MATHEMATICS (TURKISH, PHD) | |||||
PhD | TR-NQF-HE: Level 8 | QF-EHEA: Third Cycle | EQF-LLL: Level 8 |
Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
EEE5022 | Applied Statistics | Fall | 3 | 0 | 3 | 9 |
The course opens with the approval of the Department at the beginning of each semester |
Language of instruction: | En |
Type of course: | Departmental Elective |
Course Level: | |
Mode of Delivery: | Face to face |
Course Coordinator : | Assoc. Prof. SAEID KARAMZADEH |
Course Lecturer(s): |
Prof. Dr. SELİM ZAİM |
Course Objectives: | The course introduces fundamental topics in statistics and implements its applications to industrial, medical, financial, energy and similar type very large-size datasets to infer meaninful statistical results. The course is for gradute students with no significant background on this subject. Implementations will be performed on the open source statistical software R. Introduction to R programming will be given. |
The students who have succeeded in this course; I. Identify basic terms in statistics. II. Gain ability to use and apply basic methods and programming tools used in statistics over various engineering disciplines. III. Ability to explore data and its relationships. IV. Ability to perform hypothesis testing for statistical problems. V. Perform statistical inference over statistical data. |
Topics include: Introduction to R programming, Sampling, Data Exploration, Exploring Relationships, Probability, Random Variables and Probability Distributions, Estimation, Hypothesis Testing, Statistical Inference, Multiple Testing Correction, ANOVA, Analysis of Categorical Variables, Regression Analysis, Bayesian Analysis, Survival Analysis, Over Representation Analysis, Meta Analysis. |
Week | Subject | Related Preparation | |
1) | Introduction | ||
2) | Introduction to R statistical programming | ||
3) | Term Project | ||
4) | Data Exploration with R | ||
5) | Visualizing and Summarizing Relationships | ||
6) | Probability and Random Variables | ||
7) | Estimation in datasets | ||
8) | Hypothesis Testing for various engineering applications | ||
9) | Statistical Inference over various large datasets | ||
10) | ANOVA | ||
11) | Analysis of Categorical Variables | ||
12) | Regression and Bayesian Analysis | ||
13) | Survival analysis | ||
14) | Over Representation Analysis |
Course Notes: | Principles of Applied Statistics (Paperback), by D. R. Cox, Christl A. Donnelly 2011 ISBN-10: 1107644453 | ISBN-13: 978-1107644458 |
References: | Introductory Statistics with R Peter Dalgaard 2011 ISBN 978-0-387-79053-4 |
Semester Requirements | Number of Activities | Level of Contribution |
Attendance | 0 | % 0 |
Laboratory | 0 | % 0 |
Application | 0 | % 0 |
Field Work | 0 | % 0 |
Special Course Internship (Work Placement) | 0 | % 0 |
Quizzes | 0 | % 0 |
Homework Assignments | 0 | % 0 |
Presentation | 0 | % 0 |
Project | 1 | % 30 |
Seminar | 0 | % 0 |
Midterms | 1 | % 30 |
Preliminary Jury | 0 | % 0 |
Final | 1 | % 40 |
Paper Submission | 0 | % 0 |
Jury | % 0 | |
Bütünleme | % 0 | |
Total | % 100 | |
PERCENTAGE OF SEMESTER WORK | % 30 | |
PERCENTAGE OF FINAL WORK | % 70 | |
Total | % 100 |
Activities | Number of Activities | Workload | |
Course Hours | 14 | 42 | |
Laboratory | |||
Application | |||
Special Course Internship (Work Placement) | |||
Field Work | |||
Study Hours Out of Class | 14 | 42 | |
Presentations / Seminar | |||
Project | 1 | 30 | |
Homework Assignments | |||
Quizzes | |||
Preliminary Jury | |||
Midterms | 1 | 40 | |
Paper Submission | |||
Jury | |||
Final | 1 | 50 | |
Total Workload | 204 |
No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
Program Outcomes | Level of Contribution |