Statistical Methods in Biology
1. The role of statistics. Foundations of the probability theory. The statistical inference.
2. Types of stochastic variables. Designing experiments.
3. EDA I. (Exploratory Data Analysis). Descriptive statistics.
4. Statistical inference: estimation. point- and interval estimation.
5. Statistical inference: testing hypotheses. The statistical decision.
6. Statistical inference with one or two samples: comparing means and variances.
7. Students’ topics
8. Association and prediction: linear reggression and correlation.
9. Association and prediction: multiple, canonic and non-linear reggression.
10. Statistical inference with more than two samples: ANOVA.
11. Statistical inference with more than two samples: ANCOVA, MANOVA
12. Multivariate methods: EDA II. pattern recognition, classification.
13. Practicing statistical model formation.
14. Outlook: time-series analysis, resampling methods.
S. Sharma. Applied multivariate techniques. John Wiley & Sons Inc., New York, 1996.
Statistical models in S. Ed.: J. M. Chambers, T. J. Hastie AT&T Bell Laboratories. Wadsworth & Brooks/Cole Advanced Books & Software, 1992
S.J. Welham, S.A. Gezan, S.J. Clark, A. Mead: Statistical Methods in Biology: Design and Analysis of Experiments and Regression, CRC Press, 2014, ISBN 9781439898055