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.
Prerequisite(s)
-
Corequisite(s)
-
Special Requisite(s)
Attendance is mandatory; students who have attended less than 60% of classes will receive zero points from the term project regardless of their exam grades. Each student is expected to actively participate in classroom discussions and exercises.
Instructor(s)
Assist. Prof. Dr. Duygun Fatih Demirel
Course Assistant(s)
-
Schedule
This course is not offered in this semester.
Office Hour(s)
This course is not offered in this semester.
Teaching Methods and Techniques
Lecture, question-answer, discussion, problem solving
Principle Sources
Douglas C. Montgomery and George C. Runger , Statistics and Probability for Engineers, 6th edition, Wiley
Other Sources
Course notes
Course Schedules
Week
Contents
Learning Methods
1. Week
Axioms of Probability, Bayes Theorem, Discrete and Continuous Random Variables
Lecture, question-answer, discussion, problem solving
2. Week
Sample and Population Mean, Variance, Stem and Leaf Diagram, Quartiles, Histograms, Box-Plots, and Probability Plots
Lecture, question-answer, discussion, problem solving
3. Week
Sampling Distribution of Means and Central Limit Theorem, Point Estimation
Lecture, question-answer, discussion, problem solving
4. Week
Interval Estimation for a Single Sample: Confidence Intervals (Known Variance), Confidence Intervals (Unknown Variance): t-distribution, Large Sample CIs,
Lecture, question-answer, discussion, problem solving
5. Week
Hypotheses Testing for a Single Sample (Known Variance): Type 1, Type 2 error, Power of a Test
Lecture, question-answer, discussion, problem solving
6. Week
Hypotheses Testing for a Single Sample (Unknown Variance): t-test, chi-square test, Goodness of fit and Contingency Tables,
Lecture, question-answer, discussion, problem solving
7. Week
Hypotheses Testing for a Single Sample (Review)
Lecture, question-answer, discussion, problem solving
8. Week
Midterm Exam
9. Week
Hypotheses Testing for Two Samples: Inference for a difference of two means Hypotheses,
Lecture, question-answer, discussion, problem solving
10. Week
Testing for Two Samples: paired t-test, Inferences on the Variances of two Normal Populations
Lecture, question-answer, discussion, problem solving
11. Week
Simple Linear Regression, Properties of Least Squares, Confidence Intervals on the Slope and Intercept, Multiple Linear Regression
Lecture, question-answer, discussion, problem solving
12. Week
Questionnaire Design
Lecture, question-answer, discussion, problem solving
13. Week
Multiple Linear Regression in the context of questionnaire analysis
Lecture, question-answer, discussion, problem solving
14. Week
Holiday
15. Week
Final
16. Week
Final
17. Week
Final
Assessments
Evaluation tools
Quantity
Weight(%)
Midterm(s)
1
35
Quizzes
3
5
Project(s)
2
15
Final Exam
1
45
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
Explains the uses of statistics in engineering management, calculates descriptive statistics for the sample data, exemplifies data displaying methods.
LO-2
Understands the concept of random sampling and point estimation of population parameters.
LO-3
Understands sampling distributions of sample mean and sample variance, calculates probabilities related to sample mean by using central limit theorem (CLT).
LO-4
Builds and interprets confidence intervals for population parameters.
LO-5
Formulates and performs hypothesis tests, draws conclusions based on test results and calculates the p-value.
