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
STA 5023 CATEGORICAL 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

PROFESSOR DOCTOR AYLIN ALIN

Offered to

Statistics
Statistics
STATISTICS

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

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,Preparing Individual Assignments 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, Preparing Individual Assignments
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 Definition of Generalized models and its components.
9 Logistic Regression Model for Binary Outcome Variable, the Interpretation of parameters, Goodness of Fit Test
10 Logistic Regression Model for Multiple Outcome Variable, Logistic Regression Model based on the Reference Category,Preparing Individual Assignments
11 Log-Linear Models for Two-Way Tables
12 Log-Linear Models for Three-Way Tables
13 Comparing Dependent Proportions
14 Measuring agrrement between observers

Recomended or Required Reading

Textbook:
A. Agresti, Categorical data analysis, Wiley, 2002.
Supplementary Books:
S. E. Fienberg, The analysis of cross-classified data, second edition,MIT press, 1994.
J. Neter, M.H. Kutner, C.J. Nachtsheim, W. Wasserman, Applied Linear statistical Models, Fourth edition, IRWIN, 1996.

Planned Learning Activities and Teaching Methods

Lecture and Homeworks

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 ASG 1 ASSIGNMENT 1
2 ASG 2 ASSIGNMENT 2
3 ASG 3 ASSIGNMENT 3
4 FIN FINAL EXAM
5 FCG FINAL COURSE GRADE ASG 1 + ASG 2 + ASG 3/3 * 0.40 + FIN * 0.60
6 RST RESIT
7 FCGR FINAL COURSE GRADE (RESIT) ASG 1 + ASG 2 + ASG 3/3 * 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

Evaluation of homework assignments and final exam.

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 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 14 3 42
Preparations before/after weekly lectures 14 2 28
Preparing assignments 3 20 60
Preparation for final exam 1 36 36
Final 1 2 2
TOTAL WORKLOAD (hours) 168

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
LO.7555
LO.8555

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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