NUTRITION AND DIETETICS
Bachelor TR-NQF-HE: Level 6 QF-EHEA: First Cycle EQF-LLL: Level 6

General Information about the Course

Course Code: SBF109
Course Title: Biostatistics
Course Semester: 5. Semester / Fall
Course Credits:
Theoretical Practical Credit ECTS
2 0 2 3
Language of instruction: TR
Prerequisite of the course: No
Type of course: Necessary
Level of course:
Bachelor TR-NQF-HE:6. Master`s Degree QF-EHEA:First Cycle EQF-LLL:6. Master`s Degree
Course Lecturer(s): Ercan Tutar

Purpose and content of the course

Course Objectives: The aim of the course is to enable students to acquire statistical thinking skills in a conscious, competent and ethical manner and to develop data analysis skills in the field of health sciences.
Course Objective: Biostatistics course provides students with basic statistical data analysis skills and develops their ability to collect, organize, analyze and interpret data.
Mode of Delivery: Face to face

Learning Outcomes

Knowledge (Described as Theoritical and/or Factual Knowledge.)
  1) Knows and applies statistical analysis in the basic field of Health Sciences
  2) Calculates probability using z and t distributions
  3) Designs the research, states the hypotheses of the research and creates the model
Skills (Describe as Cognitive and/or Practical Skills.)
  1) Performs parametric and nonparametric test methods using statistical package program, interprets the results
Competences (Described as "Ability of the learner to apply knowledge and skills autonomously with responsibility", "Learning to learn"," Communication and social" and "Field specific" competences.)

Course Topics

Week Subject
Related Preparation Pekiştirme
1) Course Introduction
2) Measures of Central Tendency, Normal Curve, Confidence Interval and Hypothesis Testing Theoretical Expression, Computational Problem Solving
3) Correlation Test Theoretical Expression, Computational Problem Solving, Variable Types Theoretical Expression
4) Introduction to SPSS and Hypothesis testing with field-specific data set (Parametric and Nonparametric Methods)
5) (P) Dependent Sample T-test and (NP) Wilcoxon Signed Rank Test
6) Analysis of Variance: One-Way ANOVA, Repeated Measures ANOVA, ANCOVA, MANOVA.
7) Review and Q&A for the Exam
8) Midterm Exam
9) Multivariate Regression Analysis Assumptions and Case Study
10) Logistic Regression Analysis Assumptions and Case Study
12) Kaplan Meier Method, Survival Analysis and Cox Regression
13) Partial Correlation Assumptions and Case Study
14) Chi-square Analysis and Case Study
15) Final Exam
References: Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (6th ed.). Boston: Allyn & Bacon.

Field, A. P. (2009). Discovering statistics using SPSS (3rd ed.). London: Sage

Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models (2nd ed.). Thousand Oaks, CA: Sage.

Sümbüloğlu, K. & Akdağ, B. (2007). Regresyon Yöntemleri ve Korelasyon Analizi. Ankara: Hatipoğlu Yayınları.

Wilcox, R. R. (2005). Introduction to robust estimation and hypothesis testing (2nd ed.). Burlington, MA: Elsevier

Ders - Program Öğrenme Kazanım İlişkisi

No Effect 1 Lowest 2 Average 3 Highest
       
Ders Öğrenme Kazanımları

1

2

3

4

Program Outcomes
1) xxx
2) xxx
3) xxx
4) xxx
5) xxx
6) xxx
7) xxx
8) xxx
9) xxx
10) xxx
11) xxx
12) xxx
13) xxx
14) xxx
15) xxx

Course Teaching, Learning Methods

Q & A
Case Problem Solving/ Drama- Role/ Case Management
Laboratory
Quantitative Problem Solving
Fieldwork
Group Study / Assignment
Individual Assignment
WEB-based Learning
Internship
Practice in Field
Project Preparation
Report Writing
Seminar
Supervision
Social Activity
Occupational Activity
Occupational Trip
Application (Modelling, Design, Model, Simulation, Experiment et.)
Reading
Thesis Preparation
Field Study
Student Club and Council Activities
Other
Logbook
Interview and Oral Conversation
Research
Watching a movie
Bibliography preparation
Oral, inscribed and visual knowledge production
Taking photographs
Sketching
Mapping and marking
Reading maps
Copying textures
Creating a library of materials
Presentation

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Attendance % 0
Laboratory % 0
Application % 0
Practice Exam % 0
Quizzes % 0
Homework Assignments % 0
Presentation % 0
Project % 0
Special Course Internship (Work Placement) % 0
Field Study % 0
Article Critical % 0
Article Writing % 0
Module Group Study % 0
Brainstorming % 0
Role Playing + Dramatizing % 0
Out of Class Study % 0
Preliminary Work, Reinforcement % 0
Application Repetition etc. % 0
Homework (reading, writing, watching movies, etc.) % 0
Project Preparation + Presentation % 0
Report Preparation + Presentation % 0
Presentation / Seminar Preparation + Presenting % 0
Oral examination % 0
Midterms % 0
Final % 0
Report Submission % 0
Bütünleme % 0
Kanaat Notu % 0
Committee % 0
Yazma Ödev Dosyası % 0
Portfolio % 0
Take-Home Exam % 0
Logbook % 0
Discussion % 0
Participation % 0
total % 0
PERCENTAGE OF SEMESTER WORK % 0
PERCENTAGE OF FINAL WORK % 0
total % 0

Calculation of Workload and ECTS Credits

Activities Number of Activities Workload
Course Hours 13 39
Laboratory
Application
Practice Exam
Special Course Internship (Work Placement)
Field Work
Study Hours Out of Class
Article Critical 13 13
Article Writing
Module Group Study
Brainstorming
Role Playing + Dramatizing
Out-of-Class Study (Pre-study, Reinforcement, Practice Review, etc.)
Homework (reading, writing, watching movies, etc.)
Project Preparation + Presentation
Report Preparation + Presentation
Presentation / Seminar Preparation + Presenting
Oral examination
Preparing for Midterm Exams 7 14
MIDTERM EXAM (Visa)
Preparing for the General Exam 13 26
GENERAL EXAM (Final)
Participation
Discussion
Portfolio
Take-Home Exam
Logbook
Total Workload 92
ECTS (30 saat = 1 AKTS ) 3