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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS EMT 2013 BASICS OF VARIANCE ANALYSIS ELECTIVE 3 0 0 4

Offered By

Econometrics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSOCIATE PROFESSOR MURAT TANIK

Offered to

Econometrics Econometrics (Evening)

Course Objective

Before deciding to perform the hypothesis test, obtaining some summary statistics and / or editing data for the proper use of statistical techniques and analysis.

Learning Outcomes of the Course Unit

1   Having quick and easy information about a particular data 2   The find extreme observations and understanding how to fix it 3   To use resistant methods 4   To be able to make it suitable for data extraction and analysis 5   To be able to visualize data

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Steam-leaf display 2 Letter Values 3 Box Plots 4 Transformation of Data 5 Resistant Lines 6 Analysis of Residuals 7 Transformation of variable for simplify reliationships 8 Midterm 9 Smoothing of the data 10 Median Polish 11 Rotograms 12 Visual Multivariate Techniques

Recomended or Required Reading

Velleman, P. F. ve Hoaglin, D. C. (1981), Applications, Basics and Computing of Exploratory Data Analysis, Dexbury Press, Boston

Planned Learning Activities and Teaching Methods

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 Preparations before/after weekly lectures 12 2 24 Preparation for midterm exam 1 20 20 Preparation for final exam 1 28 28 Midterm 1 1 1 Final 1 1 1 TOTAL WORKLOAD (hours) 110

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 1 LO.2 1 1 LO.3 1 1 LO.4 1 1 LO.5 1 1