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Course Information

Course Information

Course Title	Code	Semester	L+U Hour	Credits	ECTS
REGRESSION ANALYSIS	İST307		0 + 0	3.0	5.0

Prerequisites

None

Language of Instruction	Turkish
Course Level	Graduate Degree
Course Type	Compulsory
Mode of delivery	Reciting topics and their applications, home works.
Course Coordinator
Instructors
Assistants
Goals	Building the fundamentals for future undergraduate and grduate courses, analysis of time series data, modeling and statistical inference
Course Content	Modeling principles in time series, reliability analysis, risk analysis, stochastic financial analysis, stochastic processes. Difference equation, differential-difference equations, stochastic differential equations, ito formulation, functions of complex numbers (or functions in complex systems) and statistical applications, essentials of modeling on linear and nonlinear optimization, model assessments.
Learning Outcomes	1) Gets knowledge about conditional expected value and regression concept, normal distribution and its properties and also investigates relations between of them. 2) Obtains information about matrix notation, properties of least squares estimators of parameters, BLUE estimators, prediction and residuals. 3) Makes interpretation about preparing ANOVA table and scrutinizing of model assumptions (residual analyse). 4) Scrutinizes heteroscedasticity and weighted least squares method, autocorrelation, normal probability plot, Box-Cox transformation methods and necessary states of them. 5) Make confidence intervals and hypotesis tests about model parameters. 6) Obtains estimators of regression parameters and scrutinizes properties in multi-regression analyse. 7) Applies choosing the best model through the methods such as stepwise methods, AIC, BIC and Cp statistics. 8) Gets information about deriving distribution of quadratic forms and using them for regreesion analysis process. 9) Applies Ridge regression for multicollinearity problem and eliminates heteroscedasticity besides the methods. 10) Understands basic descriptions about polinomial, Logistic and Poisson regression.