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Mathematical and Biostatistical Foundation of Biotechnology

Data

Official data in SubjectManager for the following academic year: 2022-2023

Course director

Number of hours/semester

lectures: 28 hours

practices: 28 hours

seminars: 14 hours

total of: 70 hours

Subject data

  • Code of subject: OTN-MBFB-T
  • 5 kredit
  • Biotechnology BSc
  • Basic Module modul
  • spring
Prerequisites:

OTN-MBBS-T completed

Course headcount limitations

min. 1 – max. 50

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. - Dr. Bugyi Beáta
  • 4. Principles of descriptive statistics. Data, types of data, data analysis and evaluation. Data presentation. - Dr. Bugyi Beáta
  • 5. Probability, random variable, discrete (binomial) and continuous (normal, standard normal) statistical distributions. - Dr. Bugyi Beáta
  • 6. Probability, random variable, discrete (binomial) and continuous (normal, standard normal) statistical distributions. - Dr. Bugyi Beáta
  • 7. Principles of inferential statistics. Statistical hypothesis testing, errors in decision, family-wise error rate. Overview of parametric and non-parametric approaches. - Karádi Kristóf Kálmán
  • 8. Principles of inferential statistics. Statistical hypothesis testing, errors in decision, family-wise error rate. Overview of parametric and non-parametric approaches. - Karádi Kristóf Kálmán
  • 9. Parametric approaches 1. - Karádi Kristóf Kálmán
  • 10. Parametric approaches 1. - Karádi Kristóf Kálmán
  • 11. Parametric approaches 2. - Karádi Kristóf Kálmán
  • 12. Parametric approaches 2. - Karádi Kristóf Kálmán
  • 13. Revision, consultation, midterm test. - Dr. Bódis Emőke
  • 14. Revision, consultation, midterm test. - Dr. Bódis Emőke
  • 15. Nonparametric approaches 1. - Dr. Bódis Emőke
  • 16. Nonparametric approaches 1. - Dr. Bódis Emőke
  • 17. Nonparametric approaches 2. - Dr. Bódis Emőke
  • 18. Nonparametric approaches 2. - Dr. Bódis Emőke
  • 19. Correlation analysis. - Dr. Visegrády Balázs
  • 20. Correlation analysis. - Dr. Visegrády Balázs
  • 21. Regression analysis 1. - Dr. Visegrády Balázs
  • 22. Regression analysis 1. - Dr. Visegrády Balázs
  • 23. Regression analysis 2. - Dr. Visegrády Balázs
  • 24. Regression analysis 2. - Dr. Visegrády Balázs
  • 25. CHI-squared test. - Kilián Balázsné Raics Katalin
  • 26. CHI-squared test. - Kilián Balázsné Raics Katalin
  • 27. Revision, consultation, end semester test. - Kilián Balázsné Raics Katalin
  • 28. Revision, consultation, end semester test. - Kilián Balázsné Raics Katalin

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 (binomial) and continuous (normal, standard normal) statistical distributions.
  • 6. Probability, random variable, discrete (binomial) and continuous (normal, standard normal) statistical distributions.
  • 7. Principles of inferential statistics. Statistical hypothesis testing, errors in decision, family-wise error rate. Overview of parametric and non-parametric approaches.
  • 8. Principles of inferential statistics. Statistical hypothesis testing, errors in decision, family-wise error rate. Overview of parametric and non-parametric approaches.
  • 9. Parametric approaches 1.
  • 10. Parametric approaches 1.
  • 11. Parametric approaches 2.
  • 12. Parametric approaches 2.
  • 13. Revision, consultation, midterm test.
  • 14. Revision, consultation, midterm test.
  • 15. Nonparametric approaches 1.
  • 16. Nonparametric approaches 1.
  • 17. Nonparametric approaches 2.
  • 18. Nonparametric approaches 2.
  • 19. Correlation analysis.
  • 20. Correlation analysis.
  • 21. Regression analysis 1.
  • 22. Regression analysis 1.
  • 23. Regression analysis 2.
  • 24. Regression analysis 2.
  • 25. CHI-squared test.
  • 26. CHI-squared test.
  • 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 (binomial) and continuous (normal, standard normal) statistical distributions.
  • 4. Principles of inferential statistics. Statistical hypothesis testing, errors in decision, family-wise error rate. Overview of parametric and non-parametric approaches.
  • 5. Parametric approaches 1.
  • 6. Parametric approaches 2.
  • 7. Revision, consultation, midterm test.
  • 8. Nonparametric approaches 1.
  • 9. Nonparametric approaches 2.
  • 10. Correlation analysis.
  • 11. Regression analysis 1.
  • 12. Regression analysis 2.
  • 13. CHI-squared test.
  • 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
  • Karádi Kristóf Kálmán
  • Kilián Balázsné Raics Katalin
Rólunk