| Course Unit Code | Course Unit Title | Type of Course Unit | Year of Study | Semester | Number of ECTS Credits | | EM203 | Statistics I | Compulsory | 2 | 3 | 4 |
|
| Level of Course Unit |
| First Cycle |
| Objectives of the Course |
| Introduction to probability theory and the teaching of basic statistical concepts |
| Name of Lecturer(s) |
| Doç. Dr. Hamid YILMAZ |
| Learning Outcomes |
| 1 | Raw data can prepare and can dismiss this frequency contours in a series of central tendency measures (average, median, mode) and can calculate the variable. | | 2 | Look at the series ' central tendency size and variability, visualize the histogram and box diagram of data, interpret the distribution of data. | | 3 | Calculate the momentum of a series, may interpret the kurtosis and skewness. | | 4 | Random deneylerdeki sample space and events as list and table and the chart, can understand. | | 5 | Discrete and continuous sample spaces, probability and conditional probability of events, calculating their. | | 6 | You can use the independence to calculate the possibilities and can determine whether events are independent. | | 7 | You can use Bayesian probability. | | 8 | Understands the concept of random variables | | 9 | Continuous probability density and discrete probability function can calculate the probabilities and possibility of continuous probability density and discrete probability function can determine. | | 10 | Are the cumulative distribution function is the cumulative distribution function of the possibilities and can determine the probability. |
|
| Mode of Delivery |
| Normal Education |
| Prerequisites and co-requisities |
| None |
| Recommended Optional Programme Components |
| None |
| Course Contents |
| Arrangement of data and analysis, introduction to probability, random variables, probability rules |
| Weekly Detailed Course Contents |
|
| 1 | Arranging input data and analysis of the Basic Concepts of statistics
| | | | 2 | Graphical and central tendency Measures
| | | | 3 | Variability and Asymmetry Size
| | | | 4 | Probability theory, then subtracted from one another and Combinations
| | | | 5 | Introduction To Probability, Some Probability Rules
| | | | 6 | Independent Events, Bayes Theorem
| | | | 7 | Random variables and Distributions
| | | | 8 | Midterm exam | | | | 9 | Two-dimensional Stochastic Variables
| | | | 10 | Two-dimensional Stochastic Variables (Continued)
| | | | 11 | The Expected Value Of Random Variables
| | | | 12 | Some Discrete Probability Distributions-I
| | | | 13 | Some Discrete Probability Distributions-II
| | | | 14 | Continuous Random Variables Distributions-I
| | | | 15 | Continuous Random Variables Distributions-II
| | |
|
| Recommended or Required Reading |
| AKDENİZ, FİKRİ., probability and statistics, 13. Printing |
| 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 | 3 | 42 |
| Self Study | 14 | 3 | 42 |
| Individual Study for Mid term Examination | 1 | 12 | 12 |
| Individual Study for Final Examination | 1 | 16 | 16 |
| Homework | 1 | 5 | 5 |
|
| Contribution of Learning Outcomes to Programme Outcomes |
| LO1 | 2 | 1 | 2 | 3 | 2 | 3 | 1 | 1 | | LO2 | 3 | 1 | 2 | 3 | 2 | 2 | 2 | 1 | | LO3 | 3 | 1 | 3 | 2 | 3 | 3 | 2 | 2 | | LO4 | 3 | 1 | 2 | 3 | 2 | 3 | 1 | 1 | | LO5 | 3 | 2 | 3 | 3 | 3 | 3 | 2 | 1 | | LO6 | 3 | 1 | 3 | 3 | 2 | 3 | 2 | 2 | | LO7 | 3 | 1 | 3 | 3 | 3 | 3 | 1 | 1 | | LO8 | 2 | 1 | 2 | 2 | 3 | 2 | 2 | 1 | | LO9 | 3 | 1 | 3 | 3 | 2 | 2 | 1 | 1 | | LO10 | 3 | 2 | 3 | 2 | 2 | 2 | 2 | 2 |
|
| * Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High |
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