ARTIFICIAL INTELLIGENCE ENGINEERING | |||||
Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 |
Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
ECO2062 | Applied Statistics | Spring | 3 | 0 | 3 | 6 |
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester. |
Language of instruction: | English |
Type of course: | Non-Departmental Elective |
Course Level: | Bachelor’s Degree (First Cycle) |
Mode of Delivery: | Hybrid |
Course Coordinator : | Dr. Öğr. Üyesi SERKAN YEŞİLYURT |
Course Lecturer(s): |
Dr. Öğr. Üyesi AYSE ERTUĞRUL BAYKAN Prof. Dr. İPEK ALTINBAŞAK FARİNA |
Recommended Optional Program Components: | None |
Course Objectives: | To apply and interpret the results of a variety of statistical techniques from both descriptive and inferential statistics |
The students who have succeeded in this course; 1. The concept of the sampling distribution and to compute probabilities related to the sample mean and the sample proportion 2. To construct and interpret confidence interval estimates for the mean and the proportion 3. The basic principles of hypothesis testing and how to use hypothesis testing to test a mean or proportion 4. How to use hypothesis testing for comparing the difference between the means and proportion of populations 5. How to use one-way and two-way analysis of variance to test for differences among the means of several populations 6. How and when to use the chi-square test for contingency tables and how to use the chi-square test for a variance or standard deviation 7. How to use regression analysis to investigate the relationship between variables. |
Sampling and Sampling Distributions Sampling Distribution Properties Point and Interval Estimates Confidence Interval for population mean Confidence Intervals for the Population Proportion, π Fundamentals of Hypothesis Testing: One-Sample Tests Hypothesis Testing for Mean and Proportion Two-Sample Tests for Mean Two-Sample Tests for Proportion One-Way Analysis of Variance Two-Way Analysis of Variance Chi-Square Tests and Nonparametric Tests Introduction to Simple Linear Regression |
Week | Subject | Related Preparation |
1) | Sampling and Sampling Distributions | |
2) | Point and Interval Estimates | |
3) | Sampling Distribution Properties | |
4) | Confidence Interval for population mean | |
5) | Confidence Intervals for the Population Proportion, π | |
6) | Fundamentals of Hypothesis Testing: One-Sample Tests | |
7) | Hypothesis Testing for Mean and Proportion | |
8) | Review | |
9) | Two-Sample Tests for Mean | |
10) | Two-Sample Tests for Proportion | |
11) | Analysis of Variance | |
12) | Chi-Square Tests and Nonparametric Tests | |
13) | Introduction to Simple Linear Regression | |
14) | Review |
Course Notes / Textbooks: | Basic Business Statistics Concepts and Applications Mark L. Brenson, David M. Levine, Timothy C. Krehbiel, Pearson Education Prentice Hal. |
References: | . |
Semester Requirements | Number of Activities | Level of Contribution |
Homework Assignments | 4 | % 20 |
Midterms | 1 | % 35 |
Final | 1 | % 45 |
Total | % 100 | |
PERCENTAGE OF SEMESTER WORK | % 55 | |
PERCENTAGE OF FINAL WORK | % 45 | |
Total | % 100 |
Activities | Number of Activities | Duration (Hours) | Workload |
Course Hours | 16 | 3 | 48 |
Study Hours Out of Class | 14 | 7 | 98 |
Midterms | 1 | 2 | 2 |
Final | 1 | 2 | 2 |
Total Workload | 150 |
No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
Program Outcomes | Level of Contribution | |
1) | Have sufficient background in mathematics, science and artificial intelligence engineering. | |
2) | Use theoretical and applied knowledge in the fields of mathematics, science and artificial intelligence engineering together for engineering solutions. | |
3) | Identify, define, formulate and solve engineering problems, select and apply appropriate analytical methods and modeling techniques for this purpose. | |
4) | Analyse a system, system component or process and design it under realistic constraints to meet desired requirements; apply modern design methods in this direction. | |
5) | Select and use modern techniques and tools necessary for engineering applications. | |
6) | Design and conduct experiments, collect data, and analyse and interpret results. | |
7) | Work effectively both as an individual and as a multi-disciplinary team member. | |
8) | Access information via conducting literature research, using databases and other resources | |
9) | Follow the developments in science and technology and constantly update themself with an awareness of the necessity of lifelong learning. | |
10) | Use information and communication technologies together with computer software with at least the European Computer License Advanced Level required by their field. | |
11) | Communicate effectively, both verbal and written; know a foreign language at least at the European Language Portfolio B1 General Level. | |
12) | Have an awareness of the universal and social impacts of engineering solutions and applications; know about entrepreneurship and innovation; and have an awareness of the problems of the age. | |
13) | Have a sense of professional and ethical responsibility. | |
14) | Have an awareness of project management, workplace practices, employee health, environment and work safety; know the legal consequences of engineering practices. |