LEARNING OUTCOMES
After passing the course the students can conduct simple multivariate statistical analyses. They are familiar with common multivariate data analysis techniques. Students are familiar with different multivariate location and scatter functionals and the corresponding estimates and they understand the basic properties of these functionals. Students know how to apply principal component analysis and how to robustify the method. Students can conduct bivariate and multiple correspondence analysis and interpret the findings. Students are familiar with canonical correlation analysis and they can apply the method in practice. Students know several approaches to discriminant analysis and classification including different depth based methods. They are also able to assess the goodness of the classification. Students are familiar with hierarchical clustering methods and moving centers -type clustering methods and they understand the restrictions of these approaches. Moreover, students are not only able to apply multivariate methods in practice, but they also understand the mathematics and the reasoning behind the methods.
Credits: 5
Schedule: 10.01.2022 - 14.04.2022
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Pauliina Ilmonen
Contact information for the course (applies in this implementation):
About the lectures: pauliina.ilmonen@aalto.fi
About the exercises: jaakko.pere@aalto.fi
CEFR level (valid for whole curriculum period):
Language of instruction and studies (applies in this implementation):
Teaching language: English. Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Content
valid for whole curriculum period:
The course is an introduction to multivariate statistical analysis. Course topics include multivariate location and scatter, principal component analysis (PCA), robustness and robust PCA, bivariate correspondence analysis, multiple correspondence analysis (MCA), canonical correlation analysis, discriminant analysis, statistical depth functions, classification and clustering. Software R is used in the exercises of this course.
Assessment Methods and Criteria
valid for whole curriculum period:
Homework assignments, exercise points, exam, compulsory project work.
applies in this implementation
You are expected to:
-Attend the lectures and be active - not compulsory, no points, but highly recommended.
-Submit your project work on time - THIS IS COMPULSORY - max 6 points.
-Take the exam - max 24 points.
-Participate to weekly exercises (group 1, group 2, group 3 OR group 4) - not compulsory, but highly recommended - max 3 points.
-Be ready to present your homework solutions in the exercise group - not compulsory, but highly recommended - max 3 points.
Max total points = 6 + 24 + 3 + 3 = 36. You need at least 16 points in order to pass the course.
How to get a good grade?
-Attend the lectures and be active!
-Work hard on your project work.
-Be active in the exercises!
-Study for the exam!
Grading is based on the total points as follows: 16p -> 1, 20p -> 2, 24p -> 3, 28p -> 4, 32p -> 5.
Workload
valid for whole curriculum period:
Lectures 24h (2), Exercises 24h (2), Project work 40h, Homework assignments 30 h, reading and studying the lecture materials 20 h
DETAILS
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Language : English
Teaching Period : 2022-2023 Spring III - IV
2023-2024 Spring III - IVEnrollment :
Registration for Courses: In Sisu (sisu.aalto.fi).