Course Information

Course Information
Course Title Code Language Type Semester L+U Hour Credits ECTS
Biostatistics HEM501 Turkish Compulsory 2 + 0 2.0 4.0
Prerequisite Courses
Course Level Graduate
Mode of delivery An interactive lesson, Project, Seminar
Course Coordinator Doç. Dr. Mehmet Ali SUNGUR
Instructor(s)
Goals To teach basic statistical concepts and methods to students by examples and applications in health sciences, to provide understanding and evaluating the statistical analysis used in the literature of their own study fields.
Course Content This course includes that; Basic statistical concepts such as: statistics and biostatistics, population, sample, statistic, parameter, data, variable, types of data, and etc., Descriptive statistics: grouping the data, measures of central tendency, measures of location, histogram, bar graph, stem & leaf graph, box-plot, etc, Descriptive statistics: measures of dispersion, error bar graph, etc, Examining the association among the variables by tables and graphics: Cross tables, tables regarding descriptive statistics (mean, s. deviation, etc.), multivariate applications of basic graphics, scatter plots, etc., Standardization (z and T scores). Theoretical distributions: Normal distribution, and normality transformations. Tests and graphs for normality. Sampling distributions and confidence intervals, Research methods. Introduction to hypothesis tests: Aims and stages of hypothesis tests, possible types of errors, p and alpha values, power, effect size, decision process, Hypothesis tests: Parametric and nonparametric one sample tests. Parametric and nonparametric independent two sample tests, Hypothesis tests: Parametric and nonparametric two independent sample tests, Hypothesis tests: Parametric and nonparametric dependent two-sample tests). Parametric and nonparametric two dependent sample tests, Measures of association: Pearson correlatin coefficient, Spearman correlation coefficient, Phi, Cramer V, Eta, etc., Simple and multiple linear regression analysis, Simple and multiple linear regression analysis and examining an article.
Learning Outcomes
# Öğrenme Kazanımı
1 Decide to proper basic statistical analysis
2 Do calculations and analysis individually
3 Interpret the findings
4 Understand statistical analysis used in the literature of their own study fields
5 Criticize statistical analysis used in the literature of their own study fields
6 Have sufficient theoretical and practical basis required in advanced statistical courses
7 Can plan and implement a scientific research in the field of Surgical Nursing, can analyzes and reports the obtained data.
Lesson Plan (Weekly Topics)
Week Topics/Applications Method
1. Week Basic statistical concepts such as: statistics and biostatistics, population, sample, statistic, parameter, data, variable, types of data, and etc.
2. Week Descriptive statistics: grouping the data, measures of central tendency, measures of location, histogram, bar graph, stem & leaf graph, box-plot, etc.
3. Week Descriptive statistics: measures of dispersion, error bar graph, etc.
4. Week Examining the association among the variables by tables and graphics: Cross tables, tables regarding descriptive statistics (mean, s. deviation, etc.), multivariate applications of basic graphics, scatter plots, etc.
5. Week Standardization (z and T scores). Theoretical distributions: Normal distribution, and normality transformations. Tests and graphs for normality. Sampling distributions and confidence intervals
6. Week 1st Midterm Examination
7. Week Research methods. Introduction to hypothesis tests: Aims and stages of hypothesis tests, possible types of errors, p and alpha values, power, effect size, decision process
8. Week Hypothesis tests: Parametric and nonparametric one sample tests. Parametric and nonparametric independent two sample tests.
9. Week Hypothesis tests: Parametric and nonparametric two independent sample tests.
10. Week Hypothesis tests: Parametric and nonparametric dependent two-sample tests). Parametric and nonparametric two dependent sample tests .
11. Week 2nd Midterm Examination
12. Week Measures of association: Pearson correlatin coefficient, Spearman correlation coefficient, Phi, Cramer V, Eta, etc.
13. Week Simple and multiple linear regression analysis.
14. Week Simple and multiple linear regression analysis and examining an article.
*Midterm and final exam dates are not specified in the 14-week course operation plan. Midterm and final exam dates are held on the dates specified in the academic calendar with the decision of the University Senate.
Relations with Education Attainment Program Course Competencies
Program Requirements DK1 DK2 DK3 DK4 DK5 DK6 DK7
Recommended Sources
Ders Kitabı veya Notu Ders Kitabı veya Ders Notu bulunmamaktadır.
Diğer Kaynaklar
  • 1. Sümbüloglu K ve Sümbüloğlu V. Biyoistatistik. Seçkin Yayıncılık, Ankara, 2010. 2. Özdamar K. Pasw ile Biyoistatistik. Kaan Kitabevi, Eskişehir, 2013. 3. Alpar R. Spor, Sağlık ve Eğitim Bilimlerinden Örneklerle Uygulamalı İstatistik ve Geçerlik-Güvenirlik. Detay Yayıncılık, Ankara, 2018. 4. Daniel, Wayne W. Biostatistics 9th Edition, New York: John Wiley&Sons, 2013.
ECTS credits and course workload
ECTS credits and course workload Quantity Duration (Hour) Total Workload (Hour)
Sınavlar
Midterm 1 1 2 2
Midterm 2 1 2 2
Homework 1 14 1 14
Final 1 2 2
Total Workload 20
*AKTS = (Total Workload) / 25,5 ECTS Credit of the Course 4.0