About the course
Aims and objectives
The aim of the course is to achieve the general capability to collect, manage, evaluate, and interpret scientific data by means of the free, renowned statistical software package [R]. Moreover, this module aims at promoting the understanding of statistical evaluation as used in scientific studies and the use of statistical parameters in inference as a tool to provide “scientific evidence”.
Content of the course
The contents of this module are organised into seven different themes:
Theme 1: Inference, data types and scales
- Definition of the terms sample, population, variable, parameter, etc.
- Types of data: parametric, non-parametric, nominal categorical, ordinal categorical, interval numerical, ratio numerical
- Quality indicators of scales
- The principle of inference.
Theme 2: Description of univariate data
- Introduction of R and R Studio
- Measures of centrality: mean, median, mode
- Measures of variation: variance, standard deviation, standard error of the mean, coefficient of variation, percentiles, inter-quartile range, and confidence intervals
- Distributions: normal Gaussian, Poisson, binomial, others.
Theme 3: Probability and presentation
- How to present data: tables and charts
- Rules of probability calculation
- Likelihoods and tree diagrams.
Theme 4: Hypothesis formulation, acceptance, and rejection based on statistical testing for two groups (bivariate testing)
- Two-group comparisons, parametric and non-parametric
- 2×2 crosstabulations and measures of association (exposure-outcome interactions)
- Correlations between two datasets.
Theme 5: Comparison of more than two groups and multivariate evaluations
- ANOVA incl. post-hoc testing
- Crosstabulation tables and their evaluation
- Simple and multiple correlations
- Elemental multivariate models.
Theme 6: Bayesian statistics
- Novel strategies to overcome weaknesses of traditional statistical approaches
- Predicting likelihood of future events.
Theme 7: Linear optimization of nutrient-food interactions
- Linear programming for nutrition-related problems
- Price-based optimization strategies for fully nutritious diets.
Each point will be approached in a hands-on manner. The focus will be on applied science, meaning that application aspects will be given priority over the theoretical background. Statistics is no sorcery!
Learn how to arrange, evaluate, and interpret the data you raised and learn to understand how statistical data were raised and interpreted by other scientists. Apply statistics to your own data.
The teaching methods applied in this module will be: theoretical approach by lecturing and individual preparation (self-study), common exercises in class, problem solving in groups and at an individual level, and presentations of problem solutions. The learning process will be supported by ICT (i.e. videos and quizzes).
The exam is an individual product which will be graded based on the 7-point scale.
The course takes place at Nørrebro.
Do you have unanswered questions after reading this description? Find contact information at the bottom of this site.