The course treats the most important practical statistical methods used in bioengineering and biomedical engineering and during the course these methods are implemented in software familiar to engineering students. The course focuses primarily on the practical aspects of statistics in biotechnology. Computer-aided exercise work with a variety of datasets constitutes an essential learning activity.
More specifically, the course contains the following topics:
- Bioengineering data and descriptive statistics, both visual and numeric presentation.
- Basic concepts such as probability, conditional probability and independent events. Bayes’ formula. Discrete and continuous random variables, in particular one dimensional random variables. Measures of central tendency, dispersion and dependence of random variables and data sets. Common distributions and models, such as the normal, exponential, Poisson and uniform distributions. The Central limit theorem.
- Point estimates and general methods of estimation, such as maximum likelihood estimation and the method of least squares. Evaluation and comparison of point estimates, for example with respect to bias and efficiency. Confidence intervals and p-values. Two sample problems. Statistical hypothesis testing. Chi2-tests of goodness of fit, homogeneity and independence. Odds ratios. One- and two-way ANOVA. Design of experiments.