The notions probability, discrete and continuous random variable, probability function, probability density function, cumulative distribution function, expected value, variance, standard deviation, covariance and correlation, are defined. Furthermore, the most common probability distributions for technical applications are treated, with special emphasis on the normal distribution, distributions for linear combinations of normally and non-normally random variables (in the latter case applying the central limit theorem), and approximations of expected values and variances for non-linear functions of random variables. The notions point estimate, unbiasedness, efficiency, hypothesis, significance level, power, type I and II errors, rejection region, p value and confidence level, are defined. The t-, Chi2-, and F-distributions are applied for hypothesis testing and interval estimation for one and two samples. Furthermore the basics of stochastic simulation, bootstrap and permutation tests, are treated. Finally the analysis of contingency tables, basic analysis of variance, and simple and multiple linear regression analysis, are covered.
The theory from Module 1 is applied on problems from areas the students might run into after their education. The data analysis is mainly done with the support of suitable statistical software, focusing mainly on presenting problems and solutions both in oral and written form.
