# 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:**

2020-2021, 2021-2022, 2022-2023, 2023-2024

## Tweet text

Analyzing environmental data with the R program.

## Description

An advanced course related to analyzing environmental and spatial data with R Statistics software. Main topics include statistical inference, multivariate methods (for modeling, classification, clustering, and dimension reduction), meta-analysis, analyses of time series, community composition analyses, and analysis methods for spatial data.

## Learning outcomes

After completing this course, the student should be able to

- Study the assumptions of statistical methods, like normality and homoscedasticity.
- Study bivariate correlations using relevant methods for different data types.
- Fit regression models to data containing independent observations, and perform variable and model selection.
- Fit non-linear dose – response models.
- Use regular statistical tests, like analysis of variance and non-parametric methods, correctly, and perform multiple comparisons.
- Analyze data obtained from repeated measurements studies.
- Calculate effect sizes and response ratios, and combine results from several research studies with suitable meta-analysis methods.
- Describe the basic principles of statistical learning, and resampling methods.
- Use classification, clustering, and projection methods for high-dimensional data.
- Use methods related to the analysis of time series, both in time domain and in frequency domain.
- Use community composition analysis methods.
- Read spatial data into R, and perform some rather simple spatial analysis methods with it.
- Use the R Statistics software well enough to solve an exercise work with it.

## Description of prerequisites

TILP2500, TILP2600 (Basics of statistics), and BENA4033 Basics of geoinformatics (or other basic knowledge of geoinformatics).

## 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
- Bivand R., Pebesma E. & Gomez-Rubio V. 2013. Applied Spatial Data Analysis with R, 2nd Edition. Springer.; ISBN: 978-1-4614-7617-7

## 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%.

Select all marked parts

**Parts of the completion methods**

x

### Teaching (5 cr)

**Type:**

Participation in teaching

**Grading scale:**

0-5

**Language:**

English, Finnish

**Study methods:**

Lectures 12 x 2 h, computer exercises 12 x 4 h, seminar, exercise work.