COURSE UNIT TITLE

: APPLIED BIOMETRY

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
CDK 6003 APPLIED BIOMETRY ELECTIVE 2 0 0 7

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSOCIATE PROFESSOR EYÜP MÜMTAZ TIRAŞIN

Offered to

MARINE LIVING RESOURCES
MARINE LIVING RESOURCES

Course Objective

One of the essential goals of the research workers in the fields of marine living resources is to analyse and evaluate the data on organisms and their environment, and subsequently obtain scientific results with high precision and accuracy. This course aims to teach graduate students various biostatistical methods, in particular the general linear models, that they can use during their own research. The course also incorporates the introduction of some contemporary statistical software packages to give students a better perspective on the several applications of biostatistics.

Learning Outcomes of the Course Unit

1   Recognize the need for sampling design and biostatistical analysis in research on organisms and their environment.
2   Demonstrate an ability to formulate statistical hypotheses and design experiments or research surveys.
3   Be able to collect biological and environmental data from ecosystems and make them ready for advanced biostatistical analysis.
4   Compute all sorts of sample statistics from the collected data and prepare various complicated graphics visualizing the information contained by the data.
5   Apply advanced biostatistical analysis methods based on the general linear models to make inferences about the collected data and draw meaningful and valid conclusions.
6   Become familiar and able to use certain contemporary statistical software such as R (R Project for Statistical Computing).
7   Demonstrate critical thinking skills in evaluation of their own research work and that of other researchers.
8   Demonstrate an ability to interpret, discuss and publish the results from own research work.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction Course outline, textbooks, and reference books. General review of the essential topics to be covered in the course. Definitions.
2 ANOVA - I Independent t-test. Paired (Dependent) t-test. F Distribution. Experimental design and ANOVA. Why ANOVA
3 ANOVA - II One-way ANOVA. Model I and Model II ANOVA.
4 ANOVA - III One-way Model I ANOVA. Planned comparisons. Least significant difference.
5 ANOVA - IV Q distribution. Unplanned (multiple) comparisons. One-way Model II ANOVA. Variance components.
6 ANOVA - V Two-way Model I ANOVA. Concept of interaction. Two-way Model II ANOVA. Two-way Model III ANOVA.
7 ANOVA - VI Hierarchical (nested) ANOVA. Two-way ANOVA without replication. Random blocks. Latin squares. Multi-way ANOVA.
8 Midterm exam
9 Regression - I Functional relationships. Simple linear regression analysis.
10 Regression - II Simple linear regression and one-way Model I ANOVA. Nonlinearity. Examination of residuals.
11 Regression - III Model I and Model II regression. Multiple regression.
12 ANCOVA Linear regression analysis and ANOVA. ANCOVA. General linear model.
13 Data transformations Transformations of the data for linear regression and ANOVA. Types of transformations. Nonparametric ANOVA and regression.
14 Correlation Correlation. Principal components analysis.

Recomended or Required Reading

Textbooks:

Snedecor, G. W. and Cochran, W. G., 1989. Statistical Methods (8th edition). Iowa State University Press. Ames, Iowa, USA.
Sokal, R. R. and Rohlf, F. J., 2012. Biometry (4th edition). W. H. Freeman Co., New York, USA.
Zar, J. H., 2010. Biostatistical Analysis (5th edition). Pearson Prentice-Hall, New Jersey, USA.

Reference books:

Quinn, G. P. and Keough, M. J., 2002. Experimental Design and Data Analysis for Biologists. Cambridge University Press, UK.
Shahbaba, B., 2012. Biostatistics with R. An Introduction to Statistics through Biological Data. Springer, New York, USA.

Planned Learning Activities and Teaching Methods

Class lectures are carried out in a highly interactive format. The instructor prompts students for response to questions posed and solicits their thoughts on issues discussed. Lectures will focus on the transfer of biostatistical concepts and techniques rather than rigorous mathematics. The emphasis will be put on the real world applications and the examples will be chosen in a way to resemble biostatistical problems that students might face in preparation of a master's or doctoral thesis. Individual participation by students in classroom discussions will be strongly encouraged.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 FIN FINAL EXAM
3 FCG FINAL COURSE GRADE MTE * 0.40 + FIN * 0.60
4 RST RESIT
5 FCGR FINAL COURSE GRADE (RESIT) MTE * 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

Regular participation in classes is essential. Students are responsible for all the topics covered in the lessons including those that they were unable to attend.

Contact Details for the Lecturer(s)

Dr. E. Mümtaz TIRAŞIN
Dokuz Eylül University, Institute of Marine Sciences and Technology,
Inciraltı 35340, Balçova - Izmir.
Phone:(+90) 232 2785565 /165
Fax: (+90) 232 2785082
E-mail: mumtaz.tirasin@deu.edu.tr

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 2 26
Preparations before/after weekly lectures 12 5 60
Reading 12 4 48
Preparation for midterm exam 1 10 10
Preparation for final exam 1 15 15
Midterm 1 3 3
Final 1 4 4
TOTAL WORKLOAD (hours) 166

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8
LO.152533222
LO.252525323
LO.353534223
LO.453534322
LO.552523322
LO.643423222
LO.742535535
LO.842545535