Statistical Methods in Experimental Science2010VT
Having completed the course, the student shall be able to:
- display good understanding of major principles for statistical analysis of experimental data;
- explore a given set of experimental data in search of a suitable statistical model;
- interpret the results of major types of statistical analysis of experimental data.
Descriptive statistics: mean, median, standard deviation, boxplots.
Probability theory: probability distributions, binomial, Poisson and normal distributions.
Statistical inference: Point estimators, confidence intervals, hypothesis testing.
One-, two- and multisample tests for means and medians: t-tests, one-way and two-way analysis of variance; block designs; rank sum tests, Kruskal-Wallis and Friedman tests.
Regression and correlation: Pearson and Spearman correlation; Simple and multiple linear regression. Polynomial regression. Dummy variables. Model selection. Covariance analysis. Models of repeated measures.
The student must have passed a minimum of 4.5 ECTS credits in statistics and 15
ECTS credits in statistics and mathematics together. This corresponds to what
is normally included in biology and chemistry programs at LiU.
The course will be given in English. The course includes lessons, 2-3 hours each, at 6-10 occasions.
The course is problem-oriented, and the student is expected to bring relevant datasets from his/her research field.
Written and oral presentations of solutions to assignments encompassing statistical analysis of experimental data.
Hassard, T.K. Understanding biostatistics. ISBN 0-8016-2078-3.
Phone: 013-28 1482, e-mail: email@example.com
April 26 to June 4 2010 (exact start and end date will be decided by the participators and the examiner).
3-4.5 ECTS credits
Page responsible: Director of Graduate Studies
Last updated: 2012-05-03