SUBJECT
An introduction to biostatistics I
lecture
master
2
Semesters 1-4
Autumn/Spring semester
1. Exploratory and confirmatory data analysis. Description of data sets.
2. Modelling. Probability and random variables. Theoretical distributions of categorical random variables and related biological models (binomial, polinomial, hypergeometrical, Poisson)
3. Theoretical distributions of continous random variables and related biological models (normal, lognormal and exponential.).
4. Estimation. Requirements. Confidence interval, a simulation study. Accuracy, precision
and sample size.
5. Maximum likelihood estimates. Maximum likelihood estimate of the p parameter of the binomial distribution.
6. Hypothesis testing. Sign test. Levels of significance and the p values. Statistical power estimation.
7. Analysis of contingency tables. Tests of goodness-of-fit. χ2 distribution, χ2 test, maximum likelihhod ratio test, G-test. Basics of model selection.
8. Testing normality with informal and formal methods.
9. Comparison of means of normally distributed random variables from t-ests to one-factorial ANOVA.
10. Planned and unplanned, orthogonal and non-orthogonal comparisons. Experimentwise error.
11. Two factorial ANOVA. The meaning of interactions. Random and fix factors.
12. Comparisons of medians. Non-parametric tests.
13. Linear regression. Estimation of the parameters of an equation.
14. Residual analysis. Comparison of slopes. Linearisation.
15. Pearson and rank correlation. Model II. regression.
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Peck, Roxy; Olsen, Chris; Devore, Jay 2001: Introduction to Statistics and Data Analysis (Statistics Ser.). Brooks/Cole, Pacific Grove, CA, 847 p.