Description of Individual Course Units
Course Unit CodeCourse Unit TitleType of Course UnitYear of StudySemesterNumber of ECTS Credits
İST301REGRESSION ANALYSISCompulsory357
Level of Course Unit
First Cycle
Objectives of the Course
The aim of this course is to gain the students the ability of creating a model in case of a functional relation between variables, analysing the created model, estimation of the parameters related to the model and making statistical inference related to the data obtained from different sources.
Name of Lecturer(s)
Prof. Dr. Onur KÖKSOY
Learning Outcomes
1To be able to comprehend the structure of the statistical model.
2To be able to make estimation of a lineer model.
3To be able to be aware of model appropriateness control.
4To be able to be aware of model validation.
5To be able to examine the validity of the assumption of normality.
6To be able to interpret the correlation coefficient
7To be able to analyze any given data set under regression model assumptions
Mode of Delivery
Face to Face
Prerequisites and co-requisities
None
Recommended Optional Programme Components
None
Course Contents
Estimation of the model and its parameters, The methods of finding estimaters and the characteristics of least square estimators, The coefficient validity control, confidence interval and hypothesis testing, Lack of fit, residual analysis and lack of fit test and graphics of residuals, Inspection of normality with steam and leaf and box graphs, Coefficient of determination and correlation, Multiple lineer regression models, Matrix approach in least square estimation, Estimation of confidence interval and parameters in multiple lineer regression models, Usage of dummy variables, criterion in choosing variables, model transformation, Multiple dependency and correlation, Case studies
Weekly Detailed Course Contents
WeekTheoreticalPracticeLaboratory
1Model creating and parameter estimation
2Estmimator finding techniques and least square estimators
3The coefficient validity control, confidence interval and hypothesis testing
4Lack of fit test and graphics of residuals
5Steam and leaf and box graphs.
6Coefficient of determination and correlation
7Multiple lineer regression models
8Mid-term Examination
9Matrix approach in least square estimation
10Multiple lineer regression models and estimation of parameters
11Multiple lineer regression models, interval estimation and hypothesis testing
12Dummy variables, criterion in choosing variables, model transformation
13Multiple dependency and correlation
14Case studies
15Case studies
16Final Examination
Recommended or Required Reading
Arnold, M. “Introduction to Probability and Statistics” Smith, D., F., “Applied Regression Analysis” Wasserman, N., F., “Applied Linear Statistical Models” Paul Newbold, William L. Carlson, Betty M. Thorne, “Statistics for Business and Economics”
Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"
Assessment Methods and Criteria
Term (or Year) Learning ActivitiesQuantityWeight
Midterm Examination1100
SUM100
End Of Term (or Year) Learning ActivitiesQuantityWeight
Final Examination1100
SUM100
Term (or Year) Learning Activities40
End Of Term (or Year) Learning Activities60
SUM100
Language of Instruction
English
Work Placement(s)
None
Workload Calculation
ActivitiesNumberTime (hours)Total Work Load (hours)
Midterm Examination122
Final Examination122
Quiz144
Attending Lectures14456
Question-Answer248
Team/Group Work3618
Project Preparation13232
Self Study3515
Individual Study for Mid term Examination13535
Individual Study for Final Examination14040
TOTAL WORKLOAD (hours)212
Contribution of Learning Outcomes to Programme Outcomes
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LO155 4444  5  4    44    4
LO255 4444  5  4    44    4
LO355 4444  5  4    44    4
LO455 4444  5  4    44    4
LO555 4444  5  4    44    4
LO655 4444  5  4    44    4
LO755 4444  5  4    44    4
* Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High
 
Ege University, Bornova - İzmir / TURKEY • Phone: +90 232 311 10 10 • e-mail: intrec@mail.ege.edu.tr