| Course Type | Compulsory Course |
|---|
| Course Level | Bachelor's Degree |
|---|
| Year of Study | 2 |
|---|
| Semester | Third Term (Fall) |
|---|
| ECTS | 6 |
|---|
| Name of Lecturer(s) |
|
| Learning Outcomes |
At the end of this course, the students;
- Define populations, samples, variables, data, and data collection methods and effectively use these methods.
- Create summary tables for qualitative and quantitative variables and analyze and interpret these tables.
- Present visual information for qualitative and quantitative variables using appropriate graphs.
- Calculate measures of central tendency and variability for quantitative variables.
- Understand probability rules and perform probability calculations.
- Carry out probability calculations for discrete and continuous random variables.
- Develop analytical thinking skills.
- Gain proficiency in using the SPSS program.
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | None |
|---|
| Course Description | This course covers fundamental concepts such as variables, frequency distributions, graphs, measures of central tendency and variability, skewness and kurtosis, the basic elements of probability, including random variables, independent events, conditional probability, and Bayes' theorem. The course also addresses topics such as univariate and bivariate distributions, discrete and continuous probability distributions, the law of large numbers, and the central limit theorem. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Introduction to statistics and its importance |
|
2nd Week |
Summarizing data obtained from qualitative and quantitative variables |
|
3rd Week |
Graphs for data obtained from qualitative and quantitative variables |
|
4th Week |
Measures of central tendency |
|
5th Week |
Measures of central tendency, measures of variability |
|
6th Week |
Measures of variability, outliers, and box plot |
|
7th Week |
Problem solving |
|
8th Week |
Midterm exam |
|
9th Week |
Introduction to probability concepts, permutations, combinations, bivariate probability, conditional probability, Bayes' theorem |
|
10th Week |
Random variables, discrete probability distributions |
|
11th Week |
Some discrete probability distributions (uniform, binomial, Poisson) |
|
12th Week |
Continuous probability distributions |
|
13th Week |
Normal distribution |
|
14th Week |
Problem solving |
|
| Recommended or Required Reading | Groebner, D. F., Shannon, P. W., Fry, P. C., & Smith, K. D. (2023). Business statistics: A decision-making approach (11th ed.). Pearson.
Cula, S., & Muluk, F. Z. (2010). Temel istatistik yöntemleri (2nd ed.). Başkent Üniversitesi. |
|---|
| Planned Learning Activities and Teaching Methods |
- Lecture
- Questions/Answers
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Mid-term |
1 |
30 |
| Quiz |
3 |
10 |
| Project |
1 |
15 |
| Total(%) |
|
55 |
| Contribution of In-term Studies to Overall Grade(%) |
|
55 |
| Contribution of Final Examination to Overall Grade(%) |
|
45 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
1 |
1 |
1 |
| Preparation for Quiz |
3 |
5 |
15 |
| Individual or group work |
14 |
3 |
42 |
| Preparation for Final exam |
1 |
40 |
40 |
| Course hours |
14 |
3 |
42 |
| Preparation for Midterm exam |
1 |
20 |
20 |
| Laboratory (including preparation) |
|
|
|
| Final exam |
1 |
1 |
1 |
| Homework |
|
|
|
| Project |
1 |
20 |
20 |
| Total Work Load | 181 |
| Total Work Load / 30 (h) | 6.03 |
| ECTS Credit of the Course | 6 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
