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Description of Individual Course Units| Course Unit Code | Course Unit Title | Type of Course Unit | Year of Study | Semester | Number of ECTS Credits | | İŞ102 | Statistical Methods II | Elective | 1 | 2 | 6 |
| | Level of Course Unit | | Second Cycle | | Objectives of the Course | | Basic statistics methods should be taught to students in Mathematics Finance Program. While preparing the course topics; It was aimed to establish a balance between theory and practice. | | Name of Lecturer(s) | | Doç. Dr. Hakan PABUÇCU | | Learning Outcomes | | 1 | Build and analyze simple and multiple regression models | | 2 | Estimate the value of a financial asset for the near term. | | 3 | Technological design plans for a problem they create. | | 4 | Learns data types and appropriate statistical techniques. |
| | Mode of Delivery | | Normal Education | | Prerequisites and co-requisities | | none | | Recommended Optional Programme Components | | none | | Course Contents | | Central tendency / dispersion measures, statistical moments, maximum likelihood estimation, correlation and simple linear regression, multiple regression model, autocorrelation and multiple linkage, portfolio management, CAPM and ARMA approaches in regression models. | | Weekly Detailed Course Contents | |
| 1 | Importance of Statistical Methods such as Central Tendency and Dispersion Measures in the evaluation of classified or non-classified financial data | | | | 2 | Momentum as a measure of skewness and kurtosis for random variables | | | | 3 | Loss function, risk function and Least Squares and Maximum Likelihood Estimation of a parameter | | | | 4 | Pearson Correlation Sperman Row Correlation, Significance Tests and Confidence Interval for Stack Correlation | | | | 5 | Simple Linear Regression, Model Coefficient Estimates and Error Squares | | | | 6 | Dependent Variable Estimation, Significance Tests of Coefficients, Confidence Interval for Estimation, Coefficient of Determination | | | | 7 | Mid Term Exam | | | | 8 | Trend Analysis, Estimation of Y in Time Series | | | | 9 | Multiple Regression Model, Variance - Covariance Matrix, Significance of Regression Coefficients, ANOVA Test | | | | 10 | Multi-connection, Autocorrelation concept Von Neumann Test | | | | 11 | Portfolio Management, Portfolio Expected Return and Risk | | | | 12 | Risky asset, Risk Zero Asset, Beta coefficient, SHARPE, TREYNOR and JENSE indices | | | | 13 | Forecast and ARMA model with Moving Average Method | | | | 14 | Final Exam | | |
| | Recommended or Required Reading | | 1. Statistics and Finance, An introduction, David Ruppert, Springer Texts in statistics.
2. Mathematical Statistics, John E. Freund, Prentice/ Hall İnternational editions, Second edition
Diğer Kaynaklar 3. Methods and Applications of Statistics in Business, Finance and Management Science, N. Balakrishnan, Editor, Wiley Publication | | 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 | | | Work Placement(s) | | none |
| | Workload Calculation | |
| Midterm Examination | 3 | 1 | 3 | | Final Examination | 3 | 1 | 3 | | Attending Lectures | 11 | 7 | 77 | | Self Study | 11 | 7 | 77 | | Individual Study for Mid term Examination | 3 | 1 | 3 | | Individual Study for Final Examination | 3 | 1 | 3 | |
| Contribution of Learning Outcomes to Programme Outcomes | | LO1 | 1 | 1 | 3 | 1 | 1 | 2 | | LO2 | 4 | 5 | 5 | 4 | 5 | 5 | | LO3 | 3 | 5 | 3 | 3 | 3 | 5 | | LO4 | 4 | 4 | 4 | 4 | 4 | 5 |
| | * Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High |
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