATH 6210.
Probability
Theory II
Conditional expectation, martingales, Introduction to
Mathematical StatisticsBrownian motion. Other topics
such as Markov chains, ergodic theory, and stochastic
calculus depending on time and interests of the
instructor.
Staff.
MATH 6710
Introduction
to
Mathematical
Statistics
Topics include an introduction to the theory of point
estimation, hypothesis testing and confidence intervals,
consistency, efficiency, and the method of maximum
likelihood. Basic concepts of decision theory are
discussed; the key role of the sufficiency principle is
highlighted and applications are given for finding
Bayesian, minimax, and unbiased optimal decisions.
Modern computer-intensive methods like the bootstrap
receive some attention, as do simulation methods
involving Markov chains. The parallel development of
some concepts of machine learning is exemplified by
classification algorithms. An optional section may
include nonparametric curve estimation and elements
of large sample asymptotics.
Staff.
MATH 6710
(measure
theoretic
probability) and
ORIE 6700, or
permission of
instructor.
MATH7740
Fall
4
MATH7750
Fall
4
MATH7770
Fall
4
MATH7780
Spring
4
NCC5010
Fall
3
Statistical
Learning
Theory
The course aims to present the developing interface
between machine learning theory and statistics. Topics
are classification and pattern recognition, support
vector machines, neural networks, tree methods, and
boosting.
Staff.
Statistical
Theories
Applicable to
Genomics
Focuses on statistical concepts useful in genomics
(e.g., microarray data analysis) that involve a large
number of populations. Topics include multiple testing
and closed testing (the cornerstone of multiple testing),
family-wise error rate, false discovery rate (FDR) of
Benjamini and Hochberg, and Storey’s papers relating
to pFDR. Also discusses the shrinkage technique or the
Empirical Bayes approach, equivalent to the BLUP in a
random effect model, which is a powerful technique,
taking advantage of a large number of populations. A
related technique, which allows use of the same data to
select and make inferences for the selected populations
(or genes), is discussed. If time permits, there may be
some lectures about permutation tests, bootstrapping,
and QTL identification
Staff.
Stocastic
Processes
Stochastic
Processes
Statistics for
Management
basic
mathematical
statistics (MATH
6740 or
equivalent) and
measure
theoretic
probability
(MATH 6710)
Staff.
This course provides the foundations of probability and
statistics required for a manager to interpret large
quantities of data and to make informed decisions
under uncertainty. Topics covered include decision
trees, sampling, hypothesis testing, and multiple
regression.
Staff.
A. Farahat.
Limited
enrollment.
Johnson
School core
course.
NRE5180
NS6370
NTRES3130
Spring
Spring
Fall
Marketing
Models
This course is a study of model-based research in the
marketing literature. The course aims to accomplish
three main objectives: (1) develop student’s knowledge
of the technical details of various techniques for
analyzing data, (2) expose students to “hands-on” use
of various computer programs for carrying out
statistical data analyses, and (3) have students propose
a model of consumer/ market behavior that potentially
constitutes a contribution to the literature.
Topics in
Nutritional
Epidemiology
3 credits. Prerequisites: graduate standing; NS 6250. S–
U or letter grades. J. McDermid.Builds upon the
foundation of epidemiological concepts and methods in
NS6520 by focusing on current topics in nutritional
epidemiology including aspects of study design,
implementation, analyses and interpretation of
findings. Material covered through lectures and in-class
discussions.
4
Biological
Statistics I
In this course, students develop statistical methods and
apply them to problems encountered in the biological
and environmental sciences. Methods include data
visualization, population parameter estimation,
sampling, bootstrap resampling, hypothesis testing, the
Normal and other probability distributions, and an
introduction to modeling. Applied analysis is carried
out in the R statistical computing environment.
2
3
NTRES4120
Spring
4
Wildlife
Population
Analysis:
Techniques
and Models
NTRES4130
Spring
4
Biological
Statistics II
S. Gupta.
NS6520,
BTRY6010
P. Sullivan.
one semester of
calculus.
Explores the theory and application of a variety of
statistical estimation and modeling techniques used in
the study of wildlife population dynamics, with
primary focus on analysis of data from marked
individuals. Computer exercises are used to reinforce
concepts presented in lecture.
E. Cooch.
NTRES 3100 or
NTRES 4100 (or
equivalent or
permission of
instructor)
see BTRY3020
P. Sullivan.
NTRES 3130,
BTRY 3010, or
STSCI 2200.
Offered
alternate
years.
Enrollment
limited to:
graduate
students.
Letter grades
only.
NTRES
statistics
requirement.
NTRES6120
NTRES6200
NTRES6700
ORIE3120
ORIE3300
Spring
Spring
Spring
Spring
Fall, summer
NTRES 3100 or
NTRES 4100 (or
equivalent or
permission of
instructor),
college-level
math and
statistics course.
4
Wildlife
Population
Analysis:
Techniques
and Models
3
Spatial
Modeling and
Analysis
see CSS6200
D. G. Rossiter.
Spatial
Statistics
Develops and applies spatial statistical concepts and
techniques to ecological and natural resource issues.
Topics include visualizing spatial data and analysis and
modeling of geostatistical, lattice, and spatial point
processes. Applied analysis is carried out in the R
statistical computing environment. CSS 6200 may be
taken simultaneously.
P. J. Sullivan.
Industrial
Data and
Systems
Analysis
Database and statistical techniques for data mining,
graphical display, and predictive analysis in the context
of industrial systems (manufacturing and distribution).
Database techniques include structured query language
(SQL), procedural event-based programming (Visual
Basic), and geographical information systems.
Statistical techniques include multiple linear
regression, classification, logistic regression, and time
series forecasting. Industrial systems analysis includes
factory scheduling and simulation, materials planning,
cost estimation, inventory planning, and quality
engineering.
Staff.
ENGRD 2700.
Optimiztion I
Formulation of linear programming problems and
solutions by the simplex method. Related topics such
as sensitivity analysis, duality, and network
programming. Applications include such models as
resource allocation and production planning.
Introduction to interior-point methods for linear
programming
Staff.
Prerequisite:
grade of C– or
better in MATH
2210 or MATH
2940.
4
4
4
see NTRES4120
E. Cooch.
CSS 4100, CSS
4200, or
equivalent, or
permission of
instructor.
BTRY 6010 and
BTRY 6020.
Highly
recommended
prerequisite:
introductory GIS
course.
Letter grades
only.
Alternate
year course.
ORIE3310
ORIE3500
ORIE3510
ORIE4350
ORIE4520
Spring, Summer
Fall, summer
Spring, summer
Spring
Spring
Optimization
II
A variety of optimization methods stressing extensions
of linear programming and its applications but also
including topics drawn from integer programming,
dynamic programming, and network optimization.
Formulation and modeling are stressed as well as
numerous applications.
4
Engineering
Probability
and Statistics
II
A rigorous foundation in theory combined with the
methods for modeling, analyzing, and controlling
randomness in engineering problems. Probabilistic
ideas are used to construct models for engineering
problems, and statistical methods are used to test and
estimate parameters for these models. Specific topics
include random variables, probability distributions,
density functions, expectation and variance,
multidimensional random variables, and important
distributions including normal, Poisson, exponential,
hypothesis testing, confidence intervals, and point
estimation using maximum likelihood and the method
of moments.
4
Introductory
Engineering
Stochastic
Processes I
4
4
4
Staff.
Prerequisite:
grade of C– or
better in ORIE
3300 or
permission of
instructor.
Staff.
grade of C– or
better in ENGRD
2700 or
equivalent.
Uses basic concepts and techniques of random
processes to construct models for a variety of problems
of practical interest. Topics include the Poisson
process, Markov chains, renewal theory, models for
queuing, and reliability.
Staff.
grade of C– or
better in ORIE
3500 or
equivalent.
Introductory
to Game
Theory
Broad survey of the mathematical theory of games,
including such topics as two-person matrix and
bimatrix games; cooperative and noncooperative nperson games; and games in extensive, normal, and
characteristic function form. Economic market games.
Applications to weighted voting and cost allocation.
Staff.
ORIE3300
Introductory
Engineering
Stochastic
Processes II
Topics chosen from martingales, random walks, Levy
processes, Brownian motion, branching processes,
Markov-renewal processes, Markov processes, optimal
stopping, dynamic programming.
Staff.
ORIE 3510 or
equivalent
ORIE4580
ORIE4600
ORIE4630
ORIE4710
ORIE4711
ORIE4712
Fall
Fall
Fall
Spring
Spring
Spring
4
3
Simulation
Modeling and
Analysis
Introduction to Monte Carlo simulation and discreteevent simulation. Emphasizes tools and techniques
needed in practice. Random variate, vector, and
process generation modeling using a discrete-event
simulation language, input and output analysis,
modeling.
Staff.
ORIE 3500 (may
be taken
concurrently)
and CS
2110/ENGRD
2110.
Introduction
to Financial
Engineering
This is an introduction to the most important notions
and ideas in modern financial engineering, such as
arbitrage, pricing, derivatives, options, interest rate
models, risk measures, equivalent martingale measures,
complete and incomplete markets, etc. Most of the time
the course deals with discrete time models. This course
can serve as a preparation for a course on continuous
time financial models such as ORIE 5600.
Staff.
ORIE 3500 and
ORIE 3510.
Staff.
engineering math
through MATH
2940, ENGRD
2700 and ORIE
3500, and
knowldge of R
and multiple
linear regression
equivalent to
ORIE 3120. No
previous
knowledge of
finance required.
Staff.
ENGRD 2700
(Weeks 1-7)
ORIE 4710
(Weeks 8–14)
Alternates
with ORIE
4712.
ORIE 4710
(Weeks 8–14)
Alternates
with ORIE
4711.
3
Operations
Research
Tools for
Financial
Engineering
2
Applied
Linear
Statistical
Models
2
Experimental
Design
2
Regression
Introduction to the applications of OR techniques, e.g.,
probability, statistics, and optimization, to finance and
financial engineering. First reviews probability and
statistics and then surveys assets returns, ARIMA time
series models, portfolio selection, regression, CAPM,
option pricing, GARCH models, fixed-income
securities, resampling techniques, and behavioral
finance. Also covers the use of MATLAB, MINITAB,
and SAS for computation.
Topics include multiple linear regression, diagnostics,
model selection, inference, one and two factor analysis
of variance. Theory and applications both treated. Use
of MINITAB stressed.
Covers randomization, blocking, sample size
determination, factorial designs, 2^p full and fractional
factorials, response surfaces, Latin squares, split plots,
and Taguchi designs. Engineering applications.
Computing in MINITAB or SAS.
Covers nonlinear regression, advanced diagnostics for
multiple linear regression, collinearity, ridge
regression, logistic regression, nonparametric
estimation including spline and kernel methods, and
regression with correlated errors. Computing in
Staff.
Staff.
MINITAB or SAS.
ORIE4740
ORIE5500
ORIE5510
ORIE5520
ORIE5640
Spring
Fall
Spring
Spring
Spring
Staff.
ORIE 3500 and
MATH 2940 or
equivalent;
programming
experience.
Exposure to
multiple linear
regression and
logistic
regression
strongly
recommended.
4
Statistical
Data Mining I
Examines the statistical aspects of data mining, the
effective analysis of large datasets. Covers the process
of building and interpreting various statistical models
appropriate to such problems arising in scientific and
business applications. Topics include naïve Bayes,
graphical models, multiple regression, logistic
regression, clustering methods and principal
component analysis. Assignments are done using one
or more statistical computing packages.
4
Engineering
Probability
and Statistics
II
See ORIE 3500.
Staff.
ENGRD 2700
Lectures comeet with
ORIE 3500.
4
Operations
Research II:
Introduction
to Stochastic
Processes I
see ORIE 3510
Staff.
ORIE 5500
Lectures comeet with
ORIE 3510.
4
Introductory
Engineering
Stochastic
Processes II
see ORIE4520
Staff.
ORIE 3510
Statistics for
Financial
Engineering
Regression, ARIMA, GARCH, stochastic volatility,
and factor models. Calibration of financial engineering
models. Estimation of diffusion models. Estimation of
risk measures. Multivariate models and copulas.
Bayesian statistics. Students are instructed in the use of
R software; prior knowledge of R is helpful but not
required. This course is intended for M.Eng. students in
financial engineering and assumes some familiarity
with finance and financial engineering. Students not in
the financial engineering program are welcome if they
have a suitable background. Students with no
background in finance should consider taking ORIE
4630 instead.
Staff.
ORIE
3500/ORIE 5500
and at least one
of ORIE 4600,
ORIE 4630, or
ORIE 5600.
4
ORIE6127
ORIE6500
ORIE6510
ORIE6700
ORIE6710
Fall
Fall
Spring
Fall
Spring
Staff.
Pre- or
corequisites:
ORIE 6300,
ORIE 6500 and
ORIE 6700.
Staff.
one-semester
calculus-based
probability
course.
Probability
Covers sample spaces, events, sigma fields, probability
measures, set induction, independence, random
variables, expectation, review of important
distributions and transformation techniques,
convergence concepts, laws of large numbers and
asymptotic normality, and conditioning.
Staff.
real analysis at
level of MATH
4130; onesemester
calculus-based
probability
course.
Statistical
Principles
Topics include review of distribution theory of special
interest in statistics: normal, chi-square, binomial,
Poisson, t, and F; introduction to statistical decision
theory; sufficient statistics; theory of minimum
variance unbiased point estimation; maximum
likelihood and Bayes estimation; basic principles of
hypothesis testing, including Neyman-Pearson Lemma
and likelihood ratio principle; confidence interval
construction; and introduction to linear models.
Staff.
ORIE 6500 or
equivalent.
Intermediate
Applied
Statistics
Topics include statistical inference based on the
general linear model; least-squares estimators and their
optimality properties; likelihood ratio tests and
corresponding confidence regions; and simultaneous
inference. Applications in regression analysis and
ANOVA models. Covers variance components and
mixed models. Use of the computer as a tool for
statistics is stressed.
Staff.
ORIE 6700 or
equivalent.
3
Computationa
l Issues in
Large Scale
Data-Driven
Models
Introduces this emerging research area. Topics include
data-driven models in operation management,
asymptotic statistics, uniform convergence of empirical
process, and efficient computational methods.
4
Applied
Stochastic
Processes
Introduction to stochastic processes that presents the
basic theory together with a variety of applications.
Topics include Markov processes, renewal theory,
random walks, branching processes, Brownian motion,
stationary processes, martingales, and point processes.
4
4
3
ORIE6720
ORIE6780
PAM2100
PAM2101
PAM3100
Spring
Spring
Fall or spring
Fall
Spring
3
Sequential
Methods in
Statistics
Covers classical sequential hypothesis tests, Wald’s
SPRT, stopping rules, Kiefer-Weiss test, optimality,
group sequential methods, estimation, repeated
confidence intervals, stochastic curtailment, adaptive
designs, and Bayesian and decision theoretic
approaches.
3
Bayesian
Statistics and
Data Analysis
Priors, posteriors, Bayes estimators, Bayes factors,
credible regions, hierarchical models, computational
methods (especially MCMC), empirical Bayes
methods, Bayesian robustness.
Staff.
Introduction
to Statistics
Introduces students to descriptive and inferential
statistics. Topics include hypothesis testing, analysis of
variance, and multiple regression. To illustrate these
topics, this course examines applications of these
methods in studies of child and family policy.
J. Lewis
Statistics for
Policy
Analysis and
Management
Majors
The primary intent is to prepare students to
successfully complete PAM 3100 Multivariate
Regression. Topics include data presentation and
descriptive statistics, summation operator, properties of
linear functions, quadratic functions, logarithmic
functions, random variables and their probability
distributions, joint and conditional distributions,
expected value, conditional expectation, statistical
sampling and inference, interval estimation and
confidence intervals, hypothesis testing using t and F
distributions, and an introduction to bivariate
regression analysis. The course uses Excel initially to
become familiar with data analysis, and then moves on
to Stata (a powerful statistical analysis computer
program).
Multiple
Regression
Analysis
Introduces basic econometric principles and the use of
statistical procedures in empirical studies of economic
models. Discusses assumptions, properties, and
problems encountered in the use of multiple regression
procedures. Students are required to specify, estimate,
and report the results of an empirical model.
4
4
4
S–U grades
only.
Staff.
ORIE 6700 or an
equivalent
course in
mathematical
statistics.
T. Evans.
PAM majors
only or
permission of
instructor.
M. Lovenheim.
PAM 2100,
AEM
2100/ILRST
2100 or
equivalent.
Sec meets
once a week.
PAM5690
PAM6090
PLBR4092
PLRB4080
Fall
Fall
Spring
Spring
3
3
Regression
Analysis and
Managerial
Forecasting
Teaches various statistical methods for managerial
decision making, with a particular emphasis on
regression and forecasting. Other topics include
ANOVA, correlation, confounding, interaction, and
statistical process control. Emphasizes applications to
health care organizations.
Empirical
Strategies for
Policy
Analysis
Focuses on empirical strategies to identify the causal
effects of public policies and programs. The course
uses problem sets based on real-world examples and
data to examine techniques for analyzing
nonexperimental data including control function
approaches, matching methods, panel-data methods,
selection models, instrumental variables, and
regression-discontinuity methods. The emphasis
throughout, however, is on the critical role of research
design in facilitating credible causal inference. The
course aids students in both learning to implement a
variety of statistical tools using large data sets, and in
learning to select which tools are best suited to a given
research project.
C. Lucarelli.
at least one
statistics course.
J. Matsudaira.
graduate course
in econometrics.
(e.g., ILRLE
7480–ILRLE
7490 or AEM
7100)
1
Introduction
to Scripting
and Statistics
for Genetics
Data
Management
This course provides instruction and hands-on
experience with the statistical package ‘R’ as flexible
platform for data analysis, combined with an
introduction to perl scripting to manage, mine and
organize large datasets.
W. De Jong and
L. Mueller.
1
QTL
Analysis:
Mapping
Genotype to
Phenotype in
Practice
Discussion of mating designs and populations as well
as statistical models to identify genetic loci that affect
the phenotype and to predict breeding and genotypic
value using DNA polymorphisms. Practical application
to real datasets.
J. L. Jannink and
E. Buckler.
PLBR 4091,
PLBR 4092,
and PLBR
4093 may be
taken
individually
or in seqence
in one
semester.
BTRY 6010 or
permission of
instructor.
PSYCH3500
Fall, summer.
4
PSYCH6430
SOC2160
Spring
4
Statistics and
Research
Design
(MQR)
4 credits. Limited to 120 students. Staff. Acquaints the
student with the elements of statistical description (e.g.,
measures of average, variation, correlation) and, more
important, develops an understanding of statistical
inference. Emphasis is placed on those statistical
methods of principal relevance to psychology and
related behavioral sciences.
T. Cleland.
Statistics in
Current
Psychological
Research
-
Staff.
Health and
Society
(SBA-AS)
This course will examine how social factors shape
physical and mental health. First, we will review social
scientific research on the relationship between health
and status characteristics, neighborhood and residential
context, employment, social relationships and support,
religion, and health-related behaviors. We will devote
particular attention to the development of research
questions and methodological approaches in this work.
Next, we will directly examine the relationship
between health and social factors using data from a
nationally representative survey. Course instruction
will include statistical analysis of survey data and
social scientific writing. Students will develop their
own research exploring how social factors contribute to
health.
E. York Cornwell.
Forbidden
Overlap:
Students may
receive credit
for only one
course in the
following
group:
PSYCH
3500, AEM
2100, ILRST
2100/STSCI
2100, MATH
1710, PAM
2100, SOC
3010. Limited
to 120
students.
SOC3010
Fall
4
Evaluating
Statistical
Evidence
This course will introduce students to the theory and
mathematics of statistical analysis. Many decisions
made by ourselves and others around us are based on
statistics, yet few people have a solid grip on the
strengths and limitations of these techniques. This
course will provide a firm foundation for statistical
reasoning and logical inference using probability.
While there is math in this course, it is not a math class
per se, as a considerable amount of attention is devoted
to interpreting statistics as well as calculating them.
SOC6010
Fall
4
Evaluating
Statistical
Evidence
See SOC3010
M Brashears.
Linear
Models
This course provides an in-depth examination of linear
modeling. We begin with the basics of linear
regression, including estimation, statistical inference,
and model assumptions. We then review several tools
for diagnosing violations of statistical assumptions and
what to do when things go wrong, including dealing
with outliers, missing data, omitted variables, and
weights. Finally, we will explore extensions of the
linear regression model, including models for
categorical outcomes and hierarchical linear modeling.
While statistical modeling is the focus of the course,
we proceed with the assumption that models are only
as good as the theoretical and substantive knowledge
behind them. Thus, in covering the technical material,
we will spend considerable time discussing the link
between substantive knowledge and statistical practice.
The course is designed primarily for graduate students
in sociology.
S. Morgan.
SOC6020
Spring
4
M. Brashears.
Arts and
Sciences
students only.
Forbidden
Overlap:
Students may
receive credit
for only one
course in the
following
group: AEM
2100, ILRST
2100/STSCI
2100, MATH
1710, PAM
2100,
PSYCH
3500, SOC
3010
STSCI2100
Fall, spring
4
Introductory
Statistics
See ILRST2110.
Staff.
See NTRES3130.
Staff.
STSCI2110
Fall, spring
3
Statistical
Methods for
the Social
Sciences II
STSCI2200
Fall
4
Biological
Statistics I
STSCI3080
Fall
4
See ILRST2100.
Forbidden
Overlap:
Students may
receive credit
for only one
course in the
following
group: AEM
2100, ILRST
2100/STSCI
2100, MATH
1710, PAM
2100,
PSYCH
3500, SOC
3010.
Staff.
ILRST
2100/STSCI
2100 or
equivalent
introductory
statistics course.
one semester of
calculus.
Forbidden
Overlap:
Students may
receive credit
for only one
course in the
following
group: STSCI
3080/BTRY
3080, ECON
3190, MATH
4710.
Probability
Models and
Inference
This course provides an introduction to probability and
parametric inference. Topics include: random
variables, standard distributions, the law of large
numbers, the central limit theorem, likelihood-based
estimation, sampling distributions and hypothesis
testing, as well as an introduction to Bayesian methods.
Some assignments may involve computation using the
R programming language.
Staff.
Staff.
two semesters of
statistics.
Staff.
BTRY 3010 or
BTRY 6010.
STSCI3100
Fall
4
Statistical
Sampling
Theory and application of statistical sampling,
especially in regard to sample design, cost, estimation
of population quantities, and error estimation.
Assessment of nonsampling errors. Discussion of
applications to social and biological sciences and to
business problems. Includes an applied project.
STSCI3200
Spring
4
Biological
Statistics II
See BTRY3020.
Co-meets
with ILRST
5100.
STSCI3510
STSCI3520
STSCI4030
STSCI4090
Spring, summer
Spring
Fall
Spring
4
4
3
4
Intoductory
Engineering
Stochastic
Processes I
See ORIE3510.
Statistical
Computing
See BTRY3520.
Applied
Linear
Statistical
Models via
Matrices
Introduction to the general linear statistical model,
which includes regression, analysis of variance, and
their variations and extensions. The course uses the
matrix algebra representation of the model, which
provides greater analytical, statistical, and geometric
insight (and generalization) than the elementary
representation used in introductory courses. A wide
range of useful linear models will be studied, including
multiple regression, ANOVA, random-effects models,
etc. Prerequisites: a second non-calculus course in
statistics, preferably on multiple regression, and at least
one semester of basic matrix (linear) algebra.
Theory of
Statistics
See BTRY4090.
Staff.
grade of C- or
better in ORIE
3500 or
equivalent.
G. Hooker.
BTRY 3080,
enrollment in
MATH 2220 and
MATH 2240 or
equivalents.
Staff.
A second noncalculus course
in statistics,
preferably on
multiple
regression, and
at least one
semester of basic
matrix (linear)
algebra.
Staff.
BTRY 3080 or
equivalent and at
least one
introductory
statistics course.
Forbidden
Overlap:
Students may
receive credit
for only one
course in the
following
group: STSCI
4090/BTRY
4090, ECON
3190, MATH
4720.
STSCI4100
STSCI4110
Spring
Spring
4
Multivariate
Analysis
Discusses techniques of multivariate statistical analysis
techniques and illustrates them using examples from
various fields. Emphasizes applications and computer
packages, but theory is not ignored. Topics include
multivariate normal distribution, sample geometry and
multivariate distances, inference about a mean vector,
comparison of several multivariate means and
covariances; principal component analysis; factor
analysis; canonical correlation analysis; discriminant
analysis; and clustering.
4
Statistical
Methods III:
Categorical
Data
Categorical data analysis, including logistic regression,
log-linear models, stratified tables, matched pairs
analysis, polytomous response, and ordinal data.
Applications in biomedical and social sciences.
Staff.
ILRST 3120 ,
STSCI 2200, or
equivalent.
Applications of experimental design including split
plots, incomplete blocks, and fractional factorials.
Stresses use of the computer for both design and
analysis, with emphasis on solving real data problems.
Staff.
STSCI 3200 or
permission of
instructor.
R. Strawderman.
STSCI4120
Spring
4
Statistical
Methods IV:
Applied
Design
STSCI4270
Fall, spring
3
Introduction
to Survival
Analysis
See BTRY4270.
4
Databases
and Statistical
Computing
The intent of the course is to provide the statistician
with the computational tools for statistical research and
applications. Topics including random number
generation and Monte Carlo methods, regression
computations and application to statistical methods of
optimization, and sorting.
4
Applied Time
Series
Analysis
Introduces statistical tools for the analysis of timedependent data. Data analysis and application will be
an integral part of this course. Topics include linear,
nonlinear, seasonal, multivariate modeling, and
financial time series.
4
Data Mining
and Machine
Learning
Examines the statistical aspects of data mining, the
effective analysis of large datasets and the introduction
to machine learning algorithms and their applications.
Topics include classification, regression trees, neural
networks, boosting, and nearest neighbor techniques.
STSCI4500
STSCI4550
STSCI4740
Spring
Spring
Fall
Staff.
ILRST 3120 ,
STSCI 2200, or
equivalent; some
knowledge of
matrix-based
regression
analysis.
Staff.
Exposure to
multiple linear
regression and
logistic
regression
strongly
recommended.
D. Matteson.
STSCI 3080,
STSCI 4030 (or
equivalent) or
permission of
instructor.
Staff.
CS 1112 ,
MATH 2220 ,
STSCI 3200 ,
STSCI 4090.
Offered
alternate
years.
STSCI4940
STSCI5010
STSCI5060
Fall, spring
Fall
Spring
1-3
4
4
Undergraduat
e Special
Topics in
Statistics
Applied
Statistical
Analysis
Consists of a series of modules on various topics in
applied statistics. Some modules include guest lectures
from practitioners. Parallel with the course, students
complete a yearlong, in-depth data analysis project.
Topics include but are not limited to statistical
computing systems, statistical software packages, data
management, statistical graphics, and simulation
methods and algorithms.
Database
Management
and SAS
High
Performance
Computing
with DBMS
Using relational databases in statistical computing has
become more and more important. The knowledge and
skill of database management and the ability to
combine this knowledge and skill with statistical
analysis software tools, such as SAS, are a critical
qualification of a statistical analyst. In this course we
will study 1) the basics of modern relational database
management systems, including database analysis,
design and implementation, 2) database application in
advanced SAS programming and, 3) SAS high
performance computing using database-related
techniques.
Staff.
Permission of
Department is
required.
Staff.
Enrollment is
limited to:
students in
M.P.S. Program.
Two-semester
core course for
students in
master of
professional
studies (M.P.S.)
degree program
in applied
statistics in
Department of
Statistical
Science.
X. Yang.
Base SAS
programming
knowledge and
skills (STSCI
5010).
Permission of
instructor
required.
Enrollment
limited to:
students in the
MPS Program in
Applied
Statistics.
Letter grades
only.
STSCI5080
Fall
4
Probability
Models and
Inference
STSCI6000
Fall, spring
1
Statistics
Seminar
STSCI6520
STSCI6940
TAM3100
Spring
Fall, spring
Fall, summer
4
Computationa
lly Intensive
Statistical
Methods
1-3
Graduate
Special
Topics in
Statistics
3
Introduction
to Applied
Mathematics
I
This course provides an introduction to probability and
parametric inference. Topics include: random
variables, standard distributions, the law of large
numbers, the central limit theorem, likelihood-based
estimation, sampling distributions and hypothesis
testing, as well as an introduction to Bayesian methods.
Some assignments may involve computation using the
R programming language.
Modem applications in statistics often require intensive
computation and the use of modem statistical learning
techniques. This course covers topics in statistical
computing, induding numerical optimization and
finding zeros (likelihood and related techniques),
regressions, logistic regressions, neural neworks,
decision trees, boosting, bagging, dimension reductions
(including classical methods and new techniques) for
handling modem massive data sets (MMDS). Intensive
programming is done in MATLAB.
Covers initial value, boundary value, and eigenvalue
problems in linear ordinary differential equations. Also
covers special functions, linear partial differential
equations. This is an introduction to probability and
statistics. Use of computers to solve problems is
emphasized.
Staff.
Staff.
BTRY 4090 or
permission of
instructor.
Staff.
ORIE 6700 (or
equivalent) and
at least one
course in
probability, or
approval of
instructor.
Staff.
Permission of
department is
required.
Staff.
MATH 2930,
MATH 2940.
VTMED642
2
VTPMD6660
Spring
Fall
1
3
Clinical
Biostatistics
for Journal
Readers
Students become familiar with the statistical methods
commonly used in veterinary clinical articles, learn to
recognize obvious misuse of those methods, and
become able to interpret the statistical results.
Advanced
Methods in
Epidemiology
(Graduate)
Concepts introduced in VTPMD 6640 and VTPMD
6650 are developed further, with emphasis on
statistical methods. Topics include interaction, effect
modification, stratified analysis, matching and
multivariate (logistic regression) methods, survival
analysis, repeated measures, and strategies for the
analysis of epidemiologic data.
Letter grades
only.
Enrollment
limited to:
first-, second, third-, and
fourth-year
veterinary
students or
permission of
instructor.
Minimum
enrollment 3;
maximum 12.
H. N. Erb.
Y. T. Grohn.
VTPMD
6650/VETCS
6650 and BTRY
6020