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2019/2020  BA-BMECV1052U  Statistics

English Title
Statistics

Course information

Language English
Course ECTS 7.5 ECTS
Type Elective
Level Bachelor
Duration One Semester
Start time of the course Autumn
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 120
Study board
Study Board for HA/cand.merc. i erhvervsøkonomi og matematik, BSc
Course coordinator
  • Mette Asmild - Department of Finance (FI)
Main academic disciplines
  • Statistics and quantitative methods
Teaching methods
  • Face-to-face teaching
Last updated on 21-02-2019

Relevant links

Learning objectives
The basic objective of this course is to familiarize the students with the principles of probability theory and statistics. The student will acquire knowledge about what statistics and probability are and expand their experience base by applying a variety of probability and statistical principles in exercises and case studies. The goal is to enable the students to interprete and understand basic statistical concepts as they apply in business, economics, different types of companies or institutions and industries.


Following the course the students can:
  • Identify key theories, models and concepts of probability and statistics.
  • Use graphical and numerical methods for exploring and summarizing data on a single categorical or quantitative variable.
  • Describe basic probability and how probability helps us understand randomness.
  • Choose and justify appropriate descriptive and inferential methods for examining and analyzing data and drawing conclusions.
  • Analyze the association between categorical, discrete, and continuous variables, using contingency tables, correlation, regressions, and analysis of variance.
  • Communicate the conclusions and interpretations of statistical analysis.
Course prerequisites
None
Examination
Statistics:
Exam ECTS 7,5
Examination form Written sit-in exam on CBS' computers
Individual or group exam Individual exam
Assignment type Written assignment
Duration 4 hours
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Winter
Aids Limited aids, see the list below:
The student is allowed to bring
  • USB key for uploading of notes, books and compendiums in a non-executable format (no applications, application fragments, IT tools etc.)
  • Any calculator
  • Books (including translation dictionaries), compendiums and notes in paper format
The student will have access to
  • Access to Canvas
  • Access to the personal drive (S-drive) on CBS´ network
  • Advanced IT application package
Make-up exam/re-exam
Same examination form as the ordinary exam
If the number of registered candidates for the make-up examination/re-take examination warrants that it may most appropriately be held as an oral examination, the programme office will inform the students that the make-up examination/re-take examination will be held as an oral examination instead.
Course content, structure and pedagogical approach

The course will, through lectures and exercises, cover:

  • Descriptive statistics, both numerical and graphical.
  • Statistical inference; estimator, confidence intervals and significance tests of hypotheses.
  • Analysis of contingency tables.
  • Regression analysis; simple, multiple and covariance analysis.
  • One-way and two-way analysis of variance.
Description of the teaching methods
Lectures, exercises and computer classes.
Feedback during the teaching period
Discussions with lecturer and teacing assistant during lectures, exercises and computer workshops. Final exam only asessed with a grade, with no personal feedback. Answer to exam paper will be made available after the exam, enabling the students to compare their answers to the correct ones in order to understand the grade awarded.
Student workload
Attending lectures 28 hours
Attending exercises 18 hours
Attending computer (JMP) workshops 4 hours
Attending exam 4 hours
Preparation for lectures 30 hours
Preparation for exercises 20 hours
Preparation for JMP workshops 12 hours
Revisions before exam 90 hours
Expected literature

Book: Agresti A., C. Franklin (2014): “Statistics: The Art and Science of Learning from Data, Perason New International Edition”, Third Edition.
Supplementary notes

Last updated on 21-02-2019