# Syllabus for Applied Statistics

## Syllabus

• 5 credits
• Course code: 1MS926
• Education cycle: First cycle
• Main field(s) of study and in-depth level: Mathematics G1F, Technology G1F, Sociotechnical Systems G1F
• Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
• Established: 2016-03-08
• Established by:
• Revised: 2018-08-30
• Revised by: The Faculty Board of Science and Technology
• Applies from: week 30, 2019
• Entry requirements: Probability and Statistics.
• Responsible department: Department of Mathematics

## Learning outcomes

On completion of the course, the student should be able to:

• use the most common statistical tests and understand their assumptions and limitations;
• formulate and choose a suitable methodology for testing in a given situation;
• use the most common estimation methods (e.g method of moments or the maximum-likelihood method);
• perform estimation in regression models and evaluate a proposed model;
• evaluate results from statistical software (e.g R).

## Content

Statistical hypothesis testing (interpretation with confidence intervals, p-values), estimation methodology (ML and LS estimation), non-parametric methods, correlation analysis, multiple regression (estimation, prediction, diagnostics).

## Instruction

Lectures, computer sessions. Guest lecture. Case studies where the course content is applied in problems arising in technology, the natural och social sciences.

## Assessment

Written examination at the end of the course (4 credits) combined with assignments given during the course (1 hp).

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.

## Other directives

This course cannot be included in the same degree as the course 1MS026.

## Syllabus Revisions

Applies from: week 50, 2019

Some titles may be available electronically through the University library.

• Alm, Sven Erick; Britton, Tom Stokastik : Sannolikhetsteori och statistikteori med tillämpningar

Liber, 2008

Find in the library

Mandatory

• Kompendium, Grundläggande regressionsanalys. Eva Enquist.

Matematiska institutionen,

Mandatory