Mathematical and Biostatistical Foundation of Biotechnology

Data

Official data in SubjectManager for the following academic year: 2023-2024

Topic

Main topics of the subject: Principles of descriptive and inferential statistics; models, planning of experiments, presentation, analysis and evaluation of data.
Principles of descriptive statistics. Data, types of data, data analysis and evaluation. Presentation of data on graphs, diagrams. Probability, random variable, discrete and continuous statistical distributions. Principles of inferential statistics. Statistical hypothesis testing. Parametric and non-parametric approaches. Correlation and regression analysis.
Statistical software packages and support: problem-solving with Microsoft Excel, an overview of other tools (Microcal Origin, Minitab, RStudio, GPower).

Lectures

• 1. Introduction. Principles of descriptive and inferential statistics. Models, planning of experiments. - Dr. Bugyi Beáta
• 2. Introduction. Principles of descriptive and inferential statistics. Models, planning of experiments. - Dr. Bugyi Beáta
• 3. Principles of descriptive statistics. Data, types of data, data analysis and evaluation. Data presentation. - Leipoldné Dr. Vig Andrea Teréz
• 4. Principles of descriptive statistics. Data, types of data, data analysis and evaluation. Data presentation. - Leipoldné Dr. Vig Andrea Teréz
• 5. Probability, random variable, discrete distributions. - Leipoldné Dr. Vig Andrea Teréz
• 6. Probability, random variable, discrete distributions. - Leipoldné Dr. Vig Andrea Teréz
• 7. Probability, random variable, continuous distributions. - Leipoldné Dr. Vig Andrea Teréz
• 8. Probability, random variable, continuous distributions. - Leipoldné Dr. Vig Andrea Teréz
• 9. Principles of inferential statistics. Statistical hypothesis testing, errors in decision, family-wise error rate. Overview of parametric and non-parametric approaches. - Dr. Bódis Emőke
• 10. Principles of inferential statistics. Statistical hypothesis testing, errors in decision, family-wise error rate. Overview of parametric and non-parametric approaches. - Dr. Bódis Emőke
• 11. Parametric approaches 1. - Dr. Bódis Emőke
• 12. Parametric approaches 1. - Dr. Bódis Emőke
• 13. Parametric approaches 2. - Dr. Visegrády Balázs
• 14. Parametric approaches 2. - Dr. Visegrády Balázs
• 15. Revision, consultation, midterm test. - Dr. Bugyi Beáta
• 16. Revision, consultation, midterm test. - Dr. Bugyi Beáta
• 17. Correlation analysis. - Dr. Visegrády Balázs
• 18. Correlation analysis. - Dr. Visegrády Balázs
• 19. Regression analysis 1. - Dr. Bódis Emőke
• 20. Regression analysis 1. - Dr. Bódis Emőke
• 21. Regression analysis 2. - Dr. Bugyi Beáta
• 22. Regression analysis 2. - Dr. Bugyi Beáta
• 23. Nonparametric approaches. - Dr. Bugyi Beáta
• 24. Nonparametric approaches. - Dr. Bugyi Beáta
• 25. Workshop. Discussion of case studies and Students' projects. - Dr. Bugyi Beáta
• 26. Workshop. Discussion of case studies and Students' projects. - Dr. Bugyi Beáta
• 27. Revision, consultation, end semester test. - Dr. Bugyi Beáta
• 28. Revision, consultation, end semester test. - Dr. Bugyi Beáta

Practices

• 1. Introduction. Principles of descriptive and inferential statistics. Models, planning of experiments.
• 2. Introduction. Principles of descriptive and inferential statistics. Models, planning of experiments.
• 3. Principles of descriptive statistics. Data, types of data, data analysis and evaluation. Data presentation.
• 4. Principles of descriptive statistics. Data, types of data, data analysis and evaluation. Data presentation.
• 5. Probability, random variable, discrete distributions.
• 6. Probability, random variable, discrete distributions.
• 7. Probability, random variable, continuous distributions.
• 8. Probability, random variable, continuous distributions.
• 9. Principles of inferential statistics. Statistical hypothesis testing, errors in decision, family-wise error rate. Overview of parametric and non-parametric approaches.
• 10. Principles of inferential statistics. Statistical hypothesis testing, errors in decision, family-wise error rate. Overview of parametric and non-parametric approaches.
• 11. Parametric approaches 1.
• 12. Parametric approaches 1.
• 13. Parametric approaches 2.
• 14. Parametric approaches 2.
• 15. Revision, consultation, midterm test.
• 16. Revision, consultation, midterm test.
• 17. Correlation analysis.
• 18. Correlation analysis.
• 19. Regression analysis 1.
• 20. Regression analysis 1.
• 21. Regression analysis 2.
• 22. Regression analysis 2.
• 23. Nonparametric approaches.
• 24. Nonparametric approaches.
• 25. Workshop. Discussion of case studies and Students' projects.
• 26. Workshop. Discussion of case studies and Students' projects.
• 27. Revision, consultation, end semester test.
• 28. Revision, consultation, end semester test.

Seminars

• 1. Introduction. Principles of descriptive and inferential statistics. Models, planning of experiments.
• 2. Principles of descriptive statistics. Data, types of data, data analysis and evaluation. Data presentation.
• 3. Probability, random variable, discrete distributions.
• 4. Probability, random variable, continuous distributions.
• 5. Principles of inferential statistics. Statistical hypothesis testing, errors in decision, family-wise error rate. Overview of parametric and non-parametric approaches.
• 6. Parametric approaches 1.
• 7. Parametric approaches 2.
• 8. Revision, consultation, midterm test.
• 9. Correlation analysis.
• 10. Regression analysis 1.
• 11. Regression analysis 2.
• 12. Nonparametric approaches.
• 13. Workshop. Discussion of case studies and Students' projects.
• 14. Revision, consultation, end semester test.

Reading material

Obligatory literature

Homepage of the Department of Biophysics:
https://aok.pte.hu/en/egyseg/10/oktatasi-anyagok

Literature developed by the Department

Homepage of the Department of Biophysics:
https://aok.pte.hu/en/egyseg/10/oktatasi-anyagok

Notes

Homepage of the Department of Biophysics:
https://aok.pte.hu/en/egyseg/10/oktatasi-anyagok

Recommended literature

Statistics Openstax, ISBN-10: 1-947172-05-0, ISBN-13: 978-1-947172-05-0
Allan G. Bluman: Elementary statistics, ISBN 978–0–07–338610–2
Myra L- Samuels, Jeffrey A. Witmer, Andrew A. Schaffner: Statistics for the life sciences, ISBN-13: 978-1-292-10181-1
James Stewart, Troy Day: Biocalculus, ISBN-13: 978-1-133-10963-1
J. Pezzullo: Biostatistics for dummies, 2013, Wiley, ISBN 978-1-118-55399-2

Conditions for acceptance of the semester

Maximum of 15 % absence allowed

Mid-term exams

The grade is based on the average result of the two tests written during the semester (expected dates: week 7 and week 14).
Grading policy:
< 60% (1, fail)
60 ≤ result < 70% (2, satisfactory)
70 ≤ result < 80% (3, average)
80 ≤ result < 90% (4, good)
90 ≤ result (5, excellent)
In case of a failed exam, the result can be improved based on the rules of the Code of Studies and Examinations.

Making up for missed classes

The opportunity to make up for absence can be discussed with the course leader.

Exam topics/questions

Principles of descriptive statistics. Data, types of data, data analysis and evaluation. Presentation of data on graphs, diagrams.
Probability, random variable, discrete (binomial) and continuous (normal, standard normal) statistical distributions.
Principles of inferential statistics. Statistical hypothesis testing, errors in the decision, family-wise error rate. Overview of parametric and non-parametric approaches.
Parametric approaches 1. (z-, and t-test)
Parametric approaches 2. (analysis of variance, F-test, ANOVA, posthoc tests (Tukey, Scheffe, Bonferroni))
Nonparametric approaches 1. (sign test, Wilcoxon, Mann-Whitney)
Nonparametric approaches 2. (Kruskal-Wallis, Friedman)
Correlation analysis. Scatter plot, correlation coefficient, the significance of the correlation coefficient, rank, rank correlation.
Regression analysis 1. Line of best fit, regression line equation. Coefficient of determination, the significance of R2, residual plots.
Regression analysis 2. Nonlinear regression.
CHI-squared test (goodness of fit, independence, homogeneity).

Examiners

• Dr. Bódis Emőke
• Dr. Bugyi Beáta
• Dr. Visegrády Balázs
• Karádi Kristóf Kálmán
• Kilián Balázsné Raics Katalin

Instructor / tutor of practices and seminars

• Dr. Bódis Emőke
• Dr. Bugyi Beáta
• Dr. Visegrády Balázs
• Leipoldné Dr. Vig Andrea Teréz