To familarize the business engineering student with the principles of explicative statistics.
Given the insecurity which comes with each business decision, a thorough knowledge of statistics is indispensable for an applied economist.
The course requires a thorough mathematical basis as provided in the first candidature of business engineering. A basis of probability calculus is required as well (course "Lineaire programmering en kansrekenen"). Moreover a mentality towards general analytical thinking is a plus.
Toledo / e-platform
Order of Enrolment
This course unit is a prerequisite for taking the following course units:
D0H26A : Production and Logistics Management (HIR)
Is also included in other courses
- Bachelor of Business Economics: Information Systems Engineering (Abridged Programme) 120 ects.
- Preparatory Programme: Master of Business Economics: Business Engineering 90 ects.
- Preparatory Programme: Master of Business Economics: Information Systems Engineering 90 ects.
- Bachelor of Business Economics: Business Engineering 180 ects.
- Bachelor of Business Economics: Information Systems Engineering 180 ects.
- Bachelor of Business Economics: Business Engineering (new registrations 2011-2012) 180 ects.
- Bachelor in Business Economics: Information Systems Engineering (new students 2012-2013 and students 2011-2012) 180 ects.
1. Classical explicative Statistics
- Sample surveys, sample index numbers, distribution of (functions of) sample index numbers, central limitation
- Point estimations and their qualities, methods of point estimation
- Reliability intervals of average(s), variance(s), proportion(s), correlation
- Tests of hypotheses for average(s), variance(s), proportion(s), correlation
- Multinomial populations: cross tables, homogenity
- Tests of postulated distribution function
2. Non-parametric methods
3. Computer-intensive methods: the bootstrap
4. The simple linear regression model
- Qualities of smallest square estimators
- Statistical interference
5. The multiple linear regression model
- Multicollinearity, heteroscedasticity, misspecification, autocorrelation
- Use of dummy variables
6. Variance analysis