STATISTICS AND COMPUTER APPLICATIONS IN INDUSTRIAL ENGINEERING

Course Description

The course is designed to give useful feedback from probability knowledge and to give brief information of computer software applications for Industrial Engineering courses. Introduction to model formulation and numerical solution methods in industrial engineering. Emphasis on decisions, constraints, and objectives in problem solving. Introductory knowladge on project management and developing a project a plan by use of Work Brake Down Structure and Network analysis. Understanding the statistical nature of engineering processes. Emphasis on proper data collection and classification, characteristics of variables and their distributions, joint probability distributions, and establishing hypotheses and statistical significance over engineering design specifications.

STATISTICS AND COMPUTER APPLICATIONS IN INDUSTRIAL ENGINEERING

Evaluation Tools (Active Term)

No evaluation items have been defined.

Course outcomes

1. 01 Using computer softwaresfor solving Industrial Engineering related problems
2. 02 Able to develop Work Brakedown Structure of a project
3. 03 Apply Critical Path Method for project scheduling
4. 04 Hypothesiz building and testing
5. 05 Design factorial experiments.

Course Syllabus

Reference Books & Course Materials

1. 01 Introduction to Probability and Statistics; Mendenhall/Beaver/Beaver; 14th Edition; Brooks/Cole, Cengage Learning; 2013; ISBN-13: 978-1-133-10375-2
2. 02 Operations Management: Theory and Practice; W.J. Stevenson; 11th Edition; McGraw-Hill; 2012; ISBN-13: 978-0077133016
3. 03 IBM SPSS Statistics 21 Brief Guide; IBM Press; 2012; Reference #: 7024972
4. 04 Microsoft Project 2013: Step by Step; Microsoft Press, 2013; ISBN-13: 978-0735669116

Learning Outcomes

1. L01 Foundational Data Analysis: Describe (3) and summarize data sets using populations, samples, and explain (4) of central tendency (mean, mode, median) and dispersion (symmetry and skewness). SOLO 3.5
2. L02 Graphical Representation: Describe (3) the application of visual data methods, including line graphs, bar graphs, pie charts, and histograms to explain (4) statistical findings.between its various forms. SOLO 3.5
3. L03 Probability Theory & Laws: Describe (3) and apply fundamental probability properties, including conditional probability, the multiplication rule, and Bayes’ Theorem. SOLO 3
4. L04 Distribution Analysis (Discrete & Continuous): Describe (3) and analyze and apply discrete (Bernoulli, Binomial, Poisson, etc.) and continuous (Normal, Exponential, Gamma, etc.) random variables and their properties (Expected Value and Variance). SOLO 3
5. L05 Sampling Theory & Limit Theorems: Generalize (5) sampling distributions (Standard Normal, t, Chi-squared, F-distributions) and explain (4) the Central Limit Theorem and approximation methods. SOLO 4.5
6. L06 Statistical Estimation: Explaining (4) estimation techniques, including biased/unbiased estimators for mean and variance, and the Maximum Likelihood Estimation (MLE) method. SOLO 4
7. L07 Hypothesis Testing: Identify (2) and explain (4) a variety of hypothesis tests, including Mean/Variance tests, ANOVA, Goodness-of-Fit, and Tests for Independence. SOLO 3
8. L08 Inferential Decision Making: Explain (4) application of p-value as a formal framework for decision-making and drawing conclusions in statistical testing. SOLO 4
9. L09 Modeling & Regression: Describe (3) and explain (4) simple linear regression models to understand relationships between variables. SOLO 3.5
10. L10 Professional Practice & Software Application: Generalize (5) the results through conducting independent research in real-world environments, work effectively in multidisciplinary teams, and produce technical reports using statistical software (Minitab). SOLO 5

Program Outcomes

1. P01 Knowledge of mathematics, natural sciences, basic engineering, computer-based computation, and topics specific to the relevant engineering discipline.
2. P02 Ability to apply knowledge of mathematics, natural sciences, basic engineering, computer-based computation, and topics specific to the relevant engineering discipline to the solution of complex engineering problems.
3. P03 Ability to define complex engineering problems by using knowledge of basic sciences, mathematics, and engineering, while considering the relevant United Nations Sustainable Development Goals (SDGs) related to the problem addressed.
4. P04 Ability to formulate complex engineering problems using knowledge of basic sciences, mathematics, and engineering, while considering the relevant United Nations Sustainable Development Goals (SDGs) associated with the problem addressed.
5. P05 Ability to analyse and solve complex engineering problems using knowledge of basic sciences, mathematics, and engineering, while considering the relevant United Nations Sustainable Development Goals (SDGs) associated with the problem addressed.
6. P06 Ability to design creative solutions to complex engineering problems.
7. P07 Ability to design complex systems, processes, devices, or products in a way that meets present and future needs while considering realistic constraints and conditions.
8. P08 Ability to select and use appropriate techniques and resources—including estimation and modelling—for the analysis and solution of complex engineering problems, while being aware of their limitations.
9. P09 Ability to select and use modern engineering and computational tools—including estimation and modelling—for the analysis and solution of complex engineering problems, while being aware of their limitations.
10. P10 Ability to conduct literature research and use appropriate research methods for the investigation of complex engineering problems.
11. P11 Ability to design experiments for the investigation of complex engineering problems.
12. P12 Ability to conduct experiments, collect data, analyse results, and interpret findings for the investigation of complex engineering problems.
13. P13 Knowledge of the impacts of engineering practices on society, health and safety, the economy, sustainability, and the environment within the framework of the United Nations Sustainable Development Goals (SDGs).
14. P14 Awareness of the legal implications of engineering solutions within the framework of the United Nations Sustainable Development Goals (SDGs).
15. P15 Knowledge of ethical responsibility and adherence to the principles of professional engineering conduct.
16. P16 Awareness of acting impartially without discrimination in any matter and of being inclusive of diversity.
17. P17 Ability to work effectively as an individual.
18. P18 Ability to work effectively as a team member or leader in intra-disciplinary teams (face-to-face, remote, or hybrid).
19. P19 Ability to work effectively as a team member or leader in multidisciplinary teams (face-to-face, remote, or hybrid).
20. P20 Ability to communicate effectively in spoken form on technical matters, taking into account the diverse characteristics of the target audience (such as education, language, and profession).
21. P21 Ability to communicate effectively in written form on technical matters, taking into account the diverse characteristics of the target audience (such as education, language, and profession).
22. P22 Knowledge of professional practices such as project management and economic feasibility analysis.
23. P23 Awareness of entrepreneurship and innovation.
24. P24 Ability for independent and lifelong learning.
25. P25 Ability to adapt to new and emerging technologies.
26. P26 Lifelong learning ability that includes the capacity to think critically about technological changes.

Po-Lo Matrix