ROCHESTER INSTITUTE OF TECHNOLOGY COURSE OUTLINE FORM COLLEGE OF SCIENCE. School of Mathematical Sciences

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! ROCHESTER INSTITUTE OF TECHNOLOGY COURSE OUTLINE FORM COLLEGE OF SCIENCE School of Mathematical Sciences New Revised COURSE: COS-STAT-425 Multivariate Analysis 1.

9-20-11 Optional Course Designations: Yes No General Education Writing Intensive Honors Approval Request Date Approval Grant Date 2.

1 ! ROCHESTER INSTITUTE OF TECHNOLOGY COURSE OUTLINE FORM COLLEGE OF SCIENCE School of Mathematical Sciences New Revised COURSE: COS-STAT-425 Multivariate Analysis 1.0 Course Designations and Approvals: Required Course Approvals: Approval Approval Request Date Grant Date Academic Unit Curriculum Committee College Curriculum Committee Optional Course Designations: Yes No General Education Writing Intensive Honors Approval Request Date Approval Grant Date 2.0 Course information: Course Title: Multivariate Analysis Credit Hours: 3 Prerequisite(s): COS-STAT-305 Co-requisite(s): None Course proposed by: School of Mathematical Sciences Effective date: Fall 2013 Contact Hours Maximum Students/section Classroom 3 35 Lab Workshop Other (specify) 2.1 Course Conversion Designation: (Please check which applies to this course) Semester Equivalent (SE) to: Semester Replacement (SR) to: New 2.2 Semester(s) offered: Fall Spring Summer Offered every other year only Other Page 1 of 5

2 2.3 Student Requirements: Students required to take the course: (by program and year, as appropriate) None Students who might elect to take the course: Applied Statistics majors, Computational Mathematics majors, Applied Mathematics majors, or students doing a statistics minor 3.0 Goals of the course: (including rationale for the course, when appropriate) 3.1 To introduce the tools and techniques of multivariate analysis. 3.2 To provide knowledge of the application of multivariate analysis to real-world problems. 3.3 To introduce statistical packages used to solve multivariate problems. 4.0 Course description: (as it will appear in the RIT Catalog, including pre- and co-requisites, semesters offered) COS-STAT-425 Multivariate Statistical Analysis This course is a study of the multivariate normal distribution, statistical inference on multivariate data, multivariate analysis of covariance, canonical correlation, principal component analysis, and cluster analysis. A statistical software package such as Excel or SAS is used for data analysis. (COS-STAT-305) Class 3, Credit 3 (S, alternate years) 5.0 Possible resources: (texts, references, computer packages, etc.) 5.1 C. Chatfield and A. J. Collins, Introduction to Multivariate Analysis, Chapman and Hall, London, UK. 5.2 R. A. Johnson and D. W. Wichern, Applied Multivariate Statistical Analysis, Prentice Hall, Upper Saddle River, NJ. 5.3 Software: SAS, Chicago, IL. 6.0 Topics: (outline) Topics with an asterisk(*) are at the instructor s discretion, as time permits 6.1 Introduction to Multivariate Analysis Multivariate distributions Multivariate data Processing multivariate data Mean vectors Covariance and Correlation matrices Graphical summaries 6.2 The Multivariate Normal Population Definition and properties of the multivariate normal distribution Multivariate normal samples and the Hotelling T2 distribution Procedures based on multivariate normal samples Inference on a mean vector Page 2 of 5

3 Comparison of two mean vectors The union-intersection principal Simultaneous confidence intervals and regions Inferences on a covariance matrix, comparison of several covariance matrices* 6.3 Discrimination and Classification Fisher s method, the linear discriminant function Classification with two multivariate normal populations Classification with several populations* 6.4 Principal Component Analysis Definition Large sample inference 6.5 Canonical Correlation Analysis Correlations: simple, partial, multiple, and canonical Canonical variates and correlation 6.6 Clustering Similarity and distance measures Hierarchical clustering methods: single and complete linkage Graphical techniques: Andrew s plots, star plots, and Chernoff faces 7.0 Intended learning outcomes and associated assessment methods of those outcomes: Assessment Methods Learning Outcomes 7.1 Identify the tools and techniques of multivariate analysis 7.2 Explain principal component analysis 7.3 Treat clustering 7.4 Apply multivariate analysis to real world problems 7.5 Use statistical packages to solve multivariate problems 8.0 Program goals supported by this course: 8.1 To develop an understanding of the statistical framework that supports engineering, science, and mathematics. 8.2 To develop critical and analytical thinking. 8.3 To develop an appropriate level of statistical literacy and competency. Page 3 of 5

4 8.4 To produce graduates who can effectively use mathematics and/or statistics to model, analyze, and solve problems arising in science, engineering, business, and other disciplines. 9.0 General education learning outcomes and/or goals supported by this course: Assessment Methods General Education Learning Outcomes 9.1 Communication Express themselves effectively in common college-level written forms using standard American English Revise and improve written and visual content Express themselves effectively in presentations, either in spoken standard American English or sign language (American Sign Language or English-based Signing) Comprehend information accessed through reading and discussion 9.2 Intellectual Inquiry Review, assess, and draw conclusions about hypotheses and theories Analyze arguments, in relation to their premises, assumptions, contexts, and conclusions Construct logical and reasonable arguments that include anticipation of counterarguments Use relevant evidence gathered through accepted scholarly methods and properly acknowledge sources of information 9.3 Ethical, Social and Global Awareness Analyze similarities and differences in human experiences and consequent perspectives Examine connections among the world s populations Identify contemporary ethical questions and relevant stakeholder positions 9.4 Scientific, Mathematical and Technological Literacy Explain basic principles and concepts of one of the natural sciences Apply methods of scientific inquiry and problem solving to contemporary issues Comprehend and evaluate mathematical and statistical information Page 4 of 5

5 Assessment Methods General Education Learning Outcomes Perform college-level mathematical operations on quantitative data Describe the potential and the limitations of technology Use appropriate technology to achieve desired outcomes 9.5 Creativity, Innovation and Artistic Literacy Demonstrate creative/innovative approaches to coursebased assignments or projects Interpret and evaluate artistic expression considering the cultural context in which it was created 10.0 Other relevant information: (such as special classroom, studio, or lab needs, special scheduling, media requirements, etc.) 10.1 Smart classroom 10.2 Computer lab with Excel and XLSTAT or SAS Page 5 of 5

STA 225: Introductory Statistics (CT)

Marshall University College of Science Mathematics Department STA 225: Introductory Statistics (CT) Course catalog description A critical thinking course in applied statistical reasoning covering basic