Programa

Análise de Dados para Economia e Gestão

Licenciatura Bolonha em Gestão

Licenciatura Bolonha em Economia

Programa

Syllabus: 1. Why economic and business data analysis is important? 2. Fundamental concepts of statistical analysis and visualization of economic and business data 2.1. Probability of an event defined as its frequency of occurrence (frequentist view) 2.2. Population, sample, representative sample, and random sample (when the probability of each element of the population being selected into the sample is known to the researcher) 2.3. Statistical units of analysis, variables, and types of variables or data 2.3.1. Qualitative data (nominal and ordinal) and Quantitative data (discrete and continuous) 2.3.2. The case of continuous data grouped into (equal - vs. unequal-length) classes 2.3.3. Cross-sectional data, time-series data, longitudinal or panel data 3. Univariate data analysis and visualization (one variable) 3.1. Frequencies 3.1.1. Simple (absolute and relative) frequencies and cumulative (absolute and relative) frequencies 3.1.2. Frequency tables, bar and line plots, histograms 3.2. Location measures 3.2.1. Central tendency (mean, median, and mode) and non-central tendency (quartiles, deciles, and percentiles) location measures 3.2.2. Asymmetry and skewness (positive and negative, to the right and to the left) 3.2.3. The box-and-whisker diagram or boxplot 3.3. Dispersion measures 3.3.1. Absolute dispersion measures: range, interquartile range, standard deviation, and variance 3.3.2. Relative dispersion measures: the coefficient of variation 3.4. Concentration measures 3.4.1. Assessing how (un)equally distributed are the cumulative relative frequencies of a variable over the cumulative relative frequencies of the statistical units of analysis 3.4.2. The Lorenz curve and the Gini Index 3.5. Visualization of and measures for the analysis of time-series 3.5.1 Line plots and components of times series (trend, seasonality, cyclicality) 3.5.2. Absolute changes and rates of change 3.5.3. Index numbers, value-, quantity- and price indices 3.5.4. The deflator to transform a variable in current prices to constant prices; 4. Bivariate data analysis and visualization (two variables) 4.1. Linear and non-linear relationships between two variables and xy-scatterplots 4.2. Covariance and the linear correlation coefficient 4.3. The simple linear regression model 4.3.1. The ordinary least squares (OLS) method of fitting a linear regression to xy-data 4.3.2. Estimating the intercept and the slope of the linear regression model 4.3.3. The r-square (R2) measure of goodness-of-fit 4.3.4. Interpreting the intercept and the slope of the linear regression model and doing prediction