COURSE SEMESTER CREDITS COURSE DESCRIPTION PROFESSOR PRE ... · model assumption evaluation, the...

COURSE

NUMBER SEMESTER CREDITS

COURSE

NAME DESCRIPTION PROFESSOR

PRE-

REQUISITE NOTES

AEM2100 Spring 4 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.

AEM4100 Fall 3 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.

AEM4110 Fall 3

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.

AEM4160 Spring 3 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.

AEM4170 Fall 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.

Enrollment is

limited to:

juniors or

seniors

(priority

given to

AEM

majors). No F

lec in weeks

labs are held.

AEM6120 Fall 1 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.

AEM7100 Spring 3 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

AEM7110 Fall 3 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.

AEM7120 Fall 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.

some formal

training in matrix

algebra.

ASTRO6523 Spring 4

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 non-

electromagnetic 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.

BEE4600 Fall 3

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.

BIOMG4870 Fall 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

BIOMG6300 Spring 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.

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.

BTRY3020 Spring 4 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, single-

factor 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.

BTRY3080 Fall 4

Probability

Models and

Inference

See STSCI3080. M. T. Wells.

BTRY3100 Fall 4 Statistical

Sampling See STSCI3100. Staff.

two semesters of

statistics.

BTRY3520 Spring 4 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.

G. Hooker.

BTRY 3080,

enrollment in

MATH 2220 and

MATH 2240 or

equivalents.

BTRY4030 Fall 3

Applied

Linear

Statistical

Models via

Matrices

See STSCI 4030. J. G. Booth.

a second non-

calculus course

in statistics,

preferably on

multiple

regression, and

at least one

semester of basic

matrix (linear)

algebra.

BTRY4090 Spring 4 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.

Staff.

BTRY 3080 or

equivalent and at

least one

introductory

statistics course.

BTRY4100 Spring 4 Multivariate

Analysis See STSCI 4100. Staff.

ILRST 3120,

STSCI 2200, or

equivalent; some

knowledge of

matrix-based

regression

analysis.

BTRY4140 Spring 4

[Statistical

Methods IV:

Applied

Design]

See STSCI 4120. Staff.

STSCI 3200 or

permission of

instructor.

BTRY4270 Fall, spring 3

Introduction

to Survival

Analysis

Develops and uses statistical methods appropriate for

analyzing right-censored (i.e., incomplete) time-to-

event 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.


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.

A. Keinan.

MATH 1110 or

equivalent. At

least one

previous course

in statistical

methods and at

least one

involving

programming, or

permission of

instructor.

Co-meets

with BTRY

6820.

BTRY4830 Spring 4

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.

Staff.

BTRY 3080 and

introductory

statistics or

equivalent.

BTRY4840 Fall 4 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.

BTRY4940 Fall, spring 1-3

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.


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 must

register using

independent

study form

(available in

140 Roberts

Hall).


Undergraduat

e Supervised

Teaching

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.

Students must

register using

independent

study form

(available in

140 Roberts

Hall).

BTRY4990 Fall, spring 1-3 Undergraduat

e Research Staff.

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).

BTRY5080 Fall 4

Probability

Models and

Inference

See STSCI 5080. M. T. Wells.

BTRY6010 Fall 4 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.

Staff.

Permission of

instructor or

graduate

standing is

required.


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.

BTRY6030 Spring 4

Statistical

Methods III:

Categorical

Data

See STSCI4110. Staff.

ILRST 3120,

STSCI 2200, or

equivalent.

BTRY6070 Fall 4

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 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.

BTRY 6010 and

BTRY 6020 or

permission of

instructor.

BTRY6520 Spring 4

Computationa

lly Intensive

Statistical

Methods


ORIE 6700 (or

equivalent) and

at least one

course in

probability, or

approval of

instructor.

Offered

alternate

years

BTRY6700 Fall 4

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.

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.

BTRY6790 Fall 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.

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).


Genomics See BTRY4820. A. Keinan.

MATH 1110 or

equivalent. At

least one

previous course

in statistical

methods and at

least one

involving

programming, or

permission of

instructor.

Co-meets

with BTRY

4820.

BTRY6830 Spring 4

Quantitative

Genomics

and Genetics

See BTRY4830. Staff.

BTRY 3080 and

introductory

statistics course

or equivalent.

BTRY6840 Fall 4 Computationa

l Genomics See BTRY4840. Staff.

BTRY 3080 and

at least one

previous course

in statistical

methods and at

least one in

algorithms.

BTRY6890 Fall, spring 1

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.

(May be

repeated for

credit)


Graduate

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.


Individual

Graduate

Study in

Biometry and

Statistics

Individual tutorial study selected by the faculty.

Because topics usually change from year to year, this

course may be repeated for credit.

Staff.

BTRY7170 Fall 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

Staff.

BTRY 4090,

BTRY 6020, or

equivalents.

BTRY7180 Fall 3

Generalized

Linear

Models

A theoretical development of generalized linear models

and related topics including generalized estimating

equations, and generalized linear mixed models.

G. Hooker.

BTRY 6020,

BTRY 4090, or

equivalent.

Primarily for

Ph.D.

students in

statistics.

BTRY7200 Spring 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.

Staff.

BTRY

4840/BTRY

6840 or

permission of

instructor.

BTRY7210 Fall 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.

Staff.

BTRY

4830/BTRY

6830 or

permission of

instructor.

BTRY7270 Spring 3

Advanced

Survival

Analysis

Focuses on the rigorous development of nonparametric,

semiparametric, and parametric modeling and

statistical inference procedures appropriate for

analyzing right censored data.

Staff.

at least one

graduate-level

course in

probability,

mathematical

statistics, and

regression

modeling.


Graduate-

Level

Dissertation

Research

Research at the Ph.D. level. Staff.

Permission of

graduate field

member

concerned is

required.

Enrollment is

limited to:

Ph.D.

candidates.

BTRY7950 Fall, spring 2-3 Statistical

Consulting

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.

Staff.

Prerequisite or

co-requisite:

BTRY 6020 and

BTRY 4090.

Permission of

instructor is

required.


Graduate

Supervised

Teaching

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.

Staff.

at least two

advanced

courses in

statistics and

biometry.

Permission of

instructor and

chair of special

committee is

required.


Master’s-

Level Thesis

Research

Research at the M.S. level. Staff.

Permission of

graduate field

member

concerned is

required.

Enrollment is

limited to:

M.S.

candidates.


Doctoral-

Level

Dissertation

Research

Staff.

CEE3040 Fall 4

Uncertainty

Analysis in

Engineering

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

CEE5290 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.

CEE7710 Fall 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

COMM2820 Fall 3

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.

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.

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.

I. Azis.

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 Spring 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.

D. G. Rossiter.

CSS 4110 or

CSS 4200, or

equivalent or

permission of

instructor.

CSS6210 Spring 2

Applications

of Space–

Time

Statistics

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.

S-U grades

only. Offered

alternate

years.

DSOC3140 Fall 4

Spatial

Thinking,

GIS, and

Related

Methods

(SBA)

(KCM)

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.

Letter grades

only.

DSOC5600 Spring 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.

DSOC6190 Fall 4

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 Spring 4

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.

J. Francis.

EAS4350 Fall 3

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.

D. Wilks.

one introductory

course each in

statistics (e.g.,

AEM 2100) and

calculus.

Co-meets

with EAS

5350.

ECE3100 Fall, summer 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, Neyman-

Pearson 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.

ECE4110 Fall 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.

ECE5640 Fall 4

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.

ECE5650 Fall 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.

Staff. ECE 3100

or ECE 3250

ECON3190 Fall 4

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.

Staff.

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.

ECON3200 Spring 4

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.

Staff.

ECON 1110–

ECON 1120,

ECON 3190, or

equivalent.

Forbidden

Overlap:

Students may

not receive

credit for

both ECON

3200 and

ECON 3210.

ECON3210 Fall, spring,

summer 4

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.

ECON 1110–

ECON 1120 and

calculus.

Forbidden

Overlap:

Students may

not receive

credit for

both ECON

3200 and

ECON 3210.

ECON6190 Fall 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.

ECON6200 Spring 4 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.

ECON7190 Fall 4

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.

ECON7200 Spring 4

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 Fall 3

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.

minimum one

and preferably

two statistics

courses (second

course may be

taken

concurrently) or

permission of

instructor.

ENGRD2700 Fall, spring,

summer 3

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.

MATH 1910 and

MATH 1920.

MATH 2940

should be

completed before

or concurrently

with ENGRD

2700.

GOVT6019 Fall 4

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

GOVT6029 Spring 4

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

HADM2010 Fall, spring 3

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 Fall, spring 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.

HADM9980 Fall 3

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 micro-

analytic 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.

HD2830 Fall 3

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 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).

A. Ong.

Letter grades

only.

ILRHR9630 Fall, spring. 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.

Staff. Ph.D.

Candidates.

ILRLE7400 Spring 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.

J. Abowd.

ILRLE7410 Fall 4

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.

G. Jakubson

graduate Ph.D.-

level sequence in

econometrics or

permission of

instructor.

ILRLE7420 Spring 4

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.

G. Jakubson

ILRLE 7410 or

permission of

instructor.

ILRST2100 Fall, spring,

summer 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.

L. Karns, P.

Velleman, and M.

Wells.

introductory

algebra.

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.

ILRST2110 Fall, spring 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.

T. Diciccio.

ILRST 2100 or

equivalent

introductory

statistics course.

ILRST2130 Fall 3

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.

ILRST2150 Fall 4

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.

ILRST2200 Fall 3 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.

ILRST3080 Fall 4

Probability

Models and

Inference


ILRST3100 Fall 4 Statistical

Sampling See STSCI3100. J. Bunge.

two semesters of

statistics.

ILRST3110 Fall 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.

ILRST3120 Spring 4

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.

ILRST4100 Spring 4 Multivariate

Analysis See STSCI4100. Staff.

ILRST 3120,

STSCI 2200, or

equivalent; some

knowledge of

matrix-based

regression.

ILRST4110 Spring 4

Statistical

Methods III:

Categorical

Data

See STSCI4110. T. Diciccio.

ILRST 3120,

STSCI 2200, or

equivalent.

ILRST4140 Spring 4

Statistical

Methods IV:

Applied

Design


BTRY 6010 and

BTRY 6020 or

permission of

instructor.

ILRST4550 Spring 4

Applied Time

Series

Analysis


STSCI 3080,

STSCI 4030 (or

equivalent) or

permission of

instructor.

ILRST4950 Fall, spring 4 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 two-

semester 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.

ILRST4970 Fall, spring 4 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.

Letter grades

only.


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.

S-U grades

only.

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 two-

semester 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.

ILRST5080 Fall 4

Probability

Models and

Inference



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.


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.

Staff.

Offered only

in New York

City for

M.P.S.

Program.

ILRST6100 Fall 4 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.

Staff.

Permission of

instructor or

graduate

standing is

required.

ILRST6140 Spring 3

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.

J. Bunge.

ILRST 2100,

ILRST 5110,

ILRST 5100 or

equivalent.

ILRST6190 Fall 3

Topics in

Social

Statistics

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-calculus-

based) statistics

such as multiple

regression.

ILRST7100 Spring 3

Special

Topics in

Social

Statistics

Areas of study are determined each semester by the

instructor offering the seminar. M. Wells.

Graduate

students only.

ILRST7990 Fall, spring 1-9 Directed

Studies

For individual research conducted under the direction

of a member of the faculty. Staff.

INFO2950 Spring 4

Mathematical

Methods for

Information

Science

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. MATH 2310 or

equivalent.

MATH1102 Fall 1

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.

Staff.

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).

MATH1710 Fall, spring,

summer 4

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.

high school

mathematics. No

previous

familiarity with

computers

presumed.

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 Fall 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.

Staff.

MATH 2210,

MATH 2230,

MATH 2310, or

MATH 2940.

MATH4420 Spring 4

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.

MATH4710 Fall 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.

Staff.

one year of

calculus.

Recommended:

some knowledge

of multivariate

calculus.

Forbidden

Overlap:

Students will

receive credit

for only one

course among

BTRY

3080/ILRST

3080/STSCI

3080, ECON

3190, MATH

4710.

MATH4720 Spring 4 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.

Staff.

MATH 4710 and

knowledge of

linear algebra

(e.g., MATH

2210).

Recommended:

some knowledge

of multivariable

calculus.

Forbidden

Overlap:

Students will

receive credit

for only one

course among

BTRY

4090/STSCI

4090, ECON

3190, MATH

4720

MATH4740 Spring 4 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.

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.

MATH6410 Spring 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.

MATH6710 Fall 4 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.

MATH6720 Spring 4 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

MATH6740 Spring 4

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

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.

basic

mathematical

statistics (MATH

6740 or

equivalent) and

measure

theoretic

probability

(MATH 6710)

MATH7750 Fall 4

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.

MATH7770 Fall 4 Stocastic

Processes Staff.

MATH7780 Spring 4 Stochastic

Processes - Staff.

NCC5010 Fall 3 Statistics for

Management

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.

A. Farahat.

Limited

enrollment.

Johnson

School core

course.

NRE5180 Spring 2 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.

S. Gupta.

NS6370 Spring 3

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.

NS6520,

BTRY6010

Offered

alternate

years.

Enrollment

limited to:

graduate

students.

NTRES3130 Fall 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.

P. Sullivan. one semester of

calculus.

NTRES4120 Spring 4

Wildlife

Population

Analysis:

Techniques

and Models

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)

Letter grades

only.

NTRES

statistics

requirement.

NTRES4130 Spring 4 Biological

Statistics II see BTRY3020 P. Sullivan.

NTRES 3130,

BTRY 3010, or

STSCI 2200.

NTRES6120 Spring 4

Wildlife

Population

Analysis:

Techniques

and Models

see NTRES4120 E. Cooch.

NTRES 3100 or

NTRES 4100 (or

equivalent or

permission of

instructor),

college-level

math and

statistics course.

Letter grades

only.

NTRES6200 Spring 3

Spatial

Modeling and

Analysis

see CSS6200 D. G. Rossiter.

CSS 4100, CSS

4200, or

equivalent, or

permission of

instructor.

NTRES6700 Spring 4 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.

BTRY 6010 and

BTRY 6020.

Highly

recommended

prerequisite:

introductory GIS

course.

Alternate

year course.

ORIE3120 Spring 4

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.

ORIE3300 Fall, summer 4 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.

ORIE3310 Spring, Summer 4 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.

Staff.

Prerequisite:

grade of C– or

better in ORIE

3300 or

permission of

instructor.

ORIE3500 Fall, summer 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.

Staff.

grade of C– or

better in ENGRD

2700 or

equivalent.

ORIE3510 Spring, summer 4

Introductory

Engineering

Stochastic

Processes I

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.

ORIE4350 Spring 4

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 n-

person games; and games in extensive, normal, and

characteristic function form. Economic market games.

Applications to weighted voting and cost allocation.

Staff. ORIE3300

ORIE4520 Spring 4

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 Fall 4

Simulation

Modeling and

Analysis

Introduction to Monte Carlo simulation and discrete-

event 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.

ORIE4600 Fall 3

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.

ORIE4630 Fall 3

Operations

Research

Tools for

Financial

Engineering

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.

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.

ORIE4710 Spring 2

Applied

Linear

Statistical

Models

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.

Staff. ENGRD 2700 (Weeks 1-7)

ORIE4711 Spring 2 Experimental

Design

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.

Staff. ORIE 4710

(Weeks 8–14)

Alternates

with ORIE

4712.

ORIE4712 Spring 2 Regression

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. ORIE 4710

(Weeks 8–14)

Alternates

with ORIE

4711.

MINITAB or SAS.

ORIE4740 Spring 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.

Staff.

ORIE 3500 and

MATH 2940 or

equivalent;

programming

experience.

Exposure to

multiple linear

regression and

logistic

regression

strongly

recommended.

ORIE5500 Fall 4

Engineering

Probability

and Statistics

II

See ORIE 3500. Staff. ENGRD 2700

Lectures co-

meet with

ORIE 3500.

ORIE5510 Spring 4

Operations

Research II:

Introduction

to Stochastic

Processes I

see ORIE 3510 Staff. ORIE 5500

Lectures co-

meet with

ORIE 3510.

ORIE5520 Spring 4

Introductory

Engineering

Stochastic

Processes II

see ORIE4520 Staff. ORIE 3510

ORIE5640 Spring 4

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.

ORIE6127 Fall 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.

Staff.

Pre- or

corequisites:

ORIE 6300,

ORIE 6500 and

ORIE 6700.

ORIE6500 Fall 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.

Staff.

one-semester

calculus-based

probability

course.

ORIE6510 Spring 4 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; one-

semester

calculus-based

probability

course.

ORIE6700 Fall 4 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.

ORIE6710 Spring 3

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.

ORIE6720 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.

Staff.

S–U grades

only.

ORIE6780 Spring 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.

ORIE 6700 or an

equivalent

course in

mathematical

statistics.

PAM2100 Fall or spring 4 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

PAM2101 Fall 4

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).

T. Evans.

PAM majors

only or

permission of

instructor.

PAM3100 Spring 4

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.

M. Lovenheim.

PAM 2100,

AEM

2100/ILRST

2100 or

equivalent.

Sec meets

once a week.

PAM5690 Fall 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.

C. Lucarelli. at least one

statistics course.

PAM6090 Fall 3

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.

J. Matsudaira.

graduate course

in econometrics.

(e.g., ILRLE

7480–ILRLE

7490 or AEM

7100)

PLBR4092 Spring 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.

PLBR 4091,

PLBR 4092,

and PLBR

4093 may be

taken

individually

or in seqence

in one

semester.

PLRB4080 Spring 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.

BTRY 6010 or

permission of

instructor.

PSYCH3500 Fall, summer. 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.

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.

PSYCH6430

Statistics in

Current

Psychological

Research

- Staff.

SOC2160 Spring 4

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.

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.

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

SOC6010 Fall 4

Evaluating

Statistical

Evidence

See SOC3010 M Brashears.

SOC6020 Spring 4 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.

STSCI2100 Fall, spring 4 Introductory

Statistics See ILRST2100. Staff.

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.

STSCI2110 Fall, spring 3

Statistical

Methods for

the Social

Sciences II

See ILRST2110. Staff.

ILRST

2100/STSCI

2100 or

equivalent

introductory

statistics course.

Co-meets

with ILRST

5100.

STSCI2200 Fall 4 Biological

Statistics I See NTRES3130. Staff.

one semester of

calculus.

STSCI3080 Fall 4

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.

Forbidden

Overlap:

Students may

receive credit

for only one

course in the

following

group: STSCI

3080/BTRY

3080, ECON

3190, MATH

4710.

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.

Staff. two semesters of

statistics.

STSCI3200 Spring 4 Biological

Statistics II See BTRY3020. Staff.

BTRY 3010 or

BTRY 6010.

STSCI3510 Spring, summer 4

Intoductory

Engineering

Stochastic

Processes I

See ORIE3510. Staff.

grade of C- or

better in ORIE

3500 or

equivalent.

STSCI3520 Spring 4 Statistical

Computing See BTRY3520. G. Hooker.

BTRY 3080,

enrollment in

MATH 2220 and

MATH 2240 or

equivalents.

STSCI4030 Fall 3

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.

Staff.

A second non-

calculus course

in statistics,

preferably on

multiple

regression, and

at least one

semester of basic

matrix (linear)

algebra.

STSCI4090 Spring 4 Theory of

Statistics See BTRY4090. 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 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.

Staff.

ILRST 3120 ,

STSCI 2200, or

equivalent; some

knowledge of

matrix-based

regression

analysis.

STSCI4110 Spring 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.

Offered

alternate

years.

STSCI4120 Spring 4

Statistical

Methods IV:

Applied

Design

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.

STSCI4270 Fall, spring 3

Introduction

to Survival

Analysis

See BTRY4270. R. Strawderman.

STSCI4500 Spring 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.

Staff.

Exposure to

multiple linear

regression and

logistic

regression

strongly

recommended.

STSCI4550 Spring 4

Applied Time

Series

Analysis

Introduces statistical tools for the analysis of time-

dependent data. Data analysis and application will be

an integral part of this course. Topics include linear,

nonlinear, seasonal, multivariate modeling, and

financial time series.

D. Matteson.

STSCI 3080,

STSCI 4030 (or

equivalent) or

permission of

instructor.

STSCI4740 Fall 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.

Staff.

CS 1112 ,

MATH 2220 ,

STSCI 3200 ,

STSCI 4090.

STSCI4940 Fall, spring 1-3

Undergraduat

e Special

Topics in

Statistics

Staff.

Permission of

Department is

required.

STSCI5010 Fall 4

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.

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.

Letter grades

only.

STSCI5060 Spring 4

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.

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.

STSCI5080 Fall 4

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.

STSCI6000 Fall, spring 1 Statistics

Seminar Staff.

BTRY 4090 or

permission of

instructor.

STSCI6520 Spring 4

Computationa

lly Intensive

Statistical

Methods

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.

Staff.

ORIE 6700 (or

equivalent) and

at least one

course in

probability, or

approval of

instructor.

STSCI6940 Fall, spring 1-3

Graduate

Special

Topics in

Statistics

Staff.

Permission of

department is

required.

TAM3100 Fall, summer 3

Introduction

to Applied

Mathematics

I

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. MATH 2930,

MATH 2940.

VTMED642

2 Spring 1

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.

H. N. Erb.

Letter grades

only.

Enrollment

limited to:

first-, second-

, third-, and

fourth-year

veterinary

students or

permission of

instructor.

Minimum

enrollment 3;

maximum 12.

VTPMD6660 Fall 3

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.

Y. T. Grohn.

VTPMD

6650/VETCS

6650 and BTRY

6020

COURSE SEMESTER CREDITS COURSE DESCRIPTION PROFESSOR PRE ... · model assumption evaluation, the...

Documents

Transcript of COURSE SEMESTER CREDITS COURSE DESCRIPTION PROFESSOR PRE ... · model assumption evaluation, the...