sap course 1429523945

Course Code & Number:

IE 547

Course Title:

Modelling and Analysis of Uncertainty

Level of Course:

MS

Credits:

(3+0+0) 3 TEDU Credits, 7.5 ECTS Credits

Catalog Description:

Probability and fundamental theorems of probability. Conditional probability. Random variables. Discrete and continuous distributions. Expected value. Hypothesis tests. Variance analysis. Simple and multiple regression. Correlation analysis.

Pre-requisites & Co-requisites:

Pre-requisites: NONE
Co-requisites: NONE
Grading: 

Homework Assignments - 20%
Term Project - 25%
Midterm Exam - 25%
Final Exam - 30%

Year of Study: 
Master
Semester: 
Fall
Mode of Delivery: 
Face-to-face
Language of Instruction: 
English
Course Type: 
Compulsory
Required Reading: 
1. Walpole, R. E., Myers, R. H., Myers S. L., Ye, K. Probability and Statistics for Engineers and Scientists (9th edition)
Course Objective: 

The goal of this course is to teach the fundamentals of analysis and modeling under uncertainty. The course introduces the nature of uncertainty and basic concepts of probability including distributions and expectations. Another goal is to develop statistical analysis skills that help understanding and modeling uncertain events in practice. The course also aims to teach students how to use spreadsheet software for various data analysis.

Extended Description: 

Probability and fundamental theorems of probability. Conditional probability. Random variables. Discrete and continuous distributions. Expected value. Hypothesis tests. Variance analysis. Simple and multiple regression. Correlation analysis.

Learning Outcomes: 

Upon succesful completion of this course, a student will be able to
1. Calculate the mean and variance of a random variable and apply general properties of the expectation and variance operators.
2. Describe the main properties of probability distributions and random variables and apply the concepts of discrete and continuous probability distributions.
3. Identify the random variable(s) of interest in a given scenario and construct the probability distribution based on a real-world situation.
4. Perform hypothesis tests to statistically prove or disprove claims involving one or more populations
5. Construct simple and multiple regression models
6. Draw conclusions based on statistical models

Planned Learning Activities and Teaching Methods: 
Telling/Explaining
Discussion/Debate
Questioning
Reading
Problem Solving
Inquiry
Predict-Observe-Explain
Case Study/Scenarion Analysis
Assessment Methods and Criteria: 
Test / Exam
Quiz/Homework
Written Project

Student Workload:

Observation
6
hrs
Quizzes /Homeworks
40
hrs
Midterm Exam 1
12
hrs
Midterm Exam 2
12
hrs
Final Exam
18
hrs
Case Study Analysis
10
hrs
Others
72
hrs

Prepared By:

Revised By:

sap_editor