ROCHESTER INSTITUTE OF TECHNOLOGY COURSE OUTLINE FORM COLLEGE OF SCIENCE. School of Mathematical Sciences
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1 ! ROCHESTER INSTITUTE OF TECHNOLOGY COURSE OUTLINE FORM COLLEGE OF SCIENCE School of Mathematical Sciences New Revised COURSE: COS-STAT-325 Design of Experiments 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: Design of Experiments Credit Hours: 3 Prerequisite(s): COS-STAT-205 or COS-MATH-252 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 this course: (by program and year, as appropriate) Third-year Applied Statistics majors Students who might elect to take the course: Students in Computational Mathematics, Applied Mathematics, or Engineering or students doing a statistics or mathematics minor 3.0 Goals of the course: (including rationale for the course, when appropriate) 3.1 To introduce the fundamental concepts of statistical design. 3.2 To provide experience in the use of statistical software (SAS). 4.0 Course description: (as it will appear in the RIT Catalog, including pre- and co-requisites, semesters offered) COS-STAT-325 Design of Experiments This course is a study of the design and analysis of experiments. It includes extensive use of statistical software. Topics include single-factor analysis of variance, multiple comparisons and model validation, multifactor factorial designs, fixed, random and mixed models, expected mean square calculations, confounding, randomized block designs, and other designs, and topics as time permits. (COS-STAT-205 or COS-MATH-252) Class 3, Credit 3 (F) 5.0 Possible resources: (texts, references, computer packages, etc.) 5.1 Montgomery, Design and Analysis of Experiments, Wiley, Hoboken, NJ. 5.2 Kutner, Nachtsheim, Neter and Li, Applied Linear Statistical Models, McGraw-Hill, Columbus OH. 6.0 Topics: (outline) Topics with an asterisk(*) are at the instructor s discretion, as time permits 6.1 Topics per Design Model and assumptions Fixed versus random effects Fitting the model - normal equations Estimation of parameters - variance components Expected mean squares Testing hypotheses - interpretation of results Multiple comparisons (fixed effects) Choice of sample size - power Model adequacy - residual analysis 6.2 Specific Designs Completely randomized design Randomized complete blocks Incomplete blocks Page 2 of 5
3 6.2.4 Factorial experiments - 2 and 3 level, interactions, confounding Nested designs Mixed models Fractional factorials Discussion of stylistic elements in a technical report Selection of topic in consultation with the instructor Proposal approved by instructor Draft of written report Final written report 7.0 Intended learning outcomes and associated assessment methods of those outcomes: Assessment Methods Learning Outcomes 7.1 Students will identify and describe a variety of models 7.2 Students will explain the fundamental concepts of design, and analyze data using statistical software (SAS) 7.3 Students will detail the assumptions of various statistical models, their limitations, and the nature of data 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. 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: Page 3 of 5
4 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 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 Page 4 of 5
5 Assessment Methods General Education Learning Outcomes 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.) Statistics Lab equipped with MINITAB and SAS software, word-processing software, and internet access. The in this class (see Topic 6.3) will constitute at least 20% of the overall grade. Students will receive written feedback from the instructor regarding the rough draft of their final report (see Topic 6.3), and are expected to implement revisions accordingly. Page 5 of 5
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