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 |
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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) |
Dr. Öğr. Üyesi Kübra ELMALI |
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. |
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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 |
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1 | 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 | Trend Analysis, Estimation of Y in Time Series | | | 8 | Mid Term Exam | | | 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 model with Moving Average Method | | | 14 | Final Exam | | | 15 | ARMA model | | | 16 | Fınal exam | | |
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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 |
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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 |
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Workload Calculation |
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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 |
Homework | 2 | 5 | 10 |
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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 |
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* Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High |
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