COLLEGE OF SCIENCE. School of Mathematical Sciences. NEW (or REVISED) COURSE: COS-STAT-741 Regression Analysis. request date: *Approval

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ROCHESTER INSTITUTE OF TECHNOLOGY COURSE OUTLINE FORM COLLEGE OF SCIENCE School of Mathematical Sciences NEW (or REVISED) COURSE: COS-STAT-741 Regression Analysis 1.

General Education: Yes No Writing Intensive: Yes No Honors Yes No Approval request date: *Approval request date: Approval granted date: **Approval granted date: 2.

1 ROCHESTER INSTITUTE OF TECHNOLOGY COURSE OUTLINE FORM COLLEGE OF SCIENCE School of Mathematical Sciences NEW (or REVISED) COURSE: COS-STAT-741 Regression Analysis 1.0 Course Designations and Approvals Required course approvals: Academic Unit Curriculum Committee College Curriculum Committee Optional designations: Is designation desired? General Education: Yes No Writing Intensive: Yes No Honors Yes No Approval request date: *Approval request date: Approval granted date: **Approval granted date: 2.0 Course information: Course title: Regression Analysis Credit hours: 3 Prerequisite(s): COS-STAT-701 or equivalent Co-requisite(s): None Course proposed by: Peter Bajorski Effective date: August 2013 Contact hours Maximum students/section Classroom 3 25 Lab 0 Studio 0 Other (specify) 0 2.a Course Conversion Designation*** (Please check which applies to this course). *For more information on Course Conversion Designations please see page four. Semester Equivalent (SE) Please indicate which quarter course it is equivalent to: Semester Replacement (SR) Please indicate the quarter course(s) this course is replacing: Regression Analysis I, Regression Analysis II New July 27, 2010

2 2.b Semester(s) offered (check) Fall (campus) Spring (online) Summer Other All courses must be offered at least once every 2 years. If course will be offered on a bi-annual basis, please indicate here: 2.c Student Requirements Students required to take this course: (by program and year, as appropriate) Students in MS in Applied Statistics Program Students who might elect to take the course: Other graduate students interested in Regression Analysis In the sections that follow, please use sub-numbering as appropriate (eg. 3.1, 3.2, etc.) 3.0 Goals of the course (including rationale for the course, when appropriate): For students 3.1 To gain a good understanding of regression techniques. 3.2 To know how and when to use regression techniques, realize when not to use them, and know where to get more information on them. 4.0 Course description (as it will appear in the RIT Catalog, including pre- and corequisites, and quarters offered). Please use the following format: COS-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. 5.0 Possible resources (texts, references, computer packages, etc.) Required: 5.1 Introduction to Linear Regression Analysis, 4th ed., Douglas C. Montgomery, Elizabeth A. Peck, G. Geoffrey Vining (2006), Wiley. Recommended: 5.2 Matrix Algebra as a Tool, Hadi (1995). 5.3 Applied Regression Analysis, Draper, N.R. and H. Smith, 1998, 3 rd ed, Wiley. 5.4 Applied Linear Statistical Models, 5 th ed., Kutner, Nachtsheim, Neter, Li (2005), McGraw-Hill 2

3 6.0 Topics (outline): 6.1 Introduction to Regression 6.2 Method of least squares 6.3 ANOVA, Sums of Squares 6.4 Inference in Simple Linear Regression 6.5 Interpretation of regression coefficients 6.6 Residual Analysis 6.7 Vectors and Matrices in Regression 6.8 Multiple Linear Regression 6.9 Polynomial models 6.10 Extra sums of squares principle 6.11 The Geometry of Least Squares 6.12 General Linear Hypotheses 6.13 Influential observations and leverage including Cook s D 6.14 Transformations 6.15 Weighted least squares 6.16 Dummy Variables 6.17 Selecting the Best Subset Model 6.18 Multicollinearity 6.19 Nonlinear Regression 7.0 Intended course learning outcomes and associated assessment methods of those outcomes (please include as many Course Learning Outcomes as appropriate, one outcome and assessment method per row). Course Objectives Level 2: Comprehension: 2.1.Formulates a theoretical statistical model for linear and non-linear regression. 2.2.Distinguishes between intrinsically linear and non-linear regression models. Level 3: Application: 3.1.Fits an acceptable and useful model to a given data set. 3.2.Verifies the regression model assumptions. 3.3.Interprets the regression model fitted to a given data set. 3.4.Uses computer software for fitting and interpretation of a regression model. Level 4: Analysis: 4.1.Analyzes impact of observations through their influence and leverage. 4.2.Selects the best subset model. 4.3.Analyzes multicollinearity of variables in a given data set. Assessment Method Homework Exams Projects 3

4 Level 5: Synthesis: 5.1.Communicates the results of statistical model building. 5.2.Draws conclusions and makes recommendations based on one or more plausible models. Level 6: Evaluation: 6.1.Evaluates several potential regression models and decides on the most appropriate one for a given purpose. 6.2.Supports recommendations with a thorough assessment of a given regression model. 8.0 Program outcomes and/or goals supported by this course Relationship to Program Outcomes (1 = slightly, 2=moderately, 3=significantly) Program Outcomes and/or Goals for CQAS 8.1 Advanced Certificate in Lean Six Sigma Demonstrates an solid understanding of statistical thinking and Lean Six Sigma methodology in solving real-world problems Leads Lean Six Sigma improvement projects. Level of Support Advanced Certificate and Masters of Science in Applied Statistics Demonstrates solid understanding of statistical thinking and applied statistics methodology in solving real-world problems Designs studies that are efficient and valid Analyzes data using appropriate statistical methods Communicates the results of statistical analysis with effective reports and presentations. Note: Students obtaining the Advanced Certificate in Applied Statistics will not be expected to perform at the same level as students obtaining a Master of Science degree. 4

5 9.0 - Not Applicable General Education Learning Outcome Supported by the Course, if appropriate 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 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 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 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 Creativity, Innovation and Artistic Literacy Demonstrate creative/innovative approaches to course-based assignments or projects Interpret and evaluate artistic expression considering the cultural context in which it was created Assessment Method 10.0 Other relevant information (such as special classroom, studio, or lab needs, special scheduling, media requirements, etc.) None 5

6 *Optional course designation; approval request date: This is the date that the college curriculum committee forwards this course to the appropriate optional course designation curriculum committee for review. The chair of the college curriculum committee is responsible to fill in this date. **Optional course designation; approval granted date: This is the date the optional course designation curriculum committee approves a course for the requested optional course designation. The chair of the appropriate optional course designation curriculum committee is responsible to fill in this date. ***Course Conversion Designations Please use the following definitions to complete table 2.a on page one. Semester Equivalent (SE) Closely corresponds to an existing quarter course (e.g., a 4 quarter credit hour (qch) course which becomes a 3 semester credit hour (sch) course.) The semester course may develop material in greater depth or length. Semester Replacement (SR) A semester course (or courses) taking the place of a previous quarter course(s) by rearranging or combining material from a previous quarter course(s) (e.g. a two semester sequence that replaces a three quarter sequence). New (N) - No corresponding quarter course(s). 6

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