Environmental data processing

Part 1: Univariate and bivariate statistics in the environment

The course deepens the statistical knowledge needed by science students.

It presents bivariate descriptive statistics, linear regression and hypotheses tests that compare 2 or more parameters (2-sample t-test, F-test, ANOVA test, chi-squared test). 

The course focuses on the understanding of the statistical process, the ciritical interpretation of statistical results, and the application of the studied statistical methods, by making use of the statistical software R.

 

 

 

 

 

Part 2: Introduction to R

The student will be introduced to R through the RStudio interface. The basic notions of coding will be taught. They will carry out statistical processing using lines of code. He will also be introduced to the basic notions of RMarkdown.

Course content :

• Introduction to coding,
• Introduction to database management,
• Recall of the basic notions of statistics and translation into lines of code,
• Introduction to basic graphing in RStudio,
• Reminder of the hypotheses and statistical tests and translation into lines of code,
• Performing linear regressions using RStudio.

Part 3: Statistical arguments

Not translated yet

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, the student will be able to :

• use the open source program R through the RStudio interface with the help of programming lines,
• import a file into their workspace,
• carry out the basic statistics seen in part 1,
• search the internet for information on coding,
• render the result(s) in a report generated with RMarkdown.

Part 3: Statistical arguments

Not translated yet.

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

• 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 course consists of a theoretical part with an application directly in practical format. It is essential for the student to follow the course as RMarkdown concepts will be introduced throughout the course to prepare the student for the examination.

Part 3: Statistical arguments

Not translated yet

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

Face-to-face course

Part 3: Statistical arguments

Blended learning

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

Basic knowledge of Exce:

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 )


Additional information:

The assessment focuses on the correct use and understanding of the statistical methods, the interpretation of the results as well as the use of the statistical software R.

The exam consists of a practical examination in R/R Markdown (statistical analyses), including questions on the good understanding of the theory (justification of choices, explaining techniques, interpreting results, ...).

During the examination, students can either work on their own laptop or on a university computer. The examination is an open book examination. The files resulting from the student's work must be submitted, all together, in a single file submitted, within the allotted time, on the partim 2 e-campus platform at the end of the exam.

The examination deals with both partim 1 and partim 2, which are evaluated together. 



Any attempt at fraud will result in a zero rating. In particular, mobile phones are strictly forbidden all along the exam.


 

 

 

Part 2: Introduction to R

Exam(s) in session

Any session

- In-person

written exam ( open-ended questions )


Additional information:

Additional information:

The examination deals with both partim 1 and partim 2, which are evaluated together.

The assessment focuses on the correct use and understanding of the statistical methods, the interpretation of the results as well as the use of the statistical software R.

The files resulting from the student's work must be submitted, all together, in a single file submitted, within the allotted time, on the partim 2 e-campus platform at the end of the exam.

The exam consists of a practical examination in R/R Markdown (statistical analyses), including questions on the good understanding of the theory (justification of choices, explaining techniques, interpreting results, ...).

During the examination, students can either work on their own laptop or on a university computer. The examination is an open book examination.



Any attempt at fraud will result in a zero rating. In particular, mobile phones are strictly forbidden all along the exam.

Part 3: Statistical arguments

Exam(s) in session

May-June exam session

- Remote

written exam

August-September exam session

- In-person

oral exam

Written work / report

Continuous assessment

Out-of-session test(s)


Additional information:

Not translated yet

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

Professor: Laurent Loosveldt (l.loosveldt@uliege.be)

Assistant: Pierre Stas

 

 

Part 2: Introduction to R

Claudia Falzone: cfalzone@uliege.be

Part 3: Statistical arguments

N.Semal@uliege.be