To give the participants an understanding of data containing random variation and understanding of under which conditions general information can be inferred from such data. Furthermore to give the participants knowledge of a number of methods, which are applicable to certain problems with the characteristic mentioned above. This enables the participants to discuss a given scientific method and the possibility to follow a research and development effort.

• Estimate and interpret simple summary statistics, such as mean, standard deviation, variance, median and quartiles.
• Apply simple graphical techniques, including histograms, normal-score plots, and box plots.
• Identify and describe probability distributions, including Poisson, binomial, exponential and the Normal distribution.
• Compare different statistical methods.
• Apply and interpret important statistical concepts, such as the formulation of models, paramteter estimation, construction of confidence intervals and hypothesis testing.
• Apply and interpret simple statistical methods within one- and two sample situations.
• Apply and interpret simple statistical methods for count data.
• Apply and interpret simple statistical methods within linear regression and analysis of variance, including factorial experiments and multiple linear regression.
• Understand and interpret output from some commonly used statistical software
• Perform sample size calculations in simplified and standard setups
• Debate and critize emprically based information.
• Apply and interpret basic calculations w.r.t. principal components and their use in regression.

Simple methods for graphical and tabular assessments of collected or
measured data. Models for certain stochastic variables as e.g. Poisson, binomial, exponential, and normal distributions. Hypothesis testing, estimation of parameters, and construction of confidence intervals in common situations (especially mean values, variances, and proportions). Introduction to regression analysis, analysis of variance, contingency tables, rank sum test, experimental design, and principal component analysis and regression.

The course is primarily directed towards students of Bachelor of Engineering (chemistry and biotechnology) which have first priority. To the extend that it is possible it is possible for M.Sc. Eng. students to attend the course.