Statistics with Applications

The course provides an introduction to the basic definitions and concepts for statistical inference and gives an introduction to some basic statistical methods. The topics covered include; chi-squared distribution, Student's T-distribution, Fisher's F distribution, gamma distribution, point estimation, properties of point estimators, some simple examples of stratification, confidence interval for the mean in the normal distribution with known and unknown variance, confidence interval for the variance in a normal distribution, confidence interval based on normal approximation, testing a statistical hypothesis, test statistic, critical region, significance level, p-value, power of a test, power function, testing in normal and binomial models, tests based on the normal approximation, correlation, simple linear regression, T-tests for comparison of two samples, one-way analysis of variance, non-parametric methods, analysis of categorical variables.

The course gives the students an introduction to, and an theoretical understanding of, the basic principles in statistical inference. Further, the commonly used statistical methods will be dealt with. The student will through the exercises be able to choose the correct method and do the analysis of data sets in simple practical situations.

Lectures and compulsory exercises. Written examiniaton. Retake of examination may be given as an oral examination.