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MEEM8011 Statistical Modeling and Design of Experiments
Course Duration: One Semester No. of Credit Units: 3 Level: R8 Medium of Instruction: English Prerequisites: Nil Precursors: Knowledge in Basic Probability and Statistics Equivalent Courses: Nil Exclusive Courses: Nil Part II 1. Course Aims: This course aims to develop students' abilities to understand the theory and application methods on statistical modeling of observational data and design of experiment data, including linear models, regression models, and analysis of variance models. 2. Course Intended Learning Outcomes (CILOs): Upon successful completion of this course, students should be able to: No. CILOs Weighting (if applicable) 1. Develop a familiarity with basic statistical estimation and hypothesis testing ideas and methods 1 2. 3 3. Understand motivations and needs for design of experiments in manufacturing and other applications. 1 4. Understand design and analysis of experiments methods to characterize and improve systems and processes. 3 5. Understand and apply regression methods and design of experiment methods to analyze and solve real life problems and applications. 2 3. Teaching and learning Activities (TLAs) Activity Type Timetabled Activity (Hours per week) Lecture/Tutorial/Laboratory Mix Lecture (3) TLA ILO Lecture Total hours CILO 1 6 6 CILO 2 12 12 CILO 3 3 3 CILO 4 12 12 CILO 5 6 6 Total 39 39 4. Assessment Tasks/Activities (ATs) Examination duration: Nil Percentage of coursework, examination, etc.: 100% continuous assessment (25 % Coursework; 35% Midterm Test; 40% Group Project) 5. Grading of Student Achievement: Grading Pattern: Standard (A+AA-…..F) Please refer to Grading of Courses in the Academic Regulations Detailed breakdown is given in the following table: CILO No. Group Work Individual Coursework Test Overall Weighting CILO 1 - 5 - 5 CILO 2 10 10 15 35 CILO 3 10 - 5 15 CILO 4 10 10 15 35 CILO 5 10 - - 10 Total (%) 40% 25% 35% 100% Part III Keyword Syllabus: · Statistical estimation and hypothesis testing · Data collection, data analysis, and model prediction · Regression modeling and analysis · Design and analysis of Experiments · Analysis of Variance modeling · Process estimation and prediction · Process characterization and improvement · Robust design and parameter design Related Links
Department of Manufacturing Engineering and Engineering Management |
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