The student will understand and be able to:
write programs that can be run on real data or self programmed artificial data;
calculate mean, standard deviations, and other statistical parameters;
evaluate when normal distribution is a suitable approximation and when other probability distributions are needed;
understand the meaning and difference between "sigma" and confidence levels;
calculate estimates for the confidence levels of a set of measurements;
compare statistical parameters from different datasets or to model data;
quantify whether a small deviation observed in the data is significant;
plan observations based on statistical requirements for the data;
understand that Pearson's correlation coefficient and linear regression are fully independent measures of the data;
understand that all correlations need not to be linear;
perform a least squares fit to a dataset and critically evaluate when it is a sensible thing to do;
evaluate when non-parametric tests are more suitable for analysis of the data;
understand the concepts of time series analysis in evenly spaced and unevenly spaced data;
evaluate the goodness of a random number generator in one's simulations;
understand the conceptual difference and similarities between a direct and an inverse problem;
evaluate when a bayesian analysis method is suitable for problem solving;
make a real attempt for a bayesian analysis solving.