PhD in Nutrition and Dietetics with a master's degree
PhD	TR-NQF-HE: Level 8	QF-EHEA: Third Cycle	EQF-LLL: Level 8

General course introduction information

Course Code:

BDY720

Course Name:

Advanced Biostatistics in Nutrition Research

Course Semester:

Spring

Course Credits:

Theoretical	Practical	Credit	ECTS
3	0	3	15

Language of instruction:

Course Requisites:

Does the Course Require Work Experience?:

Type of course:

Department Elective

Course Level:

PhD

TR-NQF-HE:8. Master`s Degree

QF-EHEA:Third Cycle

EQF-LLL:8. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator :

Dr.Öğr.Üyesi DUYGU AYDIN HAKLI

Course Lecturer(s):

Course Assistants:

Course Objective and Content

Course Objectives:	To comprehend and use the advanced biostatistics subjects.
Course Content:	Definition and basic concepts of biostatistics, data collection and data presentation techniques, Probability distributions, Sampling, Sampling error, Central tendency measures, Change measures, Hypothesis testing, Regression and correlation, Statistical Package Programs

Learning Outcomes

The students who have succeeded in this course;

Learning Outcomes

1 - Knowledge
Theoretical - Conceptual
1) Able to define the basic concepts of Biostatistics
2) Able to define data collection and data representation methods
2 - Skills
Cognitive - Practical
1) Able to explain types of variables
2) Able to define descriptive statistics
3) Able to use statistical software
4) Able to explain hypothesis testing
3 - Competences
Communication and Social Competence
Learning Competence
Field Specific Competence
1) Able to explain theoretical distributions
Competence to Work Independently and Take Responsibility
1) Able to make some hypothesis tests

Lesson Plan

Week	Subject	Related Preparation
1)	Science, scientific method, the concepts of statistics and biostatistics, basic concepts of Biostatistics	Following the Current Course Schedule
2)	Types of variables	Following the Current Course Schedule
3)	Data collection and data representation methods	Following the Current Course Schedule
4)	Descriptive statistics (measures of central tendency and dispersion)	Following the Current Course Schedule
5)	Theoretical distributions (normal, binomial and Poisson distributions)	Following the Current Course Schedule
6)	Statistical software and SPSS	Following the Current Course Schedule
7)	Basic concepts of hypothesis testing	Following the Current Course Schedule
8)	Hypothesis testing (Student?s T, Mann-Whitney U and Wilcoxon)	Following the Current Course Schedule
9)	Hypothesis testing (One way and Kruskal Wallis ANOVA)	Following the Current Course Schedule
10)	Hypothesis testing (Two way ANOVA)	Following the Current Course Schedule
11)	Hypothesis testing (Repeated measures ANOVA and Fridman test)	Following the Current Course Schedule
12)	Hypothesis testing (Chi-square tests)	Following the Current Course Schedule
13)	Regression and correlation analysis	Following the Current Course Schedule
14)	Regression and correlation analysis	Following the Current Course Schedule

Sources

Course Notes / Textbooks:	Zar, J.H. (1998). Biostatistical analysis. Prence Hall, London. 4th ed.
References:	Zar, J.H. (1998). Biostatistical analysis. Prence Hall, London. 4th ed.

Course-Program Learning Outcome Relationship

Learning Outcomes	1	3	2	4	6	7	5	8
Program Outcomes
1) To be able to defend original ideas in discussing the issues in the field, to analyze and develop social relations and norms that direct these relations with a critical point of view
1) Increase decision-making ability by developing rational solutions with creative and critical thinking method to new developments related to the field and possible problems.
2) To acquire the ability to develop, authenticate and deepen the level of expertise by researching, discussing and discussing more sophisticated and advanced information in an up-to-date and original manner in relation to the graduate education that the student has received.
3) Be able to evaluate and use new information in the field with a systematic approach.
5) To develop multidisciplinary study skills and to provide practical approaches to solve interdisciplinary problems and to be a leader in the team here.
7) to contribute to the solution of social, scientific, cultural and ethical problems encountered in the issues related to the moment, to make good use of the strategic decision-making processes in the solution of these problems and to contribute to the society in which it lives

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	To be able to defend original ideas in discussing the issues in the field, to analyze and develop social relations and norms that direct these relations with a critical point of view
1)	Increase decision-making ability by developing rational solutions with creative and critical thinking method to new developments related to the field and possible problems.
2)	To acquire the ability to develop, authenticate and deepen the level of expertise by researching, discussing and discussing more sophisticated and advanced information in an up-to-date and original manner in relation to the graduate education that the student has received.
3)	Be able to evaluate and use new information in the field with a systematic approach.
5)	To develop multidisciplinary study skills and to provide practical approaches to solve interdisciplinary problems and to be a leader in the team here.
7)	to contribute to the solution of social, scientific, cultural and ethical problems encountered in the issues related to the moment, to make good use of the strategic decision-making processes in the solution of these problems and to contribute to the society in which it lives

Learning Activity and Teaching Methods

Individual study and homework
Lesson
Application (Modelling, Design, Model, Simulation, Experiment etc.)

Assessment & Grading Methods and Criteria

Homework
Application

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Homework Assignments	1	% 20
Midterms	1	% 20
Final	1	% 60
total		% 100
PERCENTAGE OF SEMESTER WORK		% 40
PERCENTAGE OF FINAL WORK		% 60
total		% 100

Workload and ECTS Credit Grading

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	3	42
Study Hours Out of Class	14	3	42
Presentations / Seminar	1	1	1
Homework Assignments	1	1	1
Final	1	1	1
Total Workload			87