COURSE UNIT TITLE

: CATEGORICAL DATA ANALYSIS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
IST 4036 CATEGORICAL DATA ANALYSIS COMPULSORY 2 2 0 6

Offered By

Statistics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSOCIATE PROFESSOR IDIL YAVUZ

Offered to

Course Objective

To teach the basic principles of categorical data analysis, the theory underlying the most popular categorical models, and to make the students to be able to apply them on data sets by statistical computer packages.

Learning Outcomes of the Course Unit

1   Defining categorical variable
2   Distinguishing the probability distributions used for categorical variables
3   Obtaining maximum likelihood estimators for Binomial, Multinomial and Poisson distributions
4   Testing statistical hypothesis for the parameters of Binomial and Multinomial distributions
5   Testing independence for two-way contingency tables
6   Calculating Sensitivity and Specificity, Difference of Two Proportions, Relative risk and Odds ratio values for two-way contingency tables
7   Building logistic regression model for binary and multiple outcome variables
8   Building log-linear model for two-way and three-way contingency tables

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Definition of Categorical Variable, Distributions for Categorical Variable (Binomial, Poisson and Multinomial Distribution), Likelihood functions and Maximum Likelihood estimators,
2 Maximum Likelihood estimators for Binomial parameter, Likelihood Ratio Test and Wald Test statistics for Binomial parameters, Interval Estimation for Binomial Parameters,
3 Maximum Likelihood Estimators for Multinomial Parameters, Pearson Chi-square and Likelihood Ratio test statistics for a given multinomial distribution
4 Probability Structure for Contingency Tables, Sensitivity and Specificity,Independence of Categorical Variables, Probability Distributions for Two-Dimensional Tables (Poisson Distribution, Mulitnomial Distribution and Independent Multinomial Distribution)
5 Types of Study, Difference of two proportions for 2x2 Dimensional Tables, Relative Risk and Odds Ratio Calculations
6 I x J Boyutlu Tablolar Için Local Odds Tario, Uncertainty Coefficient and Gamma Coefficient Calculations
7 Odds Ratio, Interval Estimation for Relative Risk and Difference of two Proportions, Test of Independence for Two-way Contingency Tables (with the Pearson Chi-square and Likelihood Ratio Test Statistics), Examinations of Residuals
8 Midterm exam
9 Logistic Regression Model for Binary Outcome Variable, the Interpretation of parameters, Goodness of Fit Test
10 Binary Logistic Regression Model Selection
11 Logistic Regression Model for Multiple Outcome Variable, Logistic Regression Model based on the Reference Category
12 Log-Linear Models for Two-Way Tables
13 Log-Linear Models for Three-Way Tables
14 Goodness of Fit Test for Log-Linear Models for Three-Way Tables

Recomended or Required Reading

Textbook:
D. A. Powers,Statistical Methods for Categorical data Analysis,1999

Supplementary Books:
D.W.Hosmer, S.Lemeshow, Applied Logistic Regression 2nd ed., 2001.
J. Neter, M.H. kutner, C.J. Nachtsheim, W. Wasserman, Applied Linear statistical Models, Fourth edition, 1996.

Planned Learning Activities and Teaching Methods

Lecture, project assignment, problem-solving.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 ASG ASSIGNMENT
3 FIN FINAL EXAM
4 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.40 + ASG * 0.20 + FIN * 0.40
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.40 + ASG * 0.20 + RST * 0.40


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

Evaluation of exams and homework.

Language of Instruction

Turkish

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 Fen Fakültesi Istatistik Bölümü
e-mail: aylin.alin @deu.edu.tr
Tel: 0232 301 85 72

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 4 52
Preparations before/after weekly lectures 13 1 13
Preparation for midterm exam 0 0 0
Preparation for final exam 1 50 50
Preparing assignments 7 4 28
Final 1 2 2
Midterm 0 0 0
Project Assignment 0 0 0
TOTAL WORKLOAD (hours) 145

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14
LO.155443
LO.255443
LO.3555443
LO.4555443
LO.55554443
LO.65554443
LO.75554443
LO.85554443

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

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