## Learning outcome

** Knowledge**

- Understand statistical methods such that the students can analyze statistical data, and avoid the most common pitfalls in such analysis.

** Abilities**

The students shall after completion of this course be able to

- Interpret statistical data with the aid of central- and dispersion measures, frequency distributions and graphical methods.
- Master basic probability theory, included is probability models, combinatorics, sampling models, conditional probability, the law of total probability, Bayes law and independence of random variables.
- Analyze probability distributions and calculate expected value and variance of a random variable, and extend this to linear combinations of random variables.
- Understand simultaneous probability distributions, included is calculation of expected value, variance and covariance.
- Choose a probability model and do calculations with discrete and continuous probability distributions, included is the Binomial distribution, the Hypergeometric distribution, the Poisson distribution, the Normal distribution, approximation by normal distribution, and the t-distribution.
- Estimate unknown parameters, included is point estimation and interval estimation.
- Master hypothesis testing in sampling models and binomial models. Assess different methods for testing. Interpret levels of significance, P-values, and the strength of a test.
- Assess the difference between two groups, included is hypothesis testing.
- Use chi-square tests (test for models and test of independence)
- Analyze covariation between two or more stochastic variables, by regression and by interpretation of the correlation coefficient, and by estimation and testing of the regression coefficient.

** General competence**

- Obtain theoretical insights into statistical analysis as a prerequisite to practical application of statistics in advanced courses.