1. Quantitative Coursework – Tiago Luna
Courses taken during Bachelor’s Degree
Economics I:
Microeconomics.
Grade obtained: 10/10
(A+)
• Concepts covered: law of demand; consumer’s theory; principal of marginal decreasing utility;
rational choice theory; production and cost functions, preference relations; marginal substitution
rate; causality, laws and comparative statics; Nash’s equilibrium; perfect competition;
monopolies, duopolies and oligopolies; bilateral relations; game theories (including incomplete
information game theory), consumers’ and producers’ index.
Economics II:
Macroeconomics.
Grade obtained: 10/10 (A+)
• Concepts covered: Globalization, human capital, liberalization, public policy analysis poverty,
informal employment, unemployment, wealth distribution, GDP, economy of war, rule of 70,
growth index calculation, production model analysis, technologies, Solow modeling, federal
reserve, debt, monetary and fiscal policies.
Descriptive Statistics
Grade obtained: 10/10 (A+)
• Concepts covered: Frequencies (cumulative, relative, relative comulative), statistical
parameters (central, positional and dispersive), mean, mode, dispersion modeling (range,
standard deviation, variance), percentages, statistical errors, degrees of freedom, sampling.
Inferential Statistics
Grade obtained: 10/10 (A+)
• Concepts covered: ANOVA, correlation, normal distribution, central limit theorem, error
theorems, chi-square distribution, chi-square contingency tables, f-statistics, calculation of SSW
and SSB.
Demography
Grade obtained: 10/10 (A+)
• Concepts covered: fertility, mortality, life expectancy, migration, age and sex composition,
population growth rate, special demography, longitudinal analysis, demographic modeling.
Quantitative Analysis Data Processing
• Concepts covered: linear regression, graphing, quadratic functions, curve areas, small and
large size confidence intervals, one tail and two tailed tests, confidence intervals, T-confidence
intervals.

2. • SPSS used for coursework. SPSS advanced instruction including Cluster analysis, K-means,
discriminants and factor analysis.