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FORK 1002 Preparatory Course in Statistics

APPLIES TO ACADEMIC YEAR 2016/2017

FORK 1002 Preparatory Course in Statistics


Responsible for the course
Genaro Sucarrat

Department
Department of Economics

Term
According to study plan

ECTS Credits
0

Language of instruction
English

Introduction
This course focuses on statistical concepts and tools of relevance in business and managerial economics in particular, and in the social sciences more generally.

Learning outcome
To provide students with the understanding of the fundamentals of basic statistical principles (the skills necessary for interpretation and evaluation of data) sufficient knowledge for the adequate application of basic statistical procedures.

Prerequisites

All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have spesific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.

Compulsory reading
Books:
Groebner, David F., Patrick W. Shannon, Phillip C. Fry. 2014. Business statistics : a decision-making approach. 9th ed., International ed. Pearson. Selected chapters

Other:
Excercises, selected readings and hand-outs during the course


Recommended reading

Course outline
Key Concepts and Basic Statistics (4 hour)

  • Key Concepts
  • Descriptive statistics
  • Frequency and Probability Distributions
  • Hypothesis Testing
  • P-Values
  • Interval Estimation
Regression Analysis (8 hours)
  • Bivariate correlation analysis
  • The linear regression model
  • Estimation
  • Hypothesis testing

Qualitative independent variables (3 hours)
  • Qualitative vs. Quantitative variables
  • Dummy variables
  • Combining qualitative and Quantitative variables

Computer-based tools
SPSS
Stata


Learning process and workload
15 hours with lectures and 5 hours for the use of statistical software.

Practical examples and assignments will involve extensive use of statistical software, such as SPSS or STATA.


Examination
Not applicable


Form of assessment Weight Group size
Not applicable


Examination code(s)
Not applicable

Examination support materials


Re-sit examination
Not applicable

Additional information
Honour code. Academic honesty and trust are important to all of us as individuals, and are values that are integral to BI's honour code system. Students are responsible for familiarising themselves with the honour code system, to which the faculty is deeply committed. Any violation of the honour code will be dealt with in accordance with BI’s procedures for academic misconduct. Issues of academic integrity are taken seriously by everyone associated with the programmes at BI and are at the heart of the honour code. If you have any questions about your responsibilities under the honour code, please ask. The learning platform itslearning is used in the teaching of all courses at BI. All students are expected to make use of itslearning.