Factor analysis summarizes many variables by a few factors and helps to understand the structure of a correlation matrix. It takes into account multicollinearity between a large number of interdependent independent quantitative variables by grouping the variables into a few factors and reduces correlations. In our case, we have countries as observation units. We have data on different aspects of these countries like population, density, percentage of people living in cities, religion, life expectancy, literacy rates, daily calorie intake, number of people affected by AIDS, fertility, mortality rates, etc. For the purpose of this lab, we take LIFEXPF (female life expectancy) as the dependent variable and run a regression on it. However, before doing this, we perform factor analysis on other independent variables and group them into a few factors and use these factor scores as independent variables for regression. This will help reduce the correlations between the independent variables present in the model. The results of the factor analysis are analyzed below in different sections followed by the interpretation of the regression analysis. 1) The suitability of the data set for the factor analysis (mention the correlation matrix and that of Bartlett)Here I want to explain more about the dataset I use for factor analysis. The dataset contains a lot of missing information for the independent and dependent variables. Thus, I exclude all observations with missing cases to improve the analysis and model. First, I ran a correlation matrix for all independent variables to examine the strength of their relationship with the dependent variable LIFEXPF. From the correlation matrix, I find that variables like Population in thousands, Number of people per square km, region or economic group, AIDS cases, log base 10 of AIDS, log base 10 of population, climate correlation predominant with LIFEXPF are not significant. . As they are not significantly correlated, I exclude these variables from my model.