What This Document Is
This document presents a practical, hands-on exercise designed for students enrolled in Quantitative Business Analysis II (ECO 252) at West Chester University of Pennsylvania. It’s a computer-based project focused on applying econometric techniques to real-world economic data. The core task involves exploring the relationship between economic freedom and a key economic indicator – per capita income – across a diverse set of countries. It builds upon concepts learned in the course regarding regression analysis and variable selection.
Why This Document Matters
This assignment is crucial for students aiming to solidify their understanding of how to translate theoretical economic models into empirical analysis. It’s particularly beneficial for those who learn best by doing and want to gain practical experience with data manipulation and statistical software. Students preparing for further study in economics, finance, or related fields will find the skills honed through this exercise invaluable. It’s best utilized *after* mastering the fundamentals of regression modeling and data sourcing as covered in the course lectures and materials.
Common Limitations or Challenges
This exercise does not provide pre-defined datasets or step-by-step instructions for performing the analysis. Students are expected to independently locate and compile relevant data from various sources. The assignment requires a degree of self-direction and problem-solving skills, as the optimal approach to model specification and variable selection is not explicitly dictated. It also doesn’t offer a pre-determined “correct” answer; the emphasis is on the analytical process and the justification of findings.
What This Document Provides
* A clearly defined research question relating economic freedom to per capita income.
* Guidance on the scope of the analysis, including suggested sample sizes.
* A list of potential data sources, including websites offering economic indicators and country-specific information.
* Expectations regarding the use of statistical significance and model fit (R-squared) in interpreting results.
* A requirement for thoughtful commentary on the predictive power of different independent variables.