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

Study level:
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