| Course Name |
Statistics for Business & Economics
|
|
Code
|
Semester
|
Theory
(hour/week) |
Application/Lab
(hour/week) |
Local Credits
|
ECTS
|
|
ECON 280
|
Fall
|
2
|
2
|
3
|
6
|
| Prerequisites |
None
|
|||||
| Course Language |
English
|
|||||
| Course Type |
Required
|
|||||
| Course Level |
First Cycle
|
|||||
| Mode of Delivery | - | |||||
| Teaching Methods and Techniques of the Course | Problem SolvingApplication: Experiment / Laboratory / WorkshopLecture / Presentation | |||||
| National Occupation Classification | - | |||||
| Course Coordinator | ||||||
| Course Lecturer(s) | ||||||
| Assistant(s) | ||||||
| Course Objectives | This course aims to provide students in business and economics fields with a solid foundation in probability and statistics, teaching them the necessary statistical methods and tools for data analysis and interpretation, and enabling their effective application in real-world business and economic contexts. Thus, students will be able to understand probability and statistics theoretically and also use them in practice to solve business and economic problems. |
| Learning Outcomes |
The students who succeeded in this course;
|
| Course Description | This course, designed for business faculty students, dives into probability essentials, covering discrete and continuous distributions and sampling distribution creation. It sharpens skills in estimating confidence intervals and in conducting hypothesis testing for single and dual population scenarios. Bridging theory with practical business and economic applications, the course prepares students to apply statistical tools in decision-making and strategic analysis, readying them to face real-world business challenges confidently. |
| Related Sustainable Development Goals |
|
|
|
Core Courses | |
| Major Area Courses | ||
| Supportive Courses | ||
| Media and Management Skills Courses | ||
| Transferable Skill Courses |
| Week | Subjects | Related Preparation |
| 1 | Introduction to Data: Intro, Case Study; Data Basics, Sampling Principles and Strategies Experiments | Newbold, Carlson & Thorne Chapter 1 Cetinkaya-Rundel, M., Diez, D., & Barr, C. (2019). OpenIntro Statistics. Chapter 1 |
| 2 | Summarizing data: Examining numerical data and categorical data | Newbold, Carlson & Thorne Chapter 2 OpenIntro Statistics. Chapter 2 |
| 3 | Probability: Defining Probability | Newbold, Carlson & Thorne Chapter 3.1-3.2 OpenIntro Statistics. Chapter 3.1 |
| 4 | Probability: Conditional Probability, Bivariate Probabilities and The Bayes Theorem | Newbold, Carlson & Thorne Chapter 3.3-3.5 OpenIntro Statistics. Chapter 3.2 |
| 5 | Probability: Sampling, Random Variables, Discrete Distributions, Continuous Distributions | Newbold, Carlson & Thorne Chapters 4.1-4.3, 5.1-5.2 OpenIntro Statistics. Chapter 3.3 & 3.4 & 3.5 |
| 6 | Distributions of Random Variables: Continuous Distributions, Uniform Distribution, Normal Distribution | Newbold, Carlson & Thorne Chapters 5.1- 5.3 OpenIntro Statistics. Chapters 4.1 |
| 7 | Distributions of Random Variables: Normal Dist. Cont’d., Discrete Distributions: Binomial Distribution | Newbold, Carlson & Thorne Chapters 4.4, 4.6, 5.4 OpenIntro Statistics. Chapters 4.1, 4.3 |
| 8 | Distributions of discrete RVs: Poisson distribution and review before exam | Newbold, Carlson & Thorne Chapter 4.5 OpenIntro Statistics. Chapter 5.1 |
| 9 | Midterm Exam | Midterm Exam |
| 10 | Foundations for Inference: Point Estimates and Sampling Variability | Newbold, Carlson & Thorne Chapter 6 OpenIntro Statistics. Chapter 5.2 |
| 11 | Foundations for Inference: Confidence Intervals for a Proportion and for a Population Mean | Newbold, Carlson & Thorne Chapters 7.2, 7.4, 7.7-7.8 OpenIntro Statistics. Chapter 5.3 |
| 12 | Foundations for Inference: Hypothesis Testing: Single Population Proportion, Single Population Mean with known Variance, Decision Errors, P-value, Statistical Significance | Newbold, Carlson & Thorne Chapters 9.1, 9.2, 9.4 OpenIntro Statistics. Chapter 6.1 & 6.2 |
| 13 | Hypothesis Testing and Inference: The Chi-square Distribution, Tests of Variance, Testing for goodness of fit using chi-square; Chi-Square test of Independence | Newbold, Carlson & Thorne Chapter 9.6 OpenIntro Statistics. Chapter 6.3 & 6.4 |
| 14 | Hypothesis Testing and Inference: The Student’s t-Distribution, Single Population Mean with unknown variance | Newbold, Carlson & Thorne Chapter 9.3 OpenIntro Statistics. Chapter 7.1.1-7.1.3 |
| 15 | Hypothesis Testing, CI and Inference: Difference between two population means with known or unknown variance, Difference between two proportions | Newbold, Carlson & Thorne Chapters 10.3, 8.1, 8.3 OpenIntro Statistics. Chapter 7.1.4, 7.1.5 & 7.2 |
| 16 | Final Exam | Final Exam |
| Course Notes/Textbooks | Cetinkaya-Rundel, M., Diez, D., & Barr, C. (2019). OpenIntro Statistics. (Fourth Edition ed.) OpenIntro, Inc. https://www.openintro.org/book/os/ Newbold P., Carlson W.L., Thorne B., Statistics for Business and Economics, 10th edition (Pearson |
| Suggested Readings/Materials | Lind D., Marchal S., Statistical Techniques in Business & Economics, 17th edition (McGraw-Hill, 2017), ISBN-13: 978-1259666360 |
| Semester Activities | Number | Weigthing |
| Participation |
1
|
5
|
| Laboratory / Application |
1
|
10
|
| Field Work | ||
| Quizzes / Studio Critiques |
1
|
20
|
| Portfolio | ||
| Homework / Assignments | ||
| Presentation / Jury | ||
| Project |
1
|
15
|
| Seminar / Workshop | ||
| Oral Exams | ||
| Midterm |
1
|
25
|
| Final Exam |
1
|
25
|
| Total |
| Weighting of Semester Activities on the Final Grade |
5
|
70
|
| Weighting of End-of-Semester Activities on the Final Grade |
1
|
30
|
| Total |
| Semester Activities | Number | Duration (Hours) | Workload |
|---|---|---|---|
| Theoretical Course Hours (Including exam week: 16 x total hours) |
16
|
2
|
32
|
| Laboratory / Application Hours (Including exam week: '.16.' x total hours) |
16
|
2
|
32
|
| Study Hours Out of Class |
16
|
1.5
|
24
|
| Field Work |
0
|
||
| Quizzes / Studio Critiques |
1
|
12
|
12
|
| Portfolio |
0
|
||
| Homework / Assignments |
0
|
||
| Presentation / Jury |
0
|
||
| Project |
1
|
24
|
24
|
| Seminar / Workshop |
0
|
||
| Oral Exam |
0
|
||
| Midterms |
1
|
24
|
24
|
| Final Exam |
1
|
32
|
32
|
| Total |
180
|
|
#
|
Program Competencies/Outcomes |
* Contribution Level
|
|||||
|
1
|
2
|
3
|
4
|
5
|
|||
| 1 |
To be able to identify and analyze problems in the field of trade and finance, and to develop solutions. |
-
|
-
|
X
|
-
|
-
|
|
| 2 |
To be able to apply theoretical and practical knowledge of international trade and finance to real-world professional contexts. |
-
|
-
|
-
|
X
|
-
|
|
| 3 |
To be able to critically analyze global market developments and evaluate their implications for business and policy. |
-
|
-
|
-
|
-
|
-
|
|
| 4 |
To be able to collect, analyze, and interpret financial and economic data by using digital and information technologies effectively. |
-
|
-
|
X
|
-
|
-
|
|
| 5 |
To be able to understand and interpret legal frameworks, regulations and practices relevant to international trade and finance. |
-
|
-
|
-
|
-
|
-
|
|
| 6 |
To be able to anticipate, define, and manage financial and trade-related risks through informed decision-making. |
-
|
-
|
-
|
-
|
-
|
|
| 7 |
To be able to acquire and use verbal, written, and numerical skills effectively for the nature of international trade and finance program. |
-
|
-
|
-
|
-
|
X
|
|
| 8 |
To be able to obtain, synthesize, and report trade- and finance-related information clearly and effectively. |
-
|
-
|
-
|
-
|
-
|
|
| 9 |
To be able to contribute effectively as individuals, team members, and leaders in multidisciplinary environments. |
-
|
-
|
-
|
-
|
-
|
|
| 10 |
To be able to evaluate trade and finance issues from ethical, social, and sustainability perspectives. |
-
|
-
|
-
|
-
|
-
|
|
| 11 |
To be able to collect data in the areas of International Trade and Finance and communicate with colleagues in a foreign language ("European Language Portfolio Global Scale", Level B1). |
-
|
-
|
-
|
-
|
-
|
|
| 12 |
To be able to speak a second foreign at a medium level of fluency efficiently. |
-
|
-
|
-
|
-
|
-
|
|
| 13 |
To be able to relate the knowledge accumulated throughout human history to their field of expertise. |
-
|
-
|
-
|
-
|
-
|
|
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest
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