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Statistics I

The course Statistics I on advanced level should give the participants an increased understanding of and skills in using various statistical analyses.

The course aims to: (a) increase the participants’ understanding of basic statistical concepts, including probability, sampling distributions, resampling procedures and interval estimation; (b) provide the skills necessary for applied data analysis, including data management and quality assurance, data visualisation, choosing an appropriate analytical strategy and carrying out analyses using statistical software; and (c) provide training in communicating the results of statistical analyses in writing as well as orally with the help of slideshow software.

The course covers the following topics:

  • Data management, data screening, data visualisation
  • Probability, resampling techniques
  • Effect size and interval estimation
  • Correlation and linear regression
  • Variance analysis
  • Applied data analysis using statistical software.
  • Course structure

    As a registered student on this course you will find detailed course information and communication in the learning platform Athena. Login with your university account.

    This course will be given in the last quarter of the fall semester 2022.

    Course syllabus: Statistics I, 7,5 credits (102 Kb) . Autumn 2022.

    Teaching format

    Instruction is given in the form of lectures, seminars, and exercises in the use of statistical software.

    Course requirements/Mandatory components: Oral seminar presentation of a written report.

    Learning outcomes

    In order to pass the course, students are expected to be able to:

    1. understand and describe basic statistical concepts (probability, sampling distribution, resampling, effect sizes, interval estimation, etc.) and how they are related to common statistical methods, such as parametric and non-parametric tests of group differences, variance analysis and multiple linear regression.
    2. plan and carry out statistical analyses of data, including data screening, descriptive analysis, data visualisation and effect size estimation.


    The course is examined on the basis of a written examination and a written paper.


    Course leader: Professor Mats Nilsson,

  • Schedule

    The schedule will be available no later than one month before the start of the course. We do not recommend print-outs as changes can occur. At the start of the course, your department will advise where you can find your schedule during the course.
  • Course literature

    Note that the course literature can be changed up to two months before the start of the course.

    A complete list of course readings is found in the course syllabus.

    The list is subject to change until two months prior to the start of the course. In case the new course syllabus is missing above by then, please check with the course leader before you buy any expensive books etc.

  • Course reports

  • More information

  • Contact

    Registered students should primarily use Athena for teacher communication.

    To contact the Student office or a Student Councellor, see below.

    More contacts in Education

    Student office - Master's level
    Study councellor - Master's level