Biostatistics and Data Analysis
Biology
Institute of Natural and Applied Sciences
Third Cycle (Doctorate Degree)
| Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
|---|---|---|---|---|---|---|
| Biostatistics and Data Analysis | BIO660 | Compulsory | Doctorate degree | 1 | Fall | 8 |
Name of Lecturer(s)
Assistant Prof. Dr. Fevzi UÇKAN
Learning Outcomes of the Course Unit
1) Expiain standard deviation, variane and variation coefficients.
2) Expiain standard Error, table and graph construction method
3) Expiain hypothesis testing, the general information hypothesis about tests
4) Expiain istatistical decision, the appropriate test for the selection of the key
5) Expiain variance, korelation and regression assays.
Program Competencies-Learning Outcomes Relation
| Program Competencies | |||||||
| 1 | 2 | 3 | 4 | 5 | 6 | ||
| Learning Outcomes | |||||||
| 1 | No relation | No relation | No relation | No relation | No relation | No relation | |
| 2 | No relation | No relation | No relation | No relation | No relation | No relation | |
| 3 | No relation | No relation | No relation | No relation | No relation | No relation | |
| 4 | No relation | No relation | No relation | No relation | No relation | No relation | |
| 5 | No relation | No relation | No relation | No relation | No relation | No relation | |
Mode of Delivery
Face to Face
Prerequisites and Co-Requisites
None
Recommended Optional Programme Components
Türkçe
Course Contents
Measure and scale relation / Variables and their properties / Data types and statistical analysis methods / Data input with common software (Biostat, StatGraph, SPSS, Microsoft Excel etc.) / Data deriving / Graph drawing (Software like Grapher, etc.) / Parametric tests (single sample test, two independent sample tests, variable analysis) / Non-parametric tests (Chi-square independence and correspondance tests, mark test, serial tests etc.)
Weekly Schedule
1) Probability and probability distributions2) Basic ststistics: Mean, median, variance, standard variation, standard error
3) Means variances and proportions
4) Inferences about the population means
5) standard Error, Table and Graph Construction Method
6) Test ststistic and rejection region
7) Theoretical Distributions, Binomial Distribution
8) Midterm examination/Assessment
9) Hypothesis Testing, the General InformationHypothesis AboutTests
10) Hypotheses, and Error Level
11) Sample Size, Test Types and Features
12) Statistical Decision, the appropriate test for the selection of the key
13) A ststristical test about more than two population means: Analysis of variance
14) Analysis of Variance, Correlation and Regression Analysis
15) Analysis of Variance, Correlation and Regression Analysis
16) Final examination
Recommended or Required Reading
Planned Learning Activities and Teaching Methods
1) Lecture
2) Question-Answer
3) Discussion
4) Case Study
5) Self Study
6) Problem Solving
7) Project Based Learning
Assessment Methods and Criteria
Contribution of Midterm Examination to Course Grade |
30% |
|---|---|
Contribution of Final Examination to Course Grade |
70% |
Total |
100% |
Language of Instruction
Turkish
Work Placement(s)
Not Required