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805305A Introduction to Regression and Analysis of Variance, 5 ECTS cr 
Code 805305A  Validity 01.08.2017 -
Name Introduction to Regression and Analysis of Variance  Abbreviation Introduction to 
Scope5 ECTS cr   
Type Intermediate Studies Discipline3266 Statistics 
  Grading1 - 5, pass, fail 
Unit Field of Mathematics 

Jari Päkkilä 

ECTS Credits 

5 ECTS credits / 133 hours of work

Language of instruction 



Autumn term, 2nd period. Recemmended to be taken already in the 2nd year for those aiming at specialization in data science.

Learning outcomes 

Upon successful completion of the course the student can describe the basic concepts and main principles of regression and variance analysis with one or several explanatory variables, and is able to apply these methods in analysing a small scale data set as well as to apply the necessary computational tools.


Linear regression and analysis of variance models for continuous outcomes; Formulation of the model and interpretation of parameters; Fitting the models, estimation of parameters, and prediction with the method of least squares: Basic methods of model criticism and diagnostics; Use of R environment in modelling.

Mode of delivery 

Contact teaching

Learning activities and teaching methods 

Lectures 28 h, practicals 14 h, and independent work. The practicals include both homework and computer class exercises.

Target group 

Students of mathematical sciences and other interested. The course belongs to core studies for those with an orientation to data science. It is a prerequisite for those doing M.Sc. in computational mathematics and data science having data science as the specialization profile. The course is useful also for students of the Faculty of Science and the Oulu Business School as well as those of computer science or computational engineering, who have statistics as a minor subject.

Prerequisites and co-requisites 

806113P Introduction to Statistics or 806119P A Second Course in Statistics or corresponding abilities acquired otherwise.

Recommended optional programme components 

Is assumed as preliminary knowledge in the course 805306A Introduction to Multivariate Methods.

Recommended or required reading 

Lecture notes and material distributed during lectures and practicals. Recommended reading:James, G., Witten, D., Hastie, T., Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R}. Springer, New York; chapters 1-3. -- freely downloadable from

Assessment methods and criteria 

Practical exercises and final exam. Passing the course requires adequate participation in practical sessions and sufficient homework activity.


Numeric assessment scale from 1 to 5

Person responsible 

Jari Päkkilä

Working life cooperation 



Current and future instruction
Functions Name Type ECTS cr Teacher Schedule
registration period has not begun Introduction to Regression and Analysis of Variance  Course  Jari Päkkilä  01.09.20 -16.10.20

Future examinations
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