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Course 2011-2012 a.y.

30050 - APPLICATIONS FOR ECONOMICS, MANAGEMENT AND FINANCE


BIEMF

Course taught in English


Go to class group/s: 16 - 17 - 18

BIEMF (7 credits - I sem. - OB  |  SECS-P/06)
Course Director:
ARNSTEIN AASSVE

Classes: 16 (I sem.) - 17 (I sem.) - 18 (I sem.)
Instructors:
Class 16: ARNSTEIN AASSVE, Class 17: ARNSTEIN AASSVE, Class 18: ARNSTEIN AASSVE


Course Objectives
The purpose of the course is to enable students to structure and conduct autonomously a research project based on the analysis of data sets concerning business, finance, economics and in general the social sciences. The course presents a set of tools with an applied perspective, providing the methodological knowledge that is necessary to conduct such projects with a fair level of competence and with the ability to choose appropriate statistical methods for various problems. The course gives support for the use of the software program SPSS, a widely used software package in the social sciences, though students are free to use other softwares.

Course Content Summary
  • Introduction to applied research, research design, research question, causality
  • Sampling and data sources, finding data for research projects
  • Regression analysis: The simple one regressor case, multivariate regression, assumptions and properties, violation of assumptions and remedies, time series analysis and seasonality.
  • One and two factors ANOVA
  • Factor analysis: model, extraction, rotation, interpretation
  • Scale construction and evaluation: reliability analysis and composite scores
  • Cluster Analysis
  • Regression analysis revisited: regression analysis in combination with factor analysis and cluster analysis, binary response models

Detailed Description of Assessment Methods

The written exam makes up 70% of the grade and the project 30%. The exam is written and lasts two hours.


Textbooks

NCT: P. Newbold, W.L. Carlson, B. Thorne, Statistics for Business and Economics and Student CD, 6/E, Prentice Hall (International Edition), 2007. For regression and ANOVA.

Lecture notes (sampling, data sources, research design, factor analysis, scale reliability and cluster analysis)


Prerequisites
Statistics, Mathematics, Computer skills for economics
Last change 12/05/2011 17:33