SUBJECT

Title

An introduction to biostatistics II

Type of instruction

practical

Level

master

Faculty

Faculty of Science

Part of degree program

Biology MSc

Credits

Recommended in

Semesters 1-4

Typically offered in

Autumn/Spring semester

Course description

The course is based on data collected by the students themselves. Students get an introduction to and support in using either Statistica or SPSS program packages. Participation in “An introduction to biostatistics 1.” is recquired. In the end of the course a brief project report (4-8 pages) has to be made by each student.This is evaluated and marked.

Required statistical material:

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.

Readings

Peck, Roxy; Olsen, Chris; Devore, Jay 2001: Introduction to Statistics and Data Analysis (Statistics Ser.). Brooks/Cole, Pacific Grove, CA, 847 p.