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## Courses Offered

### STAT-251 Probability and Statistics for Engineers I

Statistics in engineering; enumerative and analytic studies; descriptive statistics and statistical control; sample spaces and events; axioms of probability; counting techniques; conditional probability and independence; distributions of discrete and continuous random variables; joint distributions; central limit theorem.

Prerequisite(s): COS-MATH 173 or COS-MATH 182 or COS-MATH-182A or equivalent

Class 3, Credit 3

### STAT-252 Probability and Statistics for Engineers II

Point estimation; hypothesis testing and confidence intervals; one- and two-sample inference; introduction to analysis of variance, experimental design, and non-parametric methods.

Prerequisite(s): STAT-251

Class 3, Credit 3

### STAT-325 Design of Experiments for Biomedical Engineers

Topics covered include: observational versus experimental studies, fundamentals of good design, including randomization, replication, blocking, and blinding; one-factor designs: completely randomized, randomized complete block, and Latin-Square designs; fixed and random effects; analysis of residuals; two-factor and factorial designs; repeated measures designs; two-level factorial and fractional factorial designs. Lectures and assignments incorporate real-world examples and critiques of studies in the literature.

Prerequisite(s): STAT-252

Class 3, Credit 3

### STAT-511 Statistical Software

This course is an introduction to two statistical-software packages, SAS and R, which are often used in professional practice. Some comparisons with other statistical-software packages will also be made. Topics include: data structures; reading and writing data; data manipulation, subsetting, reshaping, sorting, and merging; conditional execution and looping; built-in functions; creation of new functions or macros; graphics; matrices and arrays; simulations; select statistical applications.

Prerequisite(s): STAT-205, MATH-252, STAT-252, or permission of instructor

Class 3, Credit 3

### STAT-611 Statistical Software

This course is an introduction to two statistical-software packages, SAS and R, which are often used in professional practice. Some comparisons with other statistical-software packages will also be made. Topics include: data structures; reading and writing data; data manipulation, subsetting, reshaping, sorting, and merging; conditional execution and looping; built-in functions; creation of new functions or macros; graphics; matrices and arrays; simulations; select statistical applications.

Prerequisite(s): STAT-205, MATH-252, STAT-252, or permission of instructor

Class 3, Credit 3

### STAT-614 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.

Prerequisite(s): MATH-173 or MATH-182 or equivalent course

Class 2, Credit 2

### STAT-621 Statistical Quality Control

A practical course designed to provide in-depth understanding of the principles and practices of statistical process control, process capability, and acceptance sampling. Topics include: statistical concepts relating to processes, Shewhart charts for attribute and variables data, CUSUM charts, EWMA charts, process capability studies, attribute and variables acceptance sampling techniques.

Prerequisite(s): STAT-145, STAT-205,MATH-252, STAT-252, graduate standing, or permission of instructor.

Class 3, Credit 3

### STAT-670 Designing Experiments for Process Improvement

Course Description: 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. This course does not count as credit toward the CQAS MS degree.

Prerequisite(s): STAT-145, STAT-205,  MATH-252, STAT-252, or permission of instructor.

Class 3, Credit 3

### STAT-672 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 and service design, and evaluating marketing effectiveness. Data collection methods to be discussed will include face-to-face, mail, Internet and telephone.

Prerequisite(s): STAT-145, STAT-205,  MATH-252, STAT-252, or permission of instructor.

Class 3, Credit 3

### CQAS-682 Lean Six Sigma Fundamentals

This course presents the philosophy and tools that will enable participants to develop quality strategies and drive process improvements that are linked to and integrated with business plans. The principles of Lean Six Sigma are presented, making the course a prerequisite for Lean Six Sigma Black Belt certification.

Prerequisite(s): Graduate standing or permission of instructor.

Class 3, Credit 3

### CQAS-683 Lean Six Sigma Project

Students in this course will work on a process improvement opportunity at an organization utilizing the DMAIC (Define, Measure, Analyze, Improve, and Control) approach to problem solving as well as the Lean Six Sigma tools. This course does not count as credit toward the CQAS MS degree.

Prerequisite(s): CQAS-682

Class 3, Credit 3

Prerequisite(s): Department approval

Credit 0

### STAT-701 Foundations of Experimental Design

This course is an introduction to experimental design with emphases on both foundational and practical aspects. Topics include the role of statistics in scientific experimentation, completely randomized designs, randomized complete block designs, Latin square designs, incomplete block designs, nested designs, general factorial designs, split-plot designs, two-level factorial and fractional factorial designs, and response-surface methodology.

Prerequisite(s): STAT-205, MATH-252, STAT-252, or permission of instructor and STAT-741 or STAT-305 or permission of instructor.

Corequisite(s): STAT 511 or STAT-611 or permission of instructor

Class 3, Credit 3

### STAT-720 Math for Statistics

This is a survey of mathematical tools for 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.

Prerequisite(s): MATH-173 or MATH-182 or MATH-B.A. or equivalent course

Class 2, Lab 0, Credit 2

### STAT-731 Fundamentals of Statistical Theory

This course introduces the students to the fundamental principles of modern graduate level statistical theory with a strong emphasis on conceptual aspects of estimation theory and statistical inference along with an exploration of the modern computational techniques needed in the application/implementation of the methods covered.  Topics include fundamentals of probability theory for statistics, random variable with a focus on the understanding and use of probability distribution function (both probability density function and cumulative distribution function), quantiles of a distribution, understanding and use of the mathematical expectation operator, special discrete and continuous distributions, and distributions of functions of random variables and their use in statistical modeling, sums of random variables as used in statistics, point estimation, limit theorems, properties of estimators (bias, variance, mean squared error, consistency, efficiency, sufficiency),  bias variance trade-off, interval estimation, hypothesis testing, bootstrap approach to estimation and inference, and elements of computational statistics.

Prerequisites(s): Graduate standing in APPSTAT-MS, SMPPI-ACT, or MATH 182/182A/173 and either MATH 252 or STAT 252 or STAT 614

Class 3, Credit 3

### STAT-741 Regression Analysis

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 provide 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, polynomial models, influence diagnostics, dummy variables, selection of best linear models, nonlinear estimation, and model building.

Prerequisite(s): STAT-205, MATH-252, STAT-252, graduate standing, or permission of instructor

Corequisite(s): STAT-511 or STAT-611 is recommended

Class 3, Credit 3

### STAT-745 Predictive Analytics

This course is designed to provide the student with solid practical skills in implementing basic statistical and machine learning techniques for the purpose of predictive analytics. Throughout the course, many real world case studies are used to motivate and explain the strengths and appropriateness of each method of interest. In those case studies, students will learn how to apply data cleaning, visualization, and other exploratory data analysis tools to a variety of real world complex data. Students will gain experience with reproducibility and documentation of computational projects and with developing basic data products for predictive analytics. The following techniques will be implemented and then tested with cross-validation: regularization in linear models, regression and smoothing splines, k-nearest neighbor, and tree-based methods, including random forest.

Prerequisite(s): STAT-611 or STAT-611, STAT-741.

Class 3, Credit 3

### STAT-747 Principles of Statistical Data Mining I

This course covers topics such as clustering, classification and regression trees, multiple linear regression under various conditions, logistic regression, PCA and kernel PCA, model-based clustering via mixture of gaussians, spectral clustering, text mining, neural networks, support vector machines, multidimensional scaling, variable selection, model selection, k-means clustering, k-nearest neighbors classifiers, statistical tools for modern machine learning and data mining, naïve Bayes classifiers, variance reduction methods (bagging) and ensemble methods for predictive optimality.

Prerequisite(s): STAT-511 or STAT-611, STAT-722, and STAT-741 or permission of instructor.

Class 3, Credit 3

### STAT-753 Nonparametric Statistics & Bootstrapping

The emphasis of this course is how to make valid statistical inference in situations when the typical parametric assumptions no longer hold, with an emphasis on applications. This includes certain analyses based on rank and/or ordinal data and resampling (bootstrapping) techniques. The course provides a review of hypothesis testing and confidence-interval construction. Topics based on ranks or ordinal data include: sign and Wilcoxon signed-rank tests, Mann-Whitney and Friedman tests, runs tests, chi-square tests, rank correlation, rank order tests, Kolmogorov-Smirnov statistics. Topics based on bootstrapping include: estimating bias and variability, confidence interval methods and tests of hypothesis.

Prerequisite(s): STAT-205, MATH-252, STAT-252, graduate standing, or permission of instructor

Class 3, Credit 3

### STAT-756 Multivariate Analysis

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, principal components, factor analysis, clustering, and discriminant analysis.

Prerequisite(s): Math-241, STAT-721, STAT-511 or STAT-611, or permission of instructor.

Class 3, Credit 3

### STAT-758 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 experimental design and analysis, 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.

This course is not intended for STAT students unless they have particular interest in imaging science. STAT students should be taking the course STAT-756-Multivariate Analysis.

Prerequisites: IMGS-211 or graduate standing in the APPSTAT-MS or STATQL-ACT or SMPPI-ACT program. Or graduate standing in IMGS-MS or IMGS-PhD or CLRS-MS or CLRS-PhD.

Class 3, Credit 3

### STAT-762 SAS Database Programming

This course focuses on the SAS programming language to read data, create and manipulate SAS data sets using Structured Query Language (SQL), creating SAS macros, and SAS programming efficiency. This course covers the material required for the “SAS Base Programming” and “SAS Advanced Programming” certification exams.

Prerequisite(s): STAT-511 or STAT-611

Class 3, Credit 3

### STAT-773 Time Series Analysis and Forecasting

This course is designed to provide the student with a solid practical hands-on introduction to the fundamentals of time series analysis and forecasting. Topics include stationarity, filtering, differencing, time series decomposition , time series regression, exponential smoothing, and Box-Jenkins techniques. Within each of these we will discuss seasonal and nonseasonal models. Many real-world examples will be covered and demonstrated using modern statistical software.

Prerequisite(s): STAT-741

Class 3, Credit 3

### STAT-775 Design and Analysis of Clinical Trials

This is a graduate level survey course that stresses the concepts of statistical design and analysis for clinical trials. Topics include the design, implementation, and analysis of trials, including treatment allocation and randomization, factorial designs, cross-over designs, sample size and power, reporting and publishing, etc. SAS for Windows statistical software will be used throughout the course for data analysis.

Prerequisites(s): STAT-205, MATH-252, STAT-252, graduate standing, or permission of instructor)

Class 3, Credit 3

### STAT-784 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.

Prerequisite(s): STAT-741

Class 3, Credit 3

### STAT-786 Advanced Programming in R

This course is a continuation of the R programming language that was begun in CQAS-611. Topics include: more on function writing; ggplot2 graphics; changing text to commands or functions; handling larger data sets, efficiency considerations; simulations; select statistical applications.

Prerequisite(s): STAT-611 and one of the following: MATH-252, STAT-252, STAT-722.

Class 1, Lab 0, Credit 1

### STAT-789 Special Topics

This course number provides for the presentation of subject matter of specialized value in the field of applied statistics not offered as a regular part of the program.

Prerequisite(s): Department approval

Credit 1-3

### STAT-790 Thesis

For students working for the MS degree who are writing a research thesis.

Prerequisite(s): Department approval

Credit 1-6

### STAT-792 Capstone

This course is designed to provide a capstone experience for MS students at the end of the graduate studies, and will require a synthesis of knowledge obtained from earlier coursework.

Prerequisite(s): STAT-611, STAT-701, STAT-722, STAT-741.

Class 3, Credit 3

This course provides for one or more semesters of study and research activity. This course is required for all first-year full-time funded students in the MS program

Prerequisite(s): Department approval

Credit 0

### STAT-799 Independent Study

Credit will be assigned at the discretion of the department. A written proposal of the work involve will be required of the candidate, and may be modified at the discretion of the faculty involved before approval is given to proceed.

Prerequisite(s): Department approval

Credit 1-3

Rochester Institute of Technology
One Lomb Memorial Drive,
Rochester, NY 14623-5603