• Identify and delimit a given statistical problem including identification of necessary and sufficient information to perform a relevant statistical analysis.
• Perform basic data manipulations with a given data material using statistical software.
• Use graphical and numerical methods to summarize the most important tendencies and associations in a given data material using statistical software.
• Justify the use of a given statistical method and evaluate the strengths and weaknesses of this compared to other methods.
• Demonstrate an understanding of the basics of probability and use these to quantify the uncertainty that exist in a given data analysis.
• Independently conclude based on a given statistical analysis, critically reflect on the validity and reliability of the results, and communicate these in a clear, correct, and non-technical language

The course aims to prepare students to conduct quantitative data analysis. The focus is on the capacity to conduct independent analyses using different types of data material, to interpret their results, and to understand and evaluate the assumptions on which the analyses rest. 

The course consists of lectures and exercises. The lectures will have a teacher driven review of a focused part of the theoretical course content. Student will be involved through brief peer-to-peer discussions and class discussions.

The exercise will focus on applying the methods from the curriculum and lectures to solve data analytical problems. In addition to a focus on understanding and interpreting statistical methods, a key element will be the introduction to a statistical software program such as Stata or R. Since the purpose of the exercises is to facilitate that the students apply the methods to real data problems, a high degree of student involvement is expected. 

The exercise teacher will assist students in applying the methods and direct discussions and interpretations of results. 

The problem sets in the exercise will to the extent that it is possible rely on data that is relevant to European Business. 

Students will have the option to hand in two voluntary homework assignments. These can be up to five pages and answered in groups of up to five students. There will be no supervision on the homework assignments and feedback will be provided on a general basis to all students.

Fulfilment of the learning objectives of the course will make the students able to evaluate an appropriately formulated research questions in for example a Bachelor's thesis using statistical methods.

Note: This course starts in Autumn 2018

The full curriculum will be announced in the Fall, but it will include:

Kosuke Imai (2017): Quantitative Social Science. Princeton University Press.

Carter, Susan Payne, Kyle Greenberg, and Michael S. Walker. "The impact of computer usage on academic performance: Evidence from a randomized trial at the United States Military Academy." Economics of Education Review 56 (2017): 118-132.