Biostatistics

Course Code:

ΝΟΣ0204

Semester:

2nd Semester

Specialization Category:

General Background Mandatory ( ΜΓΥ )

Course Hours:

Theory 3

ECTS:

3



 

STUDENT PERFORMANCE EVALUATION

  • Written final exam (100%) which includes:
  • Multiple choice test
  • Short answer Questions
  • Critical thinking Questions
  • Tests

 

LEARNING OUTCOMES

By the end of the course the students will be competent to:

  • Choose the appropriate method of statistical analysis
  • Apply statistical tests in the context of nursing studies

 

GENERAL COMPETENCES

  • Decision-making
  • Search for, analysis and synthesis of data and information, with the use of the necessary technology
  • Autonomy work
  • Working in an interdisciplinary environment
  • Project planning and management
  • Production of new research ideas

The aim of the course is to enable students to deal and solve simple statistical problems, in a workplace with use of statistics, with theoretical origin and proofs of types and distributions of the most common and simple both descriptive and analytical statistics. The variability of biological characteristics expresses the common finding that all biomedical variables fluctuate. Therefore, the aim of the course is for the student to realize and take into account the context and consequences of variability.

 

 

Course Outline

 

SYLLABUS

Course Description: Introductory concepts. Biostatistics research. Collection of biometric material (inventories, special surveys, sampling). Presentation of the material (tables-charts). Statistical study. Elements of calculating probabilities.

  • Introduction-Basic concepts
  • Types of data and their presentations
  • Descriptive statistics and frequency distributions
  • Quantitative data analysis
  • Credibility Limits
  • Sample size and power
  • Analysis of qualitative data
  • Test x2 as a criterion of correlation, heterogeneity, good application, difference, etc.
  • Reliability limits of relative risk
  • Evaluation of laboratory findings
  • Relationship between quantitative characteristics
  • Variability analysis
  • Multiple linear dependence (Regression) (1)
  • Multiple linear dependence (Regression) (2)
  • Dimensional features. Non-parametric tests