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Course Information

Course Title	Code	Semester	Laboratory+Practice (Hour)	Pool	Type	ECTS
BIOSTATISTICS	SBE510	FALL-SPRING	2+0	Faculty	E	6

Learning Outcomes	1-Accesses different data sources. 2-Gains the ability to analyze data 3-Uses general purpose account analysis programs for data analysis.

ECTS / WORKLOAD

Activity	Percentage (100)	Number	Time (Hours)	Total Workload (hours)
Course Duration (Weeks x Course Hours)		14	2	28
Classroom study (Pre-study, practice)		10	5	50
Assignments	0	0	0	0
Short-Term Exams (exam + preparation)	0	0	0	0
Midterm exams (exam + preparation)	40	1	30	30
Project	0	0	0	0
Laboratory	0	0	0	0
Final exam (exam + preparation)	60	1	60	60
	0	0	0	0
Total Workload (hours)				168
Total Workload (hours) / 30 (s)				5,6 ---- (6)
ECTS Credit				6

Course Content

Week	Topics	Study Metarials
1	Biostatistics basic concepts	R1 Lecture notes
2	Data types	R1 Lecture notes
3	Pivottable an graphs, Frequency tables, descriptive statistics	R1 Lecture notes
4	Introduction to sampling theory	R1 Lecture notes
5	Probability and basic concepts	R1 Lecture notes
6	Normal distribution	R1 Lecture notes
7	Applications of normal distribution	R1 Lecture notes
8	Introduction to hypothesis tests	R1 Lecture notes
9	Paramatric tests and its applications 1	R1 Lecture notes
10	Paramatric tests and its applications 2	R1 Lecture notes
11	Paramatric tests and its applications 3	R1 Lecture notes
12	Confidence intervals	R1 Lecture notes
13	Regression and correlations analysis 1	R1 Lecture notes
14	Regression and correlations analysis 2	R1 Lecture notes

Prerequisites	-
Language of Instruction	Turkish
Responsible	Assoc. Prof. Tuba KOÇ
Instructors	1-)Doçent Dr. Tuba Koç
Assistants	-
Resources	R1-Lecture notes
Supplementary Book	SR1- İkiz, F., Püskülcü, H., Eren, Ş. İstatistiğe Giriş. Fakülteler Kitapevi, 2006.
Goals	Evaluating diffrent data base and analyzing data In Studies with problem and Practice on well-known statistical packet program.
Content	Basic statistical knowledge, type of data, table and graphics, frequency tables, descriptive statistics, sampling, probability, type of hypothesis, one and two sample test, confidence interval, regression, correlation.

Program Learning Outcomes

	Program Learning Outcomes	Level of Contribution
1	To be able to comprehend the interdisciplinary interaction related to the field.	2
2	To be able to solve the problems related to the field by using research methods.	-
3	To be able to comment and create new knowledge by integrating the knowledge gained in the field with information from different disciplines.	3
4	To be able to solve the problems related to the field by using research methods.	-
5	To be able to conduct an independent study related to the field.	3
6	To be able to develop new strategic approaches for solving complex problems encountered in applications related to the field and to be able to produce solutions by taking responsibility.	-
7	To be able to give leadership in environments that require solving problems related to the field.	3
8	To be able to evaluate the knowledge and skills acquired in the field with a critical approach and to direct the learning.	-

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