ENVS1134 Statistical methods for analyzing environmental data (5 cr)

An advanced course related to analysing environmental and spatial data with R Statistics software. Main topics include multivariate methods (for modelling, classification, clustering, and dimension reduction), machine learning, meta-analysis, analysis methods for time series, community composition analyses, and analysis methods for spatial data.

After completing this course, the student should be able to

• Explain how the statistical methods introduced on this course work, and study their assumptions.
• Study regular bivariate correlations and statistical significance of treatments using relevant methods.
• Calculate effect sizes and response ratios, and combine results from several research studies with suitable meta-analysis methods.
• Fit regression, dose – response, and mixed models to data containing independent observations, and perform predictor variable and model selection.
• Use methods related to the analysis of time series, both in time domain and in frequency domain.
• Perform classification, clustering, and projection for high-dimensional data, and use resampling methods for the selection of classifiers.
• Use community composition analysis methods based on ordination.
  
• Read spatial data into R, and apply some rather simple spatial analysis methods for them.
• Use the R Statistics software well enough to solve an exercise work with it.

TILP2400/TILP2500, TILP2600 (or other basic statistics courses), and BENA4033/BENA4053 Basics of geoinformatics (or other sufficient knowledge of geoinformatics).

Lectures and exercises (including their solutions) will be put into Moodle.