COURSE UNIT TITLE

: EXPLORATORY DATA ANALYSIS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
STA 5065 EXPLORATORY DATA ANALYSIS ELECTIVE 3 0 0 7

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASISTANT PROFESSOR ENGIN YILDIZTEPE

Offered to

Statistics
STATISTICS

Course Objective

The objective of this course is to cover modern techniques in data analysis, including stem-and-leafs, boxplots, resistant lines, smoothing and median polish.

Learning Outcomes of the Course Unit

1   Construct and interpret a histogram
2   Understand the dual role of exploratory and confirmatory approaches to data analysis
3   Develop a strategy for data analysis
4   Interpret the results of quantitative analyses
5   Develop quantitative graphics for inclusion in papers and thesis
6   Use Stata, Sas, R exc. for data management

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Review of basic statistics
2 Review of probability, histograms
3 Boxplots and batch comparison
4 Transforming data
5 Analysis of two-way tables by medians
6 Median polish
7 Nonadditivity and the diagnostic plot
8 Midterm
9 Examining residuals
10 Residuals and the fit
11 Comparing location estimators: Trimmed means, medians and trimean
12 Choosing the robust estimator, homework
13 Parameter estimation of parametric distributions, error analysis
14 Monte Carlo techniques bootstrap, jackknife, cross-validation

Recomended or Required Reading

Textbook(s):
D. C. Hoaglin, F. Mosteller, J. W. Tukey, Understanding Robust And Exploratory Data Analysis, 2000.
Martinez, W.L. and A.R. Martinez, Computational statistics handbook with MATLAB, Chapman & Hall/CRC, 2002.
Tukey J. W. Exploratory Data Analysis, Addison Wesley, 1977.

Planned Learning Activities and Teaching Methods

The course consists of lecture and homework.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 ASG ASSIGNMENT
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.30 +ASG * 0.20 +FIN * 0.50
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + ASG * 0.20 + RST * 0.50

Further Notes About Assessment Methods

None

Assessment Criteria

Evaluation of exams and homework.

Language of Instruction

English

Course Policies and Rules

Attendance to at least 70% for the lectures is an essential requirement of this course and is the responsibility of the student. It is necessary that attendance to the lecture and homework delivery must be on time. Any unethical behavior that occurs either in presentations or in exams will be dealt with as outlined in school policy. You can find the undergraduate policy at http://web.deu.edu.tr/fen.

Contact Details for the Lecturer(s)

DEU. Faculty Sciences Department of Statistics B003
e-mail: selma.erdogan@deu.edu.tr
Tel: 0232 301 85 71

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparation for midterm exam 1 40 40
Preparation for final exam 1 45 45
Preparing assignments 1 25 25
Preparations before/after weekly lectures 14 1 14
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 170

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.1555
LO.2555
LO.3555
LO.4555
LO.5555
LO.6555

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

Copyright 2012 DEÜ. BİD

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