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Be English-friendly or any other language-friendly means that UC is taught in a language but can either of the
following conditions:
1. There are support materials in English / other language;
2. There are exercises, tests and exams in English / other language;
3. There is a possibility to present written or oral work in English / other language.
1
6.0
0.0 h/sem
18.0 h/sem
0.0 h/sem
0.0 h/sem
0.0 h/sem
0.0 h/sem
1.0 h/sem
19.0 h/sem
131.0 h/sem
0.0 h/sem
150.0 h/sem
Since year
2016/2017
Pre-requisites
Previous exposure to descriptive and inferential statistics.
Objectives
This course aims to enhance the knowledge of econometric and statistics methods by learning different statistical techniques in an applied setting throughout the research process (planning, implementation and critical assessment) in the field of management sciences.
Program
1. Introduction 2. Regression analysis - Review of simple linear regression - Dummy variables - Multicollinearily and outlier observations - Exploratory and confirmatory approaches 3: Experimental analysis in management research (Conjoint analysis) - Experimental design and model specification - Data collection - Model estimation and validation - Applications in management sciences 4: Discrete choice models - Discrete choice models vs linear regression models - Model specification - Estimation and interpretation of results 5: Advanced Topics
Evaluation Method
The evaluation consists of a miterm exam (50%) and individual written report to deliver at the end of the course (50%). Students who fail (below 9.5) can apply for the re-sit following the PhD in Management rules.
Teaching Method
This course has 6 ECTS, 150 hours, of which 19 hours are of direct contact between academic staff and the students (18 hours of theoretical-practical classes and 1 hour tutorial orientation). The teaching methodology is a combination of theoretical-practical classes and classes in a computer lab, exposing the concepts and applying them using statistical packages (e.g., SPSS).
Observations
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Basic Bibliographic
Hair, Jr., J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate Data Analysis (6th ed.). Upper Saddle River, NJ: Pearson Prentice Hall. Lattin, J., D. Carroll e P. Green (2003), Analyzing Multivariate Data, Pacific Grove, CA: Thomson Learning. Franses, P.H. e R. Paap (2001), Quantitative Models in Marketing Research, Cambridge: Cambridge University Press.
