Business Administration
Course Details
KTO KARATAY UNIVERSITY
İktisadi, İdari ve Sosyal Bilimler Fakültesi
Programme of Business Administration
Course Details
İktisadi, İdari ve Sosyal Bilimler Fakültesi
Programme of Business Administration
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 | Wednesday 14:00 |
Course Content
İstatistik dersi, iş dünyasında etkili veri analizi ve karar verme için gerekli olan kapsamlı bir dizi konuyu kapsamaktadır. Temel çalışma alanları arasında tanımlayıcı istatistikler, olasılık teorisi ve çeşitli olasılık dağılımları yer almaktadır. Öğrenciler hipotez testi, güven aralıkları ve regresyon analizi dahil olmak üzere çıkarımsal istatistikler hakkında bilgi edineceklerdir. Ders ayrıca veri analizi ve görselleştirme için istatistiksel yazılım kullanımını da ele almaktadır. Vaka çalışmaları ve pratik alıştırmalar yoluyla pazar araştırması, kalite kontrol ve finansal analiz gibi gerçek dünya uygulamalarına vurgu yapılacaktır. Dersin sonunda öğrenciler, iş verilerini analiz etmek ve eyleme geçirilebilir içgörüler elde etmek için istatistiksel teknikleri uygulama konusunda yetkin olacaklardır.
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 | |
| Specialization / Field Courses | X |
| Support Courses | X |
| 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 | 1) To have advanced theoretical and / or practical knowledge and skills related to the basic functions of businesses (management and organisation, accounting and finance, production management and marketing). | 2 |
| P2 | 2) Identify, analyse and evaluate the current and potential problems of businesses by thinking critically and develop alternative solutions by using scientific methods. | 4 |
| P5 | 5) Can research and analyse information related to the field of business administration and can set operational, tactical and strategic goals according to the results obtained. | 4 |
Course Learning Outcomes
| Upon the successful completion of this course, students will be able to: | |||
|---|---|---|---|
| No | Learning Outcomes | Outcome Relationship | Measurement Method ** |
| O1 | P.1.4 | ||
| O2 | P.2.5 | ||
| O3 | P.2.6 | ||
| O4 | P.5.6 | ||
| O5 | P.2.7 | ||
| O6 | P.5.7 | ||
| ** 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 |
| 15 | Final Examination |
Textbook or Material
| Resources | Statistics for Social Sciences |
Evaluation Method and Passing Criteria
| In-Term Studies | Quantity | Percentage |
|---|---|---|
| Attendance | - | - |
| Laboratory | - | - |
| Practice | - | - |
| Field Study | - | - |
| Course Specific Internship (If Any) | - | - |
| Homework | 1 | 30 (%) |
| Presentation | - | - |
| Projects | - | - |
| Seminar | - | - |
| Quiz | - | - |
| Listening | - | - |
| Midterms | 1 | 40 (%) |
| Final Exam | 1 | 30 (%) |
| 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 | 3 | 42 |
| Midterms | 1 | 16 | 16 |
| 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 | 20 | 20 |
| 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 | P1 | P2 | P5 |
|---|---|---|---|---|
| O1 | Merkezi eğilim ölçüleri, değişkenlik ve olasılık dağılımları gibi temel istatistiksel kavramlar ve terminoloji hakkında bilgi sahibi olacaklardır. | 4 | 1 | 1 |
| O2 | Regresyon ve korelasyon gibi temel istatistiksel analiz ilke ve yöntemlerini açıklayabilir ve bunların iş karar verme sürecindeki önemini anlar. | 1 | 4 | 1 |
| O3 | Pazar araştırması anketleri, satış raporları ve mali tablolar gibi iş bağlamlarından toplanan veri setlerini analiz etmek ve yorumlamak için istatistiksel teknikleri uygulayabilirler. | 1 | 5 | 1 |
| O4 | Örneklem büyüklüğü, veri kalitesi ve istatistiksel anlamlılık gibi faktörleri göz önünde bulundurarak, belirli iş sorunlarını veya araştırma sorularını ele almada farklı istatistiksel yöntem ve tekniklerin uygunluğunu ve etkinliğini değerlendirebilirler. | 1 | 1 | 1 |
| O5 | Örüntüleri, eğilimleri ve ilişkileri belirlemek ve iş stratejileri ve kararları hakkında bilgi vermek üzere anlamlı iç görüler ve sonuçlar çıkarmak için istatistiksel verileri analiz edebilirler. | 1 | 1 | 4 |
| O6 | Gelecekteki iş trendlerini tahmin etmek için istatistiksel deneyler veya çalışmalar tasarlayabilir ve yürütebilir, istatistiksel ilkeleri uygularken yaratıcılık ve yenilikçilik sergileyebilir. | 1 | 1 | 5 |