# ENVS1134 Statistical methods for analyzing environmental data (5 cr)

**Study level:**

Advanced studies

**Grading scale:**

0-5

**Language:**

English, Finnish

**Responsible organisation:**

Department of Biological and Environmental Science

**Curriculum periods:**

2024-2025, 2025-2026, 2026-2027, 2027-2028

## Tweet text

Analyzing environmental data with the R program.

## Description

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.

## Learning outcomes

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.

## Description of prerequisites

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

## Recommended prerequisites

- Prerequisite group 1

## Study materials

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

## Literature

- Hastie T., Tibshirani R. & Friedman J. 2009. The Elements of Statistical Learning, 2nd Edition. Springer.; ISBN: 978-0-387-84857-0
- Zuur A., Ieno E.N., Walker N., Saveliev A.A. & Smith G.M. 2009. Mixed Effects Models and Extensions in Ecology with R. Springer.; ISBN: 978-0-387-87457-9
- Cryer J. & Chan K.-S. 2008. Time Series Analysis with Applications in R, 2nd Edition. Springer.; ISBN: 978-0-387-75958-6
- Pebesma E. & Bivand R. 2023. Spatial Data Science: With Applications in R. Chapman and Hall/CRC.; ISBN: 978-0-4294-5901-6

## Completion methods

### Method 1

**Description:**

Both a seminar presentation during the course and an exercise work after the course need to be passed. They will be evaluated with scale 0-5.

**Evaluation criteria:**

Exercise work report 50%, seminar presentation 50%.

**Time of teaching:**

Period 3

Select all marked parts

**Parts of the completion methods**

x

### Teaching (5 cr)

**Type:**

Participation in teaching

**Grading scale:**

0-5

**Language:**

English, Finnish