Environmental data processing

Part 1: Univariate and bivariate statistics in the environment

The course deepens the statistical knowledge needed by science students.

It focus on bivariate statistics with particular interest for three essential pratical situiations:

• case of a qualitative explanatory variable and a continuous response variable
• case of continous explanatory and response variables
• case of qualitative explanatory and response variables

Part 2: Introduction to R

The student will be required to use R through the RStudio interface. They will perform statistical processing using lines of code, while simultaneously being introduced to fundamental concepts of statistics.

Course content:

• Introduction to data management and basic database exploration
• Review of basic statistical concepts and their implementation in code
• Introduction to creating basic graphs in RStudio
• Basic concepts of statistical tests (context, errors, implementation, p-value, etc.) and their coding in R, with interpretation
• Basic concepts of linear regression and its implementation using RStudio

Part 3: Statistical arguments

Voir la verison française (cours donné en français)

Part 1: Univariate and bivariate statistics in the environment

The course aims at providing the students with the necessary tools to

• Understand and use bivariate statistical tools and linear regression.
• Understand statistical results in their context. Be able to read, in a critical way, numerical or graphical statistical results (linked to the methods seen in the course).
• Understand the challenges, benefits and limitations of statistical studies.
• Having the necessary vocabulary/background to be able to interact with a statistician in the context of an environmental problem.
• Be able to use the statistical software R and interpret its output.

Part 2: Introduction to R

At the end of this course unit, the student will be able to:

• Use the open-source program R through the RStudio interface by writing code
• Import a file into their workspace
• Manipulate and explore a database
• Perform basic statistical analyses
• Carry out several statistical tests via the RStudio interface and interpret the results
• Perform a linear regression using RStudio
• Search online for information related to coding

Part 3: Statistical arguments

Voir la version française (cours donné en français)

Part 1: Univariate and bivariate statistics in the environment

Notions of mathematics.

Basics of statistics: probability, descriptive (univariate) statistics, confidence intervals, hypothesis testing (Z-test and one-sample t-test).

 

 

 

 

 

 

Part 2: Introduction to R

In general terms:

• Knowledge of how to use a computer
• Basic knowledge of Excel
• Basic knowledge of statistics (descriptive statistics)

• Configure the keyboard correctly according to the keys (AZERTY or QUERTY)
• Know how to install a computer program on his/her computer

Part 1: Univariate and bivariate statistics in the environment

Lectures and exercices (written and computer-based)
 

Part 2: Introduction to R

A theoretical in-person lecture will be dedicated to explaining the fundamental concepts of statistical tests and linear regression.

One in-person exercise session will focus on database management, and a second in-person exercise session will address data manipulation.

The remaining teaching activities will be delivered through videos on the e-campus platform. In these videos, the theoretical concepts presented will be directly applied using the R software. Students will then be invited to test their understanding of these concepts and practice the R commands introduced through quizzes on e-campus.

Part 3: Statistical arguments

Voir la version française (cours donné en français)

Part 1: Univariate and bivariate statistics in the environment

Blended learning


Additional information:

Classes will be given face-to-face on Arlon campus.

Part 2: Introduction to R

Blended learning


Further information:

Mix of in-person activities and video content on e-campus.

Part 3: Statistical arguments

Blended learning


Further information:

Voir la version française (cours donné en français)

Part 1: Univariate and bivariate statistics in the environment

The course slides and exercises will be made available through eCampus.

 

 

Part 2: Introduction to R

Platform(s) used for course materials:
- eCampus


Further information:

The lecture notes and slides from the in-person theoretical sessions will be made available to students.

The remainder of the course will rely on videos available on the e-campus platform and tests to be completed on this platform.

Regarding basic Excel knowledge, we refer, for example, to the following site:

https://openclassrooms.com/fr/courses/7168336-maitrisez-les-fondamentaux-dexcel

Part 3: Statistical arguments

Not translated yet

Part 1: Univariate and bivariate statistics in the environment

Exam(s) in session

Any session

- In-person

written exam ( open-ended questions )

Part 2: Introduction to R

Exam(s) in session

Any session

- In-person

written exam


Further information:

Specific note exclusively for students enrolled in the Advanced Master's in Risk and Disaster Management in the Anthropocene Era.

Students in this specialised master's programme who only take Part 2 of the course will be exempt from certain in-depth comprehension questions intended for students who have also completed Parts 1 and 3. The questions from which they are exempt will be clearly indicated on the exam paper.

Part 3: Statistical arguments

Exam(s) in session

Any session

- In-person

written exam ( open-ended questions )

Part 1: Univariate and bivariate statistics in the environment

See the notes available in the course ENVT0048-2 to freshen up the basics of statistics. These notes will be made available through eCampus.

 

 

 

Part 2: Introduction to R

The student is requested to inform the teacher if he/she does not have a personal computer.

Part 1: Univariate and bivariate statistics in the environment

Part 3: Statistical arguments

Gentiane Haesbroeck (G.Haesbroeck@uliege.be)