PhD in Healthcare Management with a bachelor's degree
PhD	TR-NQF-HE: Level 8	QF-EHEA: Third Cycle	EQF-LLL: Level 8

General course introduction information

Course Code:

SAY512

Course Name:

Biostatistics and Decision Making

Course Semester:

Fall
Spring

Course Credits:

Theoretical	Practical	Credit	ECTS
3	0	3	8

Language of instruction:

Course Requisites:

Does the Course Require Work Experience?:

Type of course:

Scientific Preparation

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):

Öğr.Gör. ŞİRİN YILMAZ
Öğr.Gör. SALİM YILMAZ

Course Assistants:

Course Objective and Content

Course Objectives:	To teach the basic statistical concepts and methods with special examples and applications for health area , to provide students to understand and evaluate the literature in their field.
Course Content:	1. Basic concepts of biostatistics, 2. Descriptive statistics according to data type, 3. Graphs suitable for the data type, 4. Sample distribution and standard error, 5. Confidence intervals for statistics, 6. Hypothesis testing, 7. Correlation and simple linear regression analysis 8. Multivariate linear regression analysis

Learning Outcomes

The students who have succeeded in this course;

Learning Outcomes

1 - Knowledge
Theoretical - Conceptual
1) Knows the basic concepts of biostatistics
2) Calculates descriptive statistics according to data type
3) Draws graphs suitable for the data type
4) Understands sample distribution and standard error
5) Calculates and interprets confidence intervals for statistics
6) Selects, applies and interprets appropriate hypothesis testing
7) Applies the methods of correlation and simple linear regression analysis
8) Applies multivariate linear regression analysis
2 - Skills
Cognitive - Practical
3 - Competences
Communication and Social Competence
Learning Competence
Field Specific Competence
Competence to Work Independently and Take Responsibility

Lesson Plan

Week	Subject	Related Preparation
1)	Presentation of the course to the students: Basic statistical concepts; statistics, biostatistics, usage areas of biostatistics, population, sample, statistics, parameters, data, variables, data types, etc.
2)	Descriptive Statistics : • Descriptive statistics calculated by variable type. • Descriptive statistics; data classification, measurements. • Dispersion measures. Article criticism
3)	Single variable tables and graphs • Tables and graphs; interpret marginal table and graphs: histogram, bar graph, stem-leaf graph, box plot, mean-standard deviation graphs.
4)	Multivariate tables and Graphs • Multivariate tables and graphs; cross tables, multivariate applications of basic graphical representations, scatter plot, etc. Article criticism
5)	Theoretical Distributions • Theoretical distributions: normal distribution, binomial distribution, poisson distribution. • normality tests and graphs.
6)	Sampling Distributions and confidence intervals • point estimation, standard error, confidence level and margin of error • standard error and margin of error • confidence intervals for means and proportions
7)	Article critisicm
8)	Mid-term- Presentation
9)	Introduction To Hypothesis Tests – One-Sample Tests • the concepts of α and β. • the studies related to one sample.
10)	Independent Two-Sample Tests • two sample hypothesis tests about independent groups. • Interpret the result. Dependent Two-Sample Tests • two sample hypothesis tests about dependent groups. • Interpret the result. Article critism
11)	Independent More Than Two Sample Tests • more than two samples hypothesis tests about independent groups. • Interpret the result. Dependent More Than Two Sample Tests • more than two samples hypothesis tests about dependent groups. • Interpret the result. Article criticism
12)	Correlation and Lineer Regression Analysis • relationship between variables. • simple regression model. • multiple linear regression models. • model proficiency. Article criticism
13)	Final exam-Presentation
14)	Final exam-Presentation

Sources

Course Notes / Textbooks:	Ders notları haftalık bazda verilecektir. Her hafta makale kritiği yapılacaktır.
References:	1. Andy Field, Discovering Statistics Using SPSS, SAGE Publications,2009.

Course-Program Learning Outcome Relationship

Learning Outcomes	1	2	3	4	5	6	7	8
Program Outcomes

Course - Learning Outcome Relationship

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

Program Outcomes

Level of Contribution

Learning Activity and Teaching Methods

Expression
Brainstorming/ Six tihnking hats
Lesson
Reading
Q&A / Discussion
Application (Modelling, Design, Model, Simulation, Experiment etc.)

Assessment & Grading Methods and Criteria

Written Exam (Open-ended questions, multiple choice, true-false, matching, fill in the blanks, sequencing)
Homework
Presentation

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Attendance	14	% 10
Presentation	2	% 20
Midterms	1	% 30
Final	1	% 40
total		% 100
PERCENTAGE OF SEMESTER WORK		% 60
PERCENTAGE OF FINAL WORK		% 40
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	6	84
Homework Assignments	6	8	48
Midterms	1	20	20
Final	1	40	40
Total Workload			234