ABSTRACT
This chapter discusses the ways to do linear regressions. Here the function is linear, that is, it is estimated by two parameters: the slope and the intercept. When one faces a multivariate analysis, the estimation gets more complex. The chapter covers how to interpret the different coefficients, how to create regression tables, how to visualize predicted values, and goes further into evaluating the Ordinary Least Squares assumptions, so that one can evaluate how well one's models fit. It allows us to estimate a model with multiple control variables that have already been identified as relevant for explaining the variation of inequality in Latin America and Caribbean. Thus, the dependent variable is income inequality in Latin American and Caribbean countries, operationalized according to the Gini Index (gini). Visual inspection suggests that there is some clustering by country. That is, education expenditure by country usually stays within a range that slightly varies.
