Knowledge:

The basic concepts in mathematical statistics, such as;

• Probability distributions
• Standard errors and confidence intervals
• Maximum likelihood and least squares estimation
• Bootstrapping
• Hypothesis testing and p-values
• Linear, non-linear, logistic and Poisson regression

Skills:

• Master practical implementation in R.
• Use computer simulations for computations with probability distributions, including bootstrapping.
• Compute uncertainty measures, such as standard errors and confidence intervals, for estimated parameters.
• Compute predictions based on regression models taking into account the uncertainty of the predictions.
• Assess a fitted distribution using descriptive methods.
• Use general purpose methods, such as the method of least squares and maximum likelihood, to fit probability distributions to empirical data.
• Summarize empirical data and compute relevant descriptive statistics for discrete and continuous probability distributions.

Competences:

• Formulate scientific questions in statistical terms.
• Interpret and report the conclusions of a practical data analysis.
• Assess the fit of a regression model based on diagnostic quantities and plots.
• Investigate scientific questions that are formulated in terms of comparisons of distributions or parameters by statistical methods.
• Investigate scientific questions regarding association in terms of linear, non-linear, logistic and Poisson regression models.