Statistics B - Regression analysis and analysis of variance
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.
Course evaluation
Additional course evaluations for MS0071
Academic year 2023/2024
Statistics B - Regression analysis and analysis of variance (MS0071-40198)
2024-03-20 - 2024-06-02
Academic year 2022/2023
Statistics B - Regression analysis and analysis of variance (MS0071-40082)
2023-03-22 - 2023-06-04
Academic year 2021/2022
Statistics B - Regression analysis and analysis of variance (MS0071-20167)
2021-11-02 - 2021-12-02
Academic year 2020/2021
Statistics B - Regression analysis and analysis of variance (MS0071-20162)
2020-11-02 - 2020-12-02
Academic year 2019/2020
Statistics B - Regression analysis and analysis of variance (MS0071-20156)
2019-11-01 - 2019-12-03
Syllabus and other information
Syllabus
MS0071 Statistics B - Regression analysis and analysis of variance, 7.5 Credits
Statistik B - Regressions- och variansanalysSubjects
Mathematical StatisticsEducation cycle
Bachelor’s levelModules
Title | Credits | Code |
---|---|---|
Part 1 | 6.0 | 0102 |
Part 2 | 1.5 | 0103 |
Advanced study in the main field
First cycle, in-depth level of the course cannot be classifiedBachelor’s level (GXX)
Grading scale
The grade requirements within the course grading system are set out in specific criteria. These criteria must be available by the course start at the latest.
Language
EnglishPrior knowledge
120 credits in the first cycle5 credits basic statistics
English 6
Objectives
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
Content
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.
Grading form
The grade requirements within the course grading system are set out in specific criteria. These criteria must be available by the course start at the latest.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 a student has failed an examination, the examiner has the right to issue supplementary assignments. This applies if it is possible and there are grounds to do so.
The examiner can provide an adapted assessment to students entitled to study support for students with disabilities following a decision by the university. Examiners may also issue an adapted examination or provide an alternative way for the students to take the exam.
If this syllabus is withdrawn, SLU may introduce transitional provisions for examining students admitted based on this syllabus and who have not yet passed the course.
For the assessment of an independent project (degree project), the examiner may also allow a student to add supplemental information after the deadline for submission. Read more in the Education Planning and Administration Handbook.
Other information
The right to participate in teaching and/or supervision only applies for the course instance the student was admitted to and registered on.
If there are special reasons, students are entitled to participate in components with compulsory attendance when the course is given again. Read more in the Education Planning and Administration Handbook.
Additional information
This course is given at half speed and as distance course, with a limited number of compulsory activities on Campus Ultuna, Uppsala.The 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
Litterature list
Experimental Design and Data Analysis for Biologists
Gerry P. Quinn
Michael J. Keough
CAMBRIDGE
UNIVERSITY PRESS
ISBN 978-0-521-00976-8