ISTINYE UNIVERSITY BİLGİ PAKETİ / DERS KATALOĞU
Course Introduction and Application Information
| Course Code: | ISE026 | ||||
| Course Name: | Applied Statistics and Data Analysis | ||||
| Semester: |
Fall |
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| Course Credits: |
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| Language of instruction: | English | ||||
| Course Condition: | |||||
| Does the Course Require Work Experience?: | No | ||||
| Type of course: | Departmental Elective | ||||
| Course Level: |
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| Mode of Delivery: | Face to face | ||||
| Course Coordinator: | Doç. Dr. SALİHA KARADAYI USTA | ||||
| Course Lecturer(s): | Dr. Öğr. Üy. EMRE ÇAKMAK | ||||
| Course Assistants: |
Course Objective and Content
| Course Objectives: | The course aims to provide students with the ability to solve problems related to data analysis that arise in various fields in real life, by using statistical techniques, as well as teaching basic statistical concepts. |
| Course Content: | Introduction to Scientific Evidence and Statistics, Measures of central tendency and the normal distribution, Probability, Discrete random variables and probability distributions, Estimation of mean and standard deviation and the normal distribution, Hypothesis testing for one or two population means, Student t-test, Analysis of Variance and multiple comparison tests, Simple linear regression |
Learning Outcomes
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The students who have succeeded in this course;
1) Should know basic statistical concepts such as mean, variable, standard deviation. 2) Must have sufficient knowledge about basic probability issues such as random variables and probability distributions. 3) Should be able to use statistical methods such as hypothesis testing, analysis of variance and linear regression. 4) Should be able to apply statistical methods such as hypothesis testing, analysis of variance and linear regression to data in different fields. |
Course Flow Plan
| Week | Subject | Related Preparation |
| 1) | Introduction to Scientific Evidence and Statistics | |
| 2) | Measures of central tendency and the normal distribution | |
| 3) | Probability | |
| 4) | Discrete random variables and probability distributions | |
| 5) | Discrete random variables and probability distributions | |
| 6) | Estimation of mean and standard deviation and the normal distribution | |
| 7) | Hypothesis testing for one or two population means, Student t-test | |
| 8) | Midterm exam | |
| 9) | Hypothesis testing for one or two population means, Student t-test | |
| 10) | Hypothesis testing for small sample sizes and multinomial experiments, Fisher’s exact test | |
| 11) | Analysis of Variance and multiple comparison tests | |
| 12) | Analysis of Variance and multiple comparison tests | |
| 13) | Simple linear regression | |
| 14) | Simple linear regression |
Sources
| Course Notes / Textbooks: | Mann, P. S. (2007). Introductory statistics. John Wiley & Sons. |
| References: | Devore, J. L., Farnum, N. R., & Doi, J. A. (2013). Applied statistics for engineers and scientists. Cengage Learning. |
Course - Program Learning Outcome Relationship
| Course Learning Outcomes | 1 |
2 |
3 |
4 |
|---|---|---|---|---|
| Program Outcomes |
Course - Learning Outcome Relationship
| No Effect | 1 Lowest | 2 Average | 3 Highest |
| Program Outcomes | Level of Contribution |
Assessment & Grading
| Semester Requirements | Number of Activities | Level of Contribution |
| Homework Assignments | 4 | % 20 |
| Midterms | 1 | % 30 |
| Final | 1 | % 50 |
| total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 50 | |
| PERCENTAGE OF FINAL WORK | % 50 | |
| total | % 100 | |
Workload and ECTS Credit Calculation
| Activities | Number of Activities | Preparation for the Activity | Spent for the Activity Itself | Completing the Activity Requirements | Workload | ||
| Course Hours | 13 | 0 | 3 | 39 | |||
| Study Hours Out of Class | 13 | 0 | 1 | 13 | |||
| Homework Assignments | 4 | 0 | 10 | 40 | |||
| Midterms | 1 | 8 | 2 | 10 | |||
| Final | 1 | 18 | 2 | 20 | |||
| Total Workload | 122 | ||||||