| Course Unit Code | Course Unit Title | Type of Course Unit | Year of Study | Semester | Number of ECTS Credits | | MA116.1B2 | Statistics | Elective | 1 | 2 | 3 |
|
| Level of Course Unit |
| Short Cycle |
| Objectives of the Course |
| Both companies, as well as employees in various organizations have felt the need to take the best decision that the data, collect, acquire the ability to be processed and stored. |
| Name of Lecturer(s) |
| Öğr. Gör. Adem IRMAK |
| Learning Outcomes |
| 1 | Students learn data collection methods and develop their ability to analyze real-world data. This includes topics such as collecting data through surveys, experiments or observations, organizing, visualizing and interpreting the data. | | 2 | Students understand the basic concepts and terminology used in statistics. In this context, topics such as central tendency measures such as mean, median, mode, terms that measure variability such as variance and standard deviation, and probability distributions are included. | | 3 | By understanding probability theory, they can calculate the probabilities of random events. They can recognize and apply probability distributions such as binomial, normal and Poisson. | | 4 | Students can construct confidence intervals to determine the reliability of conclusions drawn from the data. This includes the ability to understand and interpret the impact of factors such as sample size and standard deviation. | | 5 | They may use regression and correlation analysis to examine the relationship between two or more variables. This includes techniques such as linear regression, multiple regression, Pearson and Spearman correlation coefficients. |
|
| Mode of Delivery |
| Normal Education |
| Prerequisites and co-requisities |
| None |
| Recommended Optional Programme Components |
| None |
| Course Contents |
| Description of statistics, basic concepts, data types and collection methods, organization of data, measures of central tendency, dispersion measurement, estimation theory, correlation analysis, regression analysis is aimed to teach and indexes |
| Weekly Detailed Course Contents |
|
| 1 | Basic concepts | | | | 2 | Statistical Data | | | | 3 | Statistics Series | | | | 4 | Graphics | | | | 5 | Parametric Measures of Central Tendency | | | | 6 | Nonparametric Measures of Central Tendency | | | | 7 | Parametric Measures of Variability | | | | 8 | Midterm Exam | | | | 9 | Nonparametric Measures of Variability | | | | 10 | Figure Dimensions | | | | 11 | indexes | | | | 12 | Probability Theory | | | | 13 | Discrete Random Variables and Their Distributions | Work Area Applications | | | 14 | Continuous Random Variables and Normal Distribution | Work Area Applications | | | 15 | Approximation of Discrete Probability Distributions to Normal Distribution | | |
|
| Recommended or Required Reading |
| Oktay, Erkan (2012). Introduction to Statistics. Atatürk University Open Education Faculty Publication, Erzurum. ISBN: 978-975-442-213-9 |
| Planned Learning Activities and Teaching Methods |
|
| Assessment Methods and Criteria | |
| Midterm Examination | 1 | 100 | | SUM | 100 | |
| Final Examination | 1 | 100 | | SUM | 100 | | Term (or Year) Learning Activities | 40 | | End Of Term (or Year) Learning Activities | 60 | | SUM | 100 |
| | Language of Instruction | | Turkish | | Work Placement(s) | | None |
|
| Workload Calculation |
|
| Midterm Examination | 1 | 1 | 1 |
| Final Examination | 1 | 2 | 2 |
| Attending Lectures | 14 | 2 | 28 |
| Self Study | 14 | 2 | 28 |
| Individual Study for Mid term Examination | 7 | 1 | 7 |
| Individual Study for Final Examination | 14 | 2 | 28 |
|
| Contribution of Learning Outcomes to Programme Outcomes |
| LO1 | 3 | 3 | 2 | 3 | 1 | 4 | 1 | 2 | 3 | | LO2 | 4 | 4 | 3 | 4 | 3 | 5 | 2 | 3 | 2 | | LO3 | 2 | 3 | 3 | 2 | 1 | 4 | 2 | 3 | 2 | | LO4 | 2 | 5 | 5 | 3 | 2 | 5 | 2 | 3 | 3 | | LO5 | 3 | 4 | 4 | 3 | 3 | 5 | 2 | 2 | 2 |
|
| * Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High |
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