Islamic Economics and Finance
Course Details
KTO KARATAY UNIVERSITY
İktisadi, İdari ve Sosyal Bilimler Fakültesi
Programme of Islamic Economics and Finance
Course Details
İktisadi, İdari ve Sosyal Bilimler Fakültesi
Programme of Islamic Economics and Finance
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 99700015 | Statistics II | 2 | Spring | 4 | 3+0+0 | 4 | 4 |
| Course Type | Compulsory |
| Course Cycle | Bachelor's (First Cycle) (TQF-HE: Level 6 / QF-EHEA: Level 1 / EQF-LLL: Level 6) |
| Course Language | Turkish |
| Methods and Techniques | - |
| Mode of Delivery | Face to Face |
| Prerequisites | - |
| Coordinator | - |
| Instructor(s) | Asst. Prof. Fatma ÇİFTCİ |
| Instructor Assistant(s) | - |
Course Instructor(s)
| Name and Surname | Room | E-Mail Address | Internal | Meeting Hours |
|---|---|---|---|---|
| Asst. Prof. Fatma ÇİFTCİ | C-105 | [email protected] | 7429 |
Course Content
The statistics course covers a comprehensive range of topics essential for effective data analysis and decision making in business. Key areas of study include descriptive statistics, probability theory, and various probability distributions. Students will learn about inferential statistics, including hypothesis testing, confidence intervals, and regression analysis. The course also covers the use of statistical software for data analysis and visualization. Emphasis will be placed on real-world applications such as market research, quality control, and financial analysis through case studies and practical exercises. By the end of the course, students will be proficient in applying statistical techniques to analyze business data and derive actionable insights.
Objectives of the Course
The aim of the statistics course is to equip students with the basic statistical skills and knowledge necessary to analyse data and make informed business decisions. This course aims to develop students' ability to collect, interpret and present quantitative information, providing a solid foundation in statistical methods and their applications in a business context. By integrating theoretical concepts with practical applications, students will be prepared to use statistical tools to solve business problems, optimise operations and support strategic planning.
Contribution of the Course to Field Teaching
| Basic Vocational Courses | X |
| Specialization / Field Courses | X |
| Support Courses | |
| Transferable Skills Courses | |
| Humanities, Communication and Management Skills Courses |
Relationships between Course Learning Outcomes and Program Outcomes
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Program Learning Outcomes | Level |
|---|---|---|
| P1 | İslam İktisadı ve Finans alanında kuramsal ve uygulamalı bilgilere sahip olma, sahip olduğu bilgileri kullanabilme | 1 |
| P2 | İslam İktisadı ve Finans alanında edindiği bilgi, beceri ve yetkinlikleri kullanarak meseleleri tanımlama, veri toplama, değerlendirme, analiz etme, yorumlama ve çözüm önerisi geliştirebilme | 2 |
| P3 | İslam İktisadı ve Finans alanıyla ilgili farklı bilgi kaynaklarına erişip sayısal analiz ve araştırma yapabilme | 3 |
| P4 | Disiplin içi, çok disiplinli veya çok kültürlü gruplarda ve bireysel çalışabilme | 1 |
| P5 | Ahlaki değerler ve mesleki sorumluluk bilinci ile hareket edebilme | 2 |
| P6 | Alan uygulamalarının, evrensel ve toplumsal etkileri ile hukuki sonuçlarını bilme | 2 |
Course Learning Outcomes
| Upon the successful completion of this course, students will be able to: | |||
|---|---|---|---|
| No | Learning Outcomes | Outcome Relationship | Measurement Method ** |
| O1 | Critically evaluates the results of numerical analyses. | P.3.5 | 1 |
| O2 | Shares research results in the form of scientific articles, reports, and presentations. | P.3.6 | 1 |
| ** Written Exam: 1, Oral Exam: 2, Homework: 3, Lab./Exam: 4, Seminar/Presentation: 5, Term Paper: 6, Application: 7 | |||
Weekly Detailed Course Contents
| Week | Topics |
|---|---|
| 1 | Point estimators and their properties, confidence level and interval concepts |
| 2 | Confidence interval estimation of the normal distribution mean for cases where the main mass variance is and is not know |
| 3 | Confidence interval estimation of the difference of two main population means on which the samples are dependent |
| 4 | Confidence interval estimate of the confidence interval of the difference of two main population means where the samples are independent for cases where the main population variance is and is not known |
| 5 | Hypothesis testing concepts, Hypothesis testing of the normal distribution mean for the case where the main mass variance is known, P value concept |
| 6 | Hypothesis testing of the normal distribution mean for cases where the main mass variance is unknown |
| 7 | Hypothesis testing of the difference between the means of two main masses where the samples are dependent |
| 8 | Midterm Exam |
| 9 | Hypothesis testing of the difference of two main population means where the samples are independent for cases where the main population variance is and is not known. Variance equality test for two normally distributed main masses |
| 10 | Linear models, Least squares method, Linear regression model, Least squares coefficient estimators |
| 11 | Explanatory powers of linear regression equation, Analysis of variance, Coefficient of determination |
| 12 | Hypothesis testing and confidence interval for the slope of the main mass regression, Hypothesis testing with the help of F distribution for the main mass slope, Estimation and confidence intervals, Correlation analysis and correlation hypothesis testing |
| 13 | Multiple regression model, least squares method and sample multiple regression model. Explanatory powers of multiple regression equation, coefficient of determination and multiple correlation coefficient |
| 14 | Confidence intervals and hypothesis testing for regression coefficients. Hypothesis testing by means of F distribution for all coefficients of multiple regression equation |
Evaluation Method and Passing Criteria
| In-Term Studies | Quantity | Percentage |
|---|---|---|
| Attendance | - | - |
| Course Specific Internship (If Any) | - | - |
| Homework | - | - |
| Presentation | - | - |
| Projects | - | - |
| Midterms | 1 | 40 (%) |
| Final Exam | 1 | 60 (%) |
| Total | 100 (%) | |
ECTS / Working Load Table
| Quantity | Duration | Total Work Load | |
|---|---|---|---|
| Course Week Number and Time | 14 | 3 | 42 |
| Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 14 | 2 | 28 |
| Midterms | 1 | 20 | 20 |
| Quiz | 0 | 0 | 0 |
| Homework | 0 | 0 | 0 |
| Practice | 0 | 0 | 0 |
| Laboratory | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Workshop | 0 | 0 | 0 |
| Presentation/Seminar Preparation | 0 | 0 | 0 |
| Fieldwork | 0 | 0 | 0 |
| Final Exam | 1 | 30 | 30 |
| Other | 0 | 0 | 0 |
| Total Work Load: | 120 | ||
| Total Work Load / 30 | 4 | ||
| Course ECTS Credits: | 4 | ||
Course - Learning Outcomes Matrix
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Learning Outcomes | P3 |
|---|---|---|
| O1 | Critically evaluates the results of numerical analyses. | 4 |
| O2 | Shares research results in the form of scientific articles, reports, and presentations. | 4 |