Objectives of Programme's Learning Outcomes

• Manage large-scale software development projects.
• Apply, mobilise, articulate and promote the knowledge and skills acquired in order to help lead and complete a project.
• Manage research, development and innovation.
• Organise and lead a research, development or innovation project to completion.
• Methodically research valid scientific information, lead a critical analysis, propose and argue potentially innovative solutions to targeted problems.
• Apply scientific methodology.
• Demonstrate thoroughness, independence, creativity, intellectual honesty, and ethical values.

Learning Outcomes of UE

At the end of the course, students will be able to use econometric techniques in order to analyse relations (models) explaining how variables related to economic or business issues behave. This way, they will test the existence of significant explanatory variables, estimate their impact and forecast variables under interest. The course will also enable them to interpret results and to use them for policy implications in terms of private or public management and of economic policy.

Content of UE

- Regression analysis : estimation, quality of fit, model selection criteria, testing hypotheses - Problems of multicollinearity, heteroscedasticity and serial correlation : consequences, testing, treating - Specification tests, approaches of modeling - Estimation with qualitative (binary) independent vairiables, seasonal effects and structural changes - Stata econometrical software

Prior Experience

Statistics

Type of Assessment for UE in Q1

• N/A

Q1 UE Assessment Comments

Not applicable

Type of Assessment for UE in Q2

• Written examination

Q2 UE Assessment Comments

Written examination (100%)

Type of Assessment for UE in Q3

• Written examination

Q3 UE Assessment Comments

Written examination (100%)

Type of Resit Assessment for UE in Q1 (BAB1)

• N/A

Q1 UE Resit Assessment Comments (BAB1)

Not applicable