Descriptive statistics. Probability: definitions, laws and properties. Conditional
probability. Independent events. Total probability. Bayes’ theorem. Random variables
and their distributions. Mean and variance and their properties. Important basic
distributions. Bivariate random variables. Central limit theorem. Sampling
distributions: χ2, t and F. Point estimation, confidence intervals and tests of
hypotheses. The linear model: estimation and tests on parameters, coefficient of
determination (R2), prediction. Applications using computers. Laboratory exercises.
- Teacher: Χρυσηίς Καρώνη-Ρίτσαρντσον
ECTS : 5
Language : el
Learning Outcomes : Upon successful completion of the course, the student will be able to: • understand the fundamental concepts of probability theory • apply the basic methods of classical statistical inference • apply the linear regression model • judge the model s ability to describe the data • calculate predictions from the model.