Statistical Data Analysis for Scientists and Engineers (B-KUL-G00C7A)
Aims
The course serves as a comprehensive introduction to multivariate statistics and data analysis, using the freeware statistical software R.
Upon completion of this course the student should
• Know the main multivariate statistical concepts (such as covariance, correlation, Mahalanobis distance, equivariance);
• Be able to summarize and visualize multivariate data;
• Be able to perform hypothesis tests related to multivariate data;
• Be able to perform a linear regression analysis;
• Know the strengths and weaknesses of the methods, and in which situations their use is appropriate;
• Have a critical attitude about each statistical method, know its underlying assumptions and how to verify them;
• Be able to carry out these methods by means of the R software;
• Be able to interpret the results of a statistical analysis;
Previous knowledge
The student has:
• Good knowledge of basic mathematics, including linear algebra and calculus
• Followed at least one basic course of probability and statistics
• Knowledge of confidence intervals and hypothesis testing
This online course will be available from the 2nd semester 2023-2024.
Is included in these courses of study
- Master in de geologie (Leuven) 120 ects.
- Master of Geology (Programme for students started before 2023-2024) (Leuven et al) 120 ects.
- Doctoral Programme in Science (Leuven)
- Master of Biology (Leuven) 120 ects.
- Master of Geology (Programme for students started in 2023-2024 or later) (Leuven et al) 120 ects.
- Preparatory Programme: Master of Sustainable Development (Leuven) 12 ects.
Activities
3 ects. Statistical Data Analysis for Scientists and Engineers (B-KUL-G00C7a)
Content
This is an online course where all learning materials are accessed via the edX platform. The course is available from the beginning of the academic year, but support and contact is only available from the 2nd semester onwards.
1. Introduction to R
2. Univariate data:
a. Exploratory data analysis (graphical and numerical summaries)
b. Random variables and distributions
c. Statistical models and estimators
d. Maximum likelihood estimators
e. Quantile-Quantile plot and test for normality
f. Transformation to normality
g. Inference about the mean
3. Bivariate data:
a. Exploratory data analysis, covariance, correlation
b. Bivariate distributions
c. Bivariate normal distribution
d. Simple linear regression
e. One-way analysis of variance
4. Multivariate data:
a. Exploratory data analysis
b. Multivariate distributions
c. Multivariate normal distribution
d. Statistical models and estimators
e. Test for multivariate normality
f. Inference about the mean (multiple testing)
5. Multiple linear regression:
a. The linear regression model
b. The least squares estimator
c. Properties of the least squares estimator
d. Analysis of variance
e. Statistical properties of the least squares estimator
f. Statistical inference
g. Verifying the model assumptions
h. Outlier detection
Course material
All material is available on edX
Format: more information
Asynchronous online learning
Evaluation
Evaluation: Statistical Data Analysis for Scientists and Engineers (B-KUL-G20C7a)
Explanation
Procedure on EdX platform to acquire a certificate. When following the MOOC for credits as part of your study programme, the exam is on campus and involves the analysis of data with R software. The exam will be organised in June, the retake in august or September.