[6 half days] - [starts on 18-04-2025 at 09:30] - [English] - [Louvain-la-Neuve]
Do you need a few reminders about statistics? Do you want to learn how to (better) use R? Do you want to be able to analyze data with a tool that is more and more widely used around the world?
The R language is a computing environment and a very flexible and rich progamming language used for statistical data analysis. It's a free and open source software that is regularly enriched with new libraries of functions provided by users. In particular, everyone can create a package.
Training aims
This training has two objectives. First, the standard tools of the R language will be introduced. Secondly, this training aims to give participants the necessary knowledge for the basic statistical analysis of their data starting with the importation and the formatting of the data.
Prerequisites
This training requires a basic knowledge of statistics.
Content
Location
Louvain-la-Neuve : SOCRATE 068 - Find the location on Google Maps
(This is an air-conditioned room so please cover yourself!)
- Place Cardinal Mercier, 10-12 - Rez-de-chaussée -
Attendance
The training is open to all but requires prior registration.
Time slots for training
18-04-2025 from 09:30 to 16:30
22-04-2025 from 09:30 to 16:30
24-04-2025 from 09:30 to 16:30
Rate
UCLouvain member (with an internal account), UCLouvain student : Training available free of charge but registration in advance is required.
Administrative fees will be applied in case of late cancellation.
SHS Namur member : 150 euros per day for a total of 450 euros
Invoice paid by university, UCLouvain clinic member, Researcher : 160 euros per day for a total of 480 euros
Company, Non-UCLouvain member : 250 euros per day for a total of 750 euros
Financial support
This training course is recognized by the IABE, enabling participants to earn CPD points.
(Note that this is true for all SMCS courses.)
For more information (open)
Tools used during training
R
Methods and method families discussed
Data processing
Data set cleaning and processing for statistical analysis
Descriptive statistics
Descriptive statistics
Summary tables
Charts
Parametric statistics of mean comparison
Student's t-test
ANOVA
Regression model
Simple linear regression
Logistic regression
Multiple linear regression