# GAZI UNIVERSITY INFORMATION PACKAGE - 2019 ACADEMIC YEAR

COURSE DESCRIPTION
REGRESSION ANALYSIS/İST3002
 Course Title: REGRESSION ANALYSIS Credits 4 ECTS 6 Course Semester 6 Type of The Course Compulsory
COURSE INFORMATION
-- (CATALOG CONTENT)
-- (TEXTBOOK)
-- (SUPPLEMENTARY TEXTBOOK)
-- (PREREQUISITES AND CO-REQUISITES)
-- LANGUAGE OF INSTRUCTION
Turkish
-- COURSE OBJECTIVES
-- COURSE LEARNING OUTCOMES
The statistical textbooks which include latest information about statistics, equipment and other resources supported by scientific approach on undergr
Statisticians by using knowledge and skills acquired at bachelor degree level model, analyze, and interpret datasets.
Statisticians identify and analyze the problems with current developments in statistic and also develop solutions based upon researches and proofs.
Statisticians apply theoretical and practical knowledge acquired in Statistics at bachelor degree level to the current problems.
Statisticians have the ability to use computer software and computing technology at the certain level required by statistics field.
Statisticians take responsibility at disciplinary and interdisciplinary studies as an individual or a team member.
Statisticians must have knowledge and ability to follow development in the field of Statistics, and must develop life long-learning attitudes.
By using a foreign language, statistician can keep track of every statistical information, and communicate with colleagues.
Applying the statistical knowledge in the professional sense, statistician has social, scientific, and ethical values.
. A statistician must have the ability to social sensitivity and socialization.During the process of inference, a statistician uses time efficiently w

-- MODE OF DELIVERY
The mode of delivery of this course is Face to face
 --WEEKLY SCHEDULE 1. Week Main concepts and scatter diagram 2. Week Simple linear regression model and parameter estimation with the least squares method 3. Week Model assumptions, sum of squares, coefficient of determination and confidence intervals 4. Week Testing of hypothesis and application in LAB. 5. Week Testing of hypothesis and application in LAB. 6. Week Finding the parameter estimation and variances with matrix calculation 7. Week Multiple regression model and parameter estimation 8. Week Multiple correlation coefficient and statistical inferences for multiple regression model, MIDTERM EXAM 9. Week Statistical inferences for multiple regression models and application in LAB 10. Week Part and partial correlation coefficient , use of dummy variables 11. Week Weighted least squares method , examination of residual terms 12. Week Multicolinearity, correlation matrix 13. Week Methods of variables selection and application in LAB. 14. Week Autocorrelation and application in LAB. 15. Week FINAL EXAM 16. Week
-- TEACHING and LEARNING METHODS
-- ASSESSMENT CRITERIA
 Quantity Total Weighting (%) Midterm Exams 1 30 Assignment 2 10 Application 0 0 Projects 0 0 Practice 0 0 Quiz 0 0 Percent of In-term Studies 40 Percentage of Final Exam to Total Score 60