Applied Statistics
A.Y. 2024/2025
Learning objectives
The course aims at giving the student experience of carrying out statistical analyses.
Expected learning outcomes
Knowledge:
- recognize certain data types, identify and specify appropriate statistical models, and argue for the appropriateness.
- explain the prerequisities, prospects and limitations of the methods.
Skills:
- formulate relevant problems and choose an appropriate statistical model addressing these problems.
- carry out the actual analysis (computations). This includes model fitting, model validation and hypothesis testing.
- extract relevant estimates, draw conclusions and communicate the results from the analysis.
- use the statistical programming language R to carry out the analyses.
Competences:
- independently formulate scientifically relevant questions - motivated by data of similar types as those presented in the course - and answer them by the use of statistical methods.
- recognize certain data types, identify and specify appropriate statistical models, and argue for the appropriateness.
- explain the prerequisities, prospects and limitations of the methods.
Skills:
- formulate relevant problems and choose an appropriate statistical model addressing these problems.
- carry out the actual analysis (computations). This includes model fitting, model validation and hypothesis testing.
- extract relevant estimates, draw conclusions and communicate the results from the analysis.
- use the statistical programming language R to carry out the analyses.
Competences:
- independently formulate scientifically relevant questions - motivated by data of similar types as those presented in the course - and answer them by the use of statistical methods.
Lesson period: First semester
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Lesson period
First semester
AGR/02 - AGRONOMY AND FIELD CROPS - University credits: 7
Practicals: 64 hours
Lessons: 24 hours
Lessons: 24 hours