COURSE SYLLABUS

Applied Multivariate Data Analysis 7.5 credits

Second cycle, W7001M
Version
Course syllabus valid: Autumn 2020 Sp 1 - Present
The version indicates the term and period for which this course syllabus is valid. The most recent version of the course syllabus is shown first.

 Education level Second cycle Grade scale G U 3 4 5 Subject Mathematical Statistics Subject group (SCB) Mathematical Statistics

Entry requirements

Selection

The selection is based on 20-285 credits

Course Aim

After finished the course the student will be able to:

- Summarize and analyze data with different level of complexity and be able to choose and apply different statistical/analytical methods depending on the aim of a study. Focus will be on multivariate projection methods.

- Judge when and if the methods are suitable to use based on knowledge about underlying assumptions, drawbacks and advantages of the different methods.

- Use statistical programs for planning and designing experiments and for processing and analyzing data of different complexity.

- Write reports and orally present the results of multivariate data analysis.

Contents

When working in the wood industry or in the academy sector it is of great advantage to have skills in handling and analyzing measured data and being able to choose and use statistical methods. Thus, in this course the student will learn how to use some statistical methods and also receive knowledge of many important control measures that is used in multivariate data analysis. The content of the course are:

Basic statistics and concepts, Hypothesis testing, Confidence of interval, Linear regression, Principal Component Analysis, Projection to Latent Structures and Design of experiments

Realization

The student is responsible for his/her own knowledge building and will work both alone and with other students. Literature, study guide and lessons introduce different issues that will be applied and examined at laboratory assignments. The theory lessons can be pre-recorded. Guidance by supervisors is given regularly, individually or in group.

The laboratory assignments are self-instructional and are the core in the learning process. Many of the data sets used originate from the research in wood technology. In this way the student will get insight into the nature of the wood research area.

After each section, students send questions raised on literature, theory lessons and laborations to the supervisors. To enhance the knowledge building the supervisors distribute the answers/explanations to all students in the course.

The last section consists of a student project where the student work alone or in pair. Each student analyzes a dataset from the wood research area and presents the results in a report and an oral presentation.

All material, except the main book, will be available at a web based learning platform.

Examination

The course will examined by:

-          Laboration assignments.

-          A student project.

-          A written exam (divided on each section).

All these parts will be graded with differentiated grades.

Examiner

Literature. Valid from Autumn 2012 Sp 1 (May change until 10 weeks before course start)
- Main book: Multi- and Megavariate Data Analysis Part I, Basic Principles and Applications (Ericsson et al.. Second revision and enlarged edition, 2006 or later).
- Additional material and extra reading will be available at the web-learning platform.

Course offered by
Department of Engineering Sciences and Mathematics

Modules
0001Laboratory Work 1G U 3 4 51.50MandatoryA12
0002Laboratory Work 2G U 3 4 51.50MandatoryA12
0003Laboratory Work 3G U 3 4 51.50MandatoryA12
0004Laboratory Work 4G U 3 4 51.50MandatoryA12
0005ProjectG U 3 4 51.50MandatoryA12

Study guidance
Study guidance for the course is to be found in our learning platform Canvas before the course starts. Students applying for single subject courses get more information in the Welcome letter. You will find the learning platform via My LTU.

Syllabus established
by Dept. TVM Mats Näsström 14 Mar 2012

Last revised
by Niklas Lehto 14 Feb 2020