Basic concepts like probabilities, conditional probabilities and independent events. Discrete and continuous models, normal, binomial and Poisson distribution. Central limit theorem and Law of large numbers. Measures of location and scale of random variables and data sets.
Descriptive statistics. Graphical visualisation of data sets. Construction of indices and public statistical production.
Point estimates. Confidence intervals for mean of normally distributed observations. Confidence intervals for proportions, difference in means and proportions. Testing statistical hypothesis.
To pass the course, the student should be able to do the following:
- construct elementary statistical models for experiments
- state standard models and explain the applicability of the models in given examples
- summarise data sets with descriptive statistics as measures of location, spread and dependency, and present data graphically
- define real public indices and construct an index in a real application
- calculate estimates of unknown quantities with standard methods and quantify the uncertainty in these estimates
- describe how measuring accuracy affect conclusions and quantify risks and error probabilities when testing statistical hypothesis
- explain the basic concepts behind sampling surveys and critically examine statistical information
- use statistical software
To receive the highest grade, the student should in addition be able to do the following:
- Combine all the concepts and methods mentioned above in order to solve more complex problems.
