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Regression Methods, 7,5 ECTS Credits
COURSE CATEGORY   Single Subject Course
  COURSE CODE   732G28
After completion of the course, the student should be able to:
- employ regression models to examine relationships between random variables and derive linear predictors and classifiers from given data sets,
- assess the quality of given data sets and the generalization capacity of identified statistical relationships,
- account for widely used methods for regression analysis,
- account for selection, estimation and validation of regression models.
The course provides basic skills for professional work in which data are explored, modified, modeled and assessed by the use of different regression models.
The course content comprises practical as well as theoretical elements, for example:
- computer exercises,
- simple and multiple linear regression, logistic regression, Poisson regression,
- point and interval estimation and significance testing of model parameters,
- multicollinearity,
- residual analysis,
- model selection based on hypothesis testing and information criteria.
Computer exercises in which the students have access to supervision provide practical experience of data analysis. The teaching comprises lectures/tutorials and computer exercises. The lectures/tutorials are devoted to presentations of theories, concepts, and methods and reviews of computer exercises. Language of instruction: English.
Assignments encompassing computer-based data analysis. One final written or oral examination.

Students failing an exam covering either the entire course or part of the course two times are entitled to have a new examiner appointed for the reexamination.

Students who have passed an examination may not retake it in order to improve their grades.

Student’s entering the course should have passed at least one course in basic statistics, and at least one course in mathematics. Documented knowledge of English equivalent to "Engelska B"; i.e. English as native language or an internationally recognized test, e.g. TOEFL (minimum scores: Paper based 575 + TWE-score 4.5, and internet based 90), IELTS, academic (minimum score: Overall band 6.5 and no band under 5.5), or equivalent. Exemption from Swedish B,
The course is graded according to the ECTS grading scale A-F
Course certificate is issued by the Faculty Board on request. The Department provides a special form which should be submitted to the Student Affairs Division.
The course literature is decided upon by the department in question.
Planning and implementation of a course must take its starting point in the wording of the syllabus. The course evaluation included in each course must therefore take up the question how well the course agrees with the syllabus.

The course is carried out in such a way that both men´s and women´s experience and knowledge is made visible and developed.
Regression Methods
Regression Methods
Department responsible
for the course or equivalent:
IDA - Department of Computer and Information
Registrar No: LiU-2008/00111   Course Code: 732G28      
    Exam codes: see Local Computer System      
Subject/Subject Area : Statistik - STA          
Level   Education level     Subject Area Code   Field of Education  
G1X   Basic level     STA   SA  
The syllabus was approved by the Board of Faculty of Arts and Science 2008-12-20