Beskrivelse

Content - Key areas:
Reproducible research using RStudio and RMarkdown
Loading and cleaning of datasets to simplify further analysis and visualisation of data
Plan experiments, collect and analyse datasets followed by visualisation, inference and conclusions on data

Learning outcomes:
Knowledge
Having passed this course the successful student will possess knowledge about:
- Preparing data for visualisations and analysis (tidy data)
- Histograms, scatter diagrams and empirical cumulative density functions
- Parameter estimation
- Confidence interval
- Correlation coefficient

Programmes:
Bachelor of Science in Engineering (Welfare Technology)
5. semester, mandatory. Offered in: Odense
Bachelor of Science in Engineering (Learning and Experience Technology)
5. semester, mandatory. Offered in: Odense
- Regression analysis
- Hypothesis testing

Skills
Having passed this course the successful student will be able to
- Conduct reproducible research using RStudio and RMarkdown
- Loading and cleaning data sets using the R packages dplyr and tidyr
- Visualise data using the R package ggplot2
- Explain and create histograms, scatter diagrams and empirical cumulative density functions
- Explain the concept of linear regression and correlation
- Calculate and explain the estimation of parameters and confidence intervals
- Calculate and explain hypothesis testing


Competences
Having passed this course the successful student will be able to
- plan experiments, collect and analyze datasets
- Visualise datasets
- Identify potential correlations between stochastic variables
- Communicate statistical results to a broader audience

Periode01/09/2017 → …
MålgruppeBachelor
ECTS-point5,0 ECTS