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RIT is moving to semesters in the fall of 2013. Courses and much more will change. For details on CQAS semester courses, please click here.
Credit Course Listing
Many courses listed below may be taken for 3 or 4 credits.
The fourth credit is an independent-study based credit and
is designed solely for students in other programs. Parenthetical
notes indicate course prerequisites.
0307-711 Fundamentals of Statistics I
For those taking statistics for the first time. Topics include
organizing observed data for analysis, understanding of variability,
graphical methods, and summary statistics; simple, conditional,
and joint probabilities; combinations, permutations; binomial,
Poisson, and normal distributions; sampling distributions
and the Central Limit Theorem. This course does not count
as credit for either the CQAS advanced certificates or MS
degree. (None)
Credit 3 or 4
0307-712 Fundamentals of Statistics II
Continuation of 0307-711. Topics include estimation, confidence
intervals, and hypothesis testing; tests for independence
and analysis of categorical data; two-sample problems; designed
experiments with one or two factors; introduction to analysis
of variance, simple and multiple linear regression, and correlation.
This course does not count as credit for either the CQAS advanced
certificates or MS degree. (0307-711 or equivalent)
Credit 3 or 4
0307-714 Principles of Applied Statistics
Review of fundamental probability theory; review of key distributions
in statistics; probability plotting; linear combinations of
random variables; hypothesis testing; confidence intervals
and other statistical intervals; use of simulations; importance
of assumptions; multiple-comparisons; goodness-of-fit tests.
This course does not count as credit toward either the CQAS
advanced certificates or MS degree. (0307-712 or equivalent)
Credit 3
0307-721 Statistical Process Control
A practical course designed to provide in-depth understanding
of the principles and practices of statistical process control.
Topics include: statistical concepts relating to processes,
Shewhart charts for measurement and attribute data, CUSUM
charts, EWMA charts, measures of chart performance, tolerances,
specifications, process capability studies, short-run control
charts. (0307-712)
Credit 3 or 4
0307-731 Statistical Acceptance Control
How to apply modern process-oriented sampling plans to assess
performance of product and processes. Topics include: single,
double, multiple and sequential sampling plans, variables
sampling, techniques for sampling continuous production, skip-lot
plans, chain plans, AOQL schemes, AQL sampling systems and
recent contributions to literature. (0307-712)
Credit 3 or 4
0307-742 Statistical Computing
This course focuses on the programming language used in SAS
statistical software to read in raw data, create and manipulate
SAS data sets, and create SAS Macros. This course covers the
material required for "SAS Base Programmer" certification
and students seeking employment in statistical professions
are encouraged to go on after the course to seek professional
certification. Corresponding Minitab commands and macro programming
will also be covered. (0307-712 or equivalent or Consent of
Department)
Credit 3
0307-751 Mathematics for Statistics
This is a survey of the mathematical tools of some of the
more mathematically rigorous statistics courses of the MS
program. The topics include partial and higher-order differentiation,
various methods of integration, the gamma and beta functions,
and a brief overview of linear algebra, all in the context
of application to statistics. (The course assumes calculus
pre-requisites for the program have been met; it is not a
substitute for the program's calculus requirements.) (0307-712)
Credit 3
0307-770 Design of Experiments for Engineering and
Science
This course covers the fundamentals of the logical and economical
approach to the design and analysis of engineering, scientific,
and industrial experiments. It integrates the essential organizational
aspects of experimentation with proven statistical approaches.
Designs covered include the two-level factorial and fractional
factorial; response surface designs (CCD); blocking designs
when randomization is restricted; nested designs to uncover
sources of variation. The appropriate analysis methods complement
the designs. Simulation modeling and robust design show the
power and applicability of the information derived from the
designed experiments. This course is intended for non-CQAS
students. It does not count as credit for either the Advanced
Certificates or the MS degree in CQAS. (1016-314 or 1016-391
or 1016-351 or 0307-712 or equivalent)
Credit 4
0307-772 Applied Survey Design and Analysis
This course is an introduction to sample survey design with
emphasis on practical aspects of survey methodology. Topics
include: survey planning, sample design and selection, survey
instrument design, data collection methods, and analysis and
reporting. Application areas discussed will include program
evaluation, opinion polling, customer satisfaction, product
or service design, and evaluating marketing effectiveness.
Data collection methods to be discussed will include face-to-face,
mail, Internet and telephone. (0307-712).
Credit 3 or 4
0307-781 Quality Management
This course focuses on ASQ’s Certified Quality Manager
body of knowledge and introduces process improvement methodologies,
including the Six-Sigma framework. Topics include: quality
standards and awards, organization for quality, customer satisfaction,
continuous improvement, team management, quality costs, project
management, process improvement methodologies. (None)
Credit 3 or 4
0307-782 Quality Engineering
This course, in conjunction with 0307-781, covers the non-statistical
elements in ASQ’s Certified Quality Engineer body of
knowledge. Topics include: quality philosophies, elements
of a quality system, quality planning, supplier management,
quality auditing, quality and management tools, process and
material control, measurement systems, and safety and reliability.
(None)
Credit 3 or 4
0307-801 Design of Experiments I
Topics include completely randomized designs, randomized
complete block designs, Latin square designs, incomplete block
designs; general factorial designs, including fixed, random,
and mixed-effects models and expected mean squares; nested
designs; split-plot designs. (0307-712 or equivalent)
Credit 3 or 4
0307-802 Design of Experiments II
How to design and analyze experiments, with an emphasis on
applications in engineering and the physical sciences. Topics
include the role of statistics in scientific experimentation;
general principles of design, including randomization, replication,
and blocking; replicated and unreplicated two-level factorial
designs; two-level fractional-factorial designs; response
surface designs; evolutionary operation. (0307-801)
Credits: 3 or 4
0307-803 Design and Analysis of Experiments III
A continuation of the DOE sequence, covering more advanced,
but applied, topics and providing a strong foundation for
handling complex and non-standard situations. Topics include:
design and analysis of general, complete balanced designs,
including continued study of variance components, mixed models,
split-plot, and arbitrarily complex "no-name" designs;
restricted and unrestricted forms of the model; design and
analysis of general unreplicated designs; optimal designs
for non-standard situations, using D-optimality and related
criteria. (0307-801 or 818, 0307-802 or 717, 0307-841)
Credit 3
0307-821 Theory of Statistics I
This course introduces the student to the fundamental principles
of statistical theory while laying the groundwork for study
in the course sequel and future reading. Topics include: classical
probability, probability mass/density functions, mathematical
expectation (including moment-generating functions), special
discrete and continuous distributions, and distributions of
functions of random variables. (1016-253 and 0307-712)
Credit 3
0307-822 Theory of Statistics II
Building on foundations laid in the first course, this second
course in statistical theory answers some of the "How?"
and "Why?" questions of statistics. Topics include
the sampling distributions and the theory and application
of point and interval estimation and hypothesis testing. (0307-821)
Credit 3
0307-824 Probability Models
An introduction to stochastic processes, this course is intended
to encourage a greater appreciation of statistical theory,
while at the same time more fully enabling students to read,
understand, and even contribute to statistical journals. Topics
include: Poisson processes and their relationship to uniform,
exponential, gamma, and beta distributions; the basics of
queuing theory; and discrete-time Markov chains. Characteristic
functions and using Taylor series to approximate the mean
and variance of functions of one or more random variables
are among miscellaneous topics. (0307-821)
Credit 3
0307-830 Multivariate-Analysis Theory
Multivariate data are characterized by multiple responses.
This course concentrates on the mathematical and statistical
theory that underlies the analysis of multivariate data. Some
important applied methods are covered. Topics include matrix
algebra, the multivariate normal model, multivariate t-tests,
repeated measures, MANOVA and principal components. (0307-822,
and any of 0307-712/362, 1016-314/319/352. 0307-801 is useful.)
Credit 3
0307-831 Multivariate-Analysis Applications
This course includes some theory but concentrates on the applications
of multivariate analysis methods. The course relies heavily
on the use of computer software. Topics include: principal
components, factor analysis, MANOVA, canonical correlation,
discriminant analysis, clustering analysis and multidimensional
scaling. (basic matrix algebra; 0307-712 or equivalent. 0307-830
is useful but not required.)
Credit 3 or 4
0307-834 Multivariate Statistics for Imaging Science
This course introduces multivariate statistical techniques
and shows how they are applied in the field of Imaging Science.
The emphasis is on practical applications, and all topics
will include case studies from imaging science. Topics include
the multivariate Gaussian distribution, principal components
analysis, singular value decomposition, orthogonal subspace
projection, cluster analysis, canonical correlation and canonical
correlation regression, regression, multivariate noise whitening,
least squares energy minimization, and signal-to-noise optimization
with generalized eigenvector (matched filter). This course
is intended for students from the Imaging Science Department.
It does not count as credit for either the Advanced Certificates
or the MS dergree in CQAS. (basic matrix algebra; 0307-712
or equivalent; 0307-841 or equivalent is recommeded.)
Credit 4
0307-841 Regression Analysis I
A course that studies how a response variable is related to
a set of predictor variables. Regression techniques provide
a foundation for the analysis of observational data and provides
insight into the analysis of data from designed experiments.
Topics include: happenstance data versus designed experiments;
simple linear regression; the matrix approach to simple and
multiple linear regression; analysis of residuals; transformations;
weighted least squares. (0307-712. 0307-801 is useful but
not required.)
Credit 3 or 4
0307-842 Regression Analysis II
A continuation of 0307-841. Topics include dummy variables,
orthogonal polynomials, selection of best linear models, regression
applied to analysis of variance problems, the geometry of
least squares, ridge regression, generalized linear models,
nonlinear estimation, and model building. (0307-841)
Credit 3 or 4
0307-846 Principles of Data Mining I
This course is designed to give the student the foundational tools to
help discover and navigate the increasingly popular field of
statistical data mining. We provide a gentle yet thorough
introduction to supervised learning with topics such as multiple
linear and nonlinear regression, pattern recognition using techniques
such as logistic regression and support vector machines. We also
cover unsupervised learning, featuring cluster analysis, feature
selection, dimensionality reduction and latent variable models. The
course culminates with modern techniques of model selection and model
aggregation. (0307-702, or 714 and 841, or permission of instructor)
Credit 3 or 4
0307-851 Nonparametric Statistics
The emphasis of this course is how to analyze certain designs
when the normality assumption can not be made, with an emphasis
on applications. This includes certain analyses of ranked
data and ordinal data. The course provides a review of hypothesis
testing and confidence-interval construction. Topics include:
sign and Wilcoxon signed-rank tests, Mann-Whitney and Friedman
tests, runs tests, chi-square tests, rank correlation, rank
order tests; and Kolmogorov-Smirnov statistics. (0307-801)
Credit 3
0307-856 Interpretation of Data
How to use statistics in troubleshooting processes and interpreting
data. Topics include: coordination of use of statistical measures,
employing control charts in data analysis, outlier tests,
analysis of small-sample data, narrow-limit gauging, analysis
of means for variables and attributes data, identification
of assignable causes. (0307-802)
Credit 3
0307-862 Reliability Statistics I
A methods course in statistical aspects of reliability. Topics
include: applications of normal, log-normal, exponential,
and Weibull models to reliability problems; censored data;
probability and hazard plotting; series systems and multiple-failure
modes; maximum likelihood estimation; introduction to accelerated-life
models and analysis. (1016-282 or equivalent, 0307-801, 0307-841.
0307-822 is strongly recommended as a prerequisite or co-requisite)
Credit 3
0307-872 Survey Sampling and Estimation
This course focuses on sample size determination and parameter
estimation
in complex sample surveys such as those conducted by the Bureau
of Labor
Statistics. Topics include: random, systematic, stratified,
cluster, and
multi-stage sampling; and statistical techniques such as ratio,
difference
and regression estimators. (0307-822).
Credit 3
0307-873 Time Series Analysis and Forecasting
The course develops statistical methods in modeling and forecasting
of time series data with emphasis on model identification,
model fitting, and diagnostic checking. Topics include: survey
of forecasting methods, regression methods, moving averages,
exponential smoothing, seasonality, analysis of forecast errors,
Box-Jenkins models, transfer function models. (0307-841)
Credit 3 or 4
0307-883 Quality Engineering by Design
This course introduces the Taguchi approach to off-line quality
control including loss function, signal-to-noise metrics,
parameter design and tolerance design. These methods are aimed
at producing improved products and processes at lower costs.
Full attention is given to the more controversial aspects
of Taguchi methods and alternatives to these methods that
follow better statistical protocol are presented. (0307-802)
Credit 3
0307-884 Categorical Data Analysis
The course develops statistical methods for modeling and analysis of
data for which the response variable is categorical. Topics include:
contingency tables, matched pair analysis, Fisher's exact test,
logistic regression, analysis of odds ratios, log linear models,
multi-categorical logit models, ordinal and paired response analysis.
(0307-841)
Credit 3 or 4
0307-886 Sample Size Determination
This course presents procedures to determine the proper sample
size needed for the most commonly applied statistical methods.
Topics include confidence intervals and hypothesis tests for
the parameters of applied distributions and approximations
to distributions. Sample size determination for designed experiments
is covered extensively. (0307-802)
Credit 3
0307-889 Independent Study Project
Credit will be assigned at the discretion of the candidate’s
advisor, and will depend on the character and involvement
of the project. A written proposal setting forth the character
and procedures involved will be required of the candidate,
and may be modified at the discretion of the candidate’s
advisor before approval is given to proceed.
Credit 1, 2, 3, 6, or 9
0307-891 Special Topics in Applied Statistics
This course number provides for the presentation of subject
matter of important specialized value in the field of applied
statistics not offered as a regular part of the statistics
program. (Consent of the Department)
Credit 3
0307-895 Statistics Seminar
This pass/fail course provides for one or more quarters of
independent study and research activity. This course is required
each quarter for all full-time funded students in the MS program.
Credit 0
0307-894 Capstone
For students working toward the MS degree, this course is
designed to ensure that students can integrate the knowledge
from their other courses to solve more complex statistical
problems. (Consent of Department Chair)
Credit 3
0307-896 Thesis
For students working for the MS degree who are writing a research
thesis. (Consent of the Department)
Credit 3, 6, or 9
0307-899 Individual Achievement Project
Research project under faculty supervision for students working
for the MS degree. (Consent of the Department)
Credit 1-9
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