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Statistics B - Regression analysis and analysis of variance

Att analysera statistiska samband mellan olika variabler är användbart i många situationer. I denna kurs får du stifta bekantskap med några vanliga metoder, genomföra statistiska analyser med modern programvara och arbeta med analys av data i ett eget projekt.
I många sammanhang vill man studera samband mellan olika variabler och dra eventuella slutsatser. I kursen introduceras du till de kraftfulla och allmängiltiga statistiska metoder som finns inom regressions- och variansanalys. Du får också en överblick över vanliga försöksdesigner.
Vid praktiskt arbete med data är det viktigt att kunna välja en lämplig metod för ett givet problem, och att kunna utvärdera resultatet från en metod och dra rimliga slutsatser. I kursen arbetar du med frågeställningar kring data i datorövningar och tillämpar mot slutet dina kunskaper i ett eget litet projekt.
Welcome to the course MS0071
Welcome to the course Statistics B: Regression analysis and analysis of variance. The course will start on Monday the 2nd of November at 13.00 on zoom (live). Course literature: Experimental Design and Data Analysis for Biologists Gerry P. Quinn Michael J. Keough Schedule A detailed schedule can be found in the file schedule with content. The Course information has been sent to you individually by mail. Otherwise, see the course information file.pdf. on Canvas; invitation already sent. Accept it! Remember that you need to be registered for the course at the latest on Monday, Nov 2nd by self-registrations.

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MS0071-20162 - Course evaluation report

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Additional course evaluations for MS0071

Academic year 2019/2020

Statistics B - Regression analysis and analysis of variance (MS0071-20156) 2019-11-01 - 2019-12-03

MS0071 Statistics B - Regression analysis and analysis of variance, 7.5 Credits

Statistik B - Regressions- och variansanalys

Syllabus approved



Mathematical Statistics

Education cycle

First cycle


Title Credits Code
Part 1 6.00 1002
Part 2 1.50 1003

Advanced study in the main field

First cycle, course specialisation cannot be classified(GXX)

Grading scale

5:Pass with Distinction, 4:Pass with Credit, 3:Pass, U:Fail The requirements for attaining different grades are described in the course assessment criteria which are contained in a supplement to the course syllabus. Current information on assessment criteria shall be made available at the start of the course.



Prior knowledge

120 credits in the first cycle
5 credits basic statistics
English 6


The objective of the course is to give an overview of methods within the range of general linear models, such as regression analysis and analysis of variance. On completion of the course the student will be able to:
• describe general linear models, such as regression and ANOVA models, including conditions and assumptions
• select an appropriate general linear model for a given experimental or study design
• carry out a regression or ANOVA analysis in the statistical software R
• interpret and evaluate results correctly and draw reasonable conclusions
• clearly and concisely communicate results and conclusions


The course is built on lectures and computer exercises, as well as a project. The main components are as follows:
Part 1 (6 hp)
• Simple linear regression.
• Multiple linear regression.
• Analysis of variance with one or more fixed and random factors.
• Analysis of covariance, dummy variable coding and the general linear model.
• Analysis of residuals.
• Some extensions or alternatives to models if assumptions for the base models are not fulfilled, e.g. nonparametric methods, nonlinear models and models for data that are not independent.

Part 2 (1.5 hp)
Project work either alone or in a group.

Formats and requirements for examination

Part 1
- Passed examination in written form. Grade U, 3, 4, or 5 are given.

Part 2
- Passed project report and passed oral presentation at mandatory seminar. Discussions of other groups’ reports can also be part of the examination. Reports, oral presentations, discussions and presence at seminars are only graded with Pass (G) or Fail (U).

  • If the student fails a test, the examiner may give the student a supplementary assignment, provided this is possible and there is reason to do so.
  • If the student has been granted special educational support because of a disability, the examiner has the right to offer the student an adapted test, or provide an alternative assessment.
  • If changes are made to this course syllabus, or if the course is closed, SLU shall decide on transitional rules for examination of students admitted under this syllabus but who have not yet passed the course.
  • For the examination of a degree project (independent project), the examiner may also allow the student to add supplemental information after the deadline. For more information on this, please refer to the regulations for education at Bachelor's and Master's level.

Other information

  • The right to take part in teaching and/or supervision only applies to the course date to which the student has been admitted and registered on.
  • If there are special reasons, the student may take part in course components that require compulsory attendance at a later date. For more information on this, please refer to the regulations for education at Bachelor's and Master's level.

Additional information

This course overlaps with the following courses given at SLU:
ST0058 Statistik för ekonomer, 15 hp
MS0064 Variansanalys, 5 hp
HV0131 Avel 1 (moment variansanalys)
HV0132 Avel 2 (moment variansanalys)

Responsible department

Department of Energy and Technology

Further information

Determined by: Programnämnden för utbildning inom naturresurser och jordbruk (PN - NJ)
There are no Grading criteria posted for this course
1) Experimental Design and Data Analysis for Biologists
Author: Gerry P. Quinn, Michael J. Keough
ISBN: 978-0-521-00976-8

Course facts

The course is offered as an independent course: Yes Tuition fee: Tuition fee only for non-EU/EEA/Switzerland citizens: 19027 SEK Cycle: First cycle
Subject: Mathematical Statistics
Course code: MS0071 Application code: SLU-20162 Location: Uppsala Distance course: No Language: English Responsible department: Department of Energy and Technology Pace: 100%