Statistics is one of the core concepts in engineering management. The majority of the engineering management tools and techniques are based on statistics. This course aims to provide students with the theoretical and applied knowkedge of statistics that an engineer should have.
Lecture, question-answer, discussion, problem solving
2. Week
Discrete and continuaous random variables and Bayes Theorem.
Lecture, question-answer, discussion, problem solving
3. Week
Sample statistics ans population parameters, quartiles, histogram, scatter plot, box plot, probability plot.
Lecture, question-answer, discussion, problem solving
4. Week
Sampling distribution of means and Central Limit Theorem.
Lecture, question-answer, discussion, problem solving
5. Week
Interval Estimation for a Single Sample: Confidence Intervals (Known Variance)
Lecture, question-answer, discussion, problem solving
6. Week
Hypotheses Testing for a Single Sample (Known Variance): Type 1, Type 2 error, Power of a Test
Lecture, question-answer, discussion, problem solving
7. Week
Testing for Two Samples: paired t-test, Inferences on the Variances of two Normal Populations
Lecture, question-answer, discussion, problem solving
8. Week
Midterm
9. Week
Simple Linear Regression, Properties of Least Squares, Confidence Intervals on the Slope and Intercept
Lecture, question-answer, discussion, problem solving
10. Week
One-way ANOVA
Lecture, question-answer, discussion, problem solving
11. Week
Two-factor ANOVA and general linear model
Lecture, question-answer, discussion, problem solving
12. Week
2^k Factorials
Lecture, question-answer, discussion, problem solving
13. Week
2^k Factorials (continued)
Lecture, question-answer, discussion, problem solving
14. Week
Response surface designs
Lecture, question-answer, discussion, problem solving
15. Week
Final
16. Week
Final
17. Week
Final
Assessments
Evaluation tools
Quantity
Weight(%)
Midterm(s)
1
35
Quizzes
3
15
Final Exam
1
50
Program Outcomes
PO-1
Knowledge about management processes and management skills
PO-2
Knowledge and application skills related to the methods and competencies required for solving engineering problems
PO-3
Knowledge about developing areas of manufacturing and service sectors
PO-4
Ability to work in multi-disciplinary engineering teams
PO-5
Experience and knowledge of scientific research and publishing within the frame of academic ethics
Learning Outcomes
LO-1
Knowledge about the use of statistics in industrial engineering, calculate descriptive statistics of a data set, give examples of data display methods.
LO-2
Knows special probability distribution functions, uses random distributions in modeling and problem-solving.
LO-3
Constructs and applies the hypothesis test related to parameters, deducts inferences from the test results and calculates the p-value.
LO-4
Creates the Simple and Multiple Linear Regression Model with the help of Excel and Minitab. Interprets the statistical significance of the relationships between variables through ANOVA output.
LO-5
Designs and applies factorial experiments and constructs ANOVA table. Interprets the results of the tests related to the model and variables, and lists the variables according to their significance level.
