• HOME
• ABOUT US
  • Name Address
  • General Description
  • Governance
  • Academic Calendar
  • Degree Programmes
  • List of Programmes in English
  • General Admission and Registration Procedures
  • Credit Mobility and Recognition of Prior Learning
  • ECTS Credit Allocation
  • Academic Guidance
  • Contacts
• Degree Programmes
  • Third Cycle Programmes (Doctorate Degree)
  • Second Cycle Programmes (Master's Degree)
  • First Cycle Programmes (Bachelor's Degree)
  • Short Cycle Programmes (Associate's Degree)
• GENERAL INFORMATION FOR STUDENTS
  • Cost of Living
  • Accommodation
  • Meals
  • Medical Facilities
  • Facilities for Disabled Students
  • Insurance
  • Financial Supports
  • Student Affairs Office
  • Practical Information for International Students
  • Language Courses
  • Work Placement Possibilities
  • Sports, Social and Cultural Facilities
  • Student Associations and Clubs
  • About İzmir - Turkey
• INTERNATIONAL RELATIONS
  • International Relations Office
  • Registration Procedures
  • Agreements
  • Coordinators
  • Documents
  • List of Programmes in English
  • ECTS / DS Labels

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS EMT 3016 REGRESSION ANALYSIS COMPULSORY 3 0 0 5

Offered By

Econometrics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

PROFESSOR DOCTOR ALI KEMAL ŞEHIRLIOĞLU

Offered to

Econometrics (Evening) Econometrics

Course Objective

Econometric analyzes of theoretical knowledge to teach the model.

Learning Outcomes of the Course Unit

1   To be able to determine the most appropriate model for the data set to work on 2   To be able to identify to check to assumptions of the model 3   To be able to determine whether the model is significantly 4   To be able to identify slingshot observations, effective Observation and high-impact points in the model 5   To be able to test pure error and lack of fit 6   To be able to analyze residuals and interpret the results

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Least Squares Regression and Assumptions 2 Quadratic Forms 3 F-test for the significance of regression 4 Pure error and lack of fit 5 Linear regression of Projection Geometry of Matrix 6 Linear regression of Projection Geometry of Matrix 7 The properties of projection matrix 8 Slingshot Observations, Effective Observation and High-Impact Points. 9 Slingshot Observations, Effective Observation and High-Impact Points. 10 Mid-term 11 Regression Diagnostics for a Single Observation 12 Residuals Generated on Measurements, Residual Analysis 13 Deleting a Single Point Measurements were created on the X-Y space 14 Impact Curves Generated Observations on 15 Observations on the character of the generated X Matrix, Transformations

Recomended or Required Reading

1. Applied Regression Analysis, Drapper and Smith, 2009. 2. Applied Regression Analysis, Rawlings, 2000.

Planned Learning Activities and Teaching Methods

This course will be presented using class lectures, class discussions, overhead projections, and demonstrations.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTE MIDTERM EXAM 2 MTEG MIDTERM GRADE MTEG * 1 3 FIN FINAL EXAM 4 FCGR FINAL COURSE GRADE MTEG * 0.40 + FIN * 0.60 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) MTEG * 0.40 + RST * 0.60 *** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

To be announced.

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 12 3 36 Preparation for midterm exam 1 25 25 Preparations before/after weekly lectures 12 2 24 Preparation for final exam 1 28 28 Midterm 1 1 1 Final 1 1 1 TOTAL WORKLOAD (hours) 115

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 PO.7 PO.8 PO.9 PO.10 LO.1 1 LO.2 1 LO.3 1 LO.4 1 LO.5 1 LO.6 1