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SYLLABUS

Statistical Methods, 6 ECTS Credits

COURSE CATEGORY   Master´s Programme in Statistics and Data Mining MAIN FIELD OF STUDY   Statistic - STA SUBJECT AREA   Statistics – ST1   COURSE CODE   732A49

AIM OF THE COURSE

The course provides a theoretical basis of statistical concepts and methods that are required for qualified work and research in statistics. After completion of the course, the student should be able to: - use knowledge of the common statistical distributions for building statistical models. - demonstrate a good understanding of the main principles within point estimation, interval estimation and hypothesis testing, - demonstrate a good understanding of the main concepts of Bayesian analysis, - build linear regression models, check their uncertainty and perform model comparison, - apply methods for sampling from large finite populations, - apply the basic imputation methods for model building and estimation.

CONTENTS

- Concept of probability - Random variable, common statistical distributions and their properties - Point- and interval estimation - Hypothesis testing - Simple and multiple linear regression, t-test and F-test. Residual and outlier analyses - Likelihood, prior and posterior distribution, and Bayes theorem - Concept of Markov chains - sampling with and without replacement. - imputation for model building

TEACHING

The teaching comprises lectures, seminars, and computer exercises complemented by self-studies. The lectures are devoted to presentations of concepts, theories and methods. The computer exercises provide practical experience of statistical analysis. The seminars comprise presentations and discussions of various assignments.

EXAMINATION

Written reports on the computer assignments. One final written 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.

ADMISSION REQUIREMENTS

For acceptance to the course, the student must have a bachelor’s degree with a total of at least 90 ECTS credits (1.5 years of full-time studies) in mathematics, applied mathematics, statistics, or computer science. The undergraduate courses in mathematics should include both calculus and linear algebra. Basic undergraduate courses in statistics and computer science are also required. Documented knowledge of English equivalent to Engelska B/Engelska 6: internationally recognized test, e.g. TOEFL (minimum scores: Paper based 575 + TWE-score 4.5, and internet based 90+TWE-score 20), IELTS, academic (minimum score Overall band 6.5 and no band under 5.5), or equivalent.

GRADING

The course is graded according to the ECTS grading scale A-F

CERTIFICATE

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.

COURSE LITERATURE

The course literature is decided upon by the department in question.

OTHER INFORMATION

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.

Statistical Methods Statistical Methods   Department responsible for the course or equivalent: IDA - Department of Computer and Information Science             Registrar No: LiU-2012-01412   Course Code: 732A49           Exam codes: see Local Computer System       Subject/Subject Area : Statistic - STA                       Level   Education level     Subject Area Code   Field of Education   A1X   Advanced level     ST1   SA   The syllabus was approved by the Board of Faculty of Arts and Science 2014-05-28