Jack Minardi & Chris Lioi
Jack Minardi and Chris Lioi
Many notions of happiness exist, and most of them are subjective and hard to quantify. However, many people have proposed various schema for the quantification of happiness. Many of these are the result of surveys and self-assessments questionnaires that aim to assign a number on a certain scale indicative of how happy a person is. What sort of factors affect happiness? Or perhaps a weaker but more reasonable question, what sort of factors affect a certain numerical quantification of happiness? It may be that metrics based on different characterizations of happiness are affected by different things.
Our project proposal is to find what (if any) such correlations exist for a certain metric (or metrics). In particular, there is ample data offered by the organization World Database of Happiness. Their website is “an ongoing register of scientific research on the subjective enjoyment of life”. It lists several metrics of happiness by nation or geographical region. There is also another measure known as Gross National Happiness  (GNH) named such to parallel the concept of Gross National Product. It is claimed to be a better measure of a given country’s success as compared to GDP. Either one of these databases may be used. Since the data presented in the World Database of Happiness does not seem to be easily downloadable, we will write a python script t scrape the site and collect the needed data. We will correlate these metrics with various other data for the world’s nations, such as population, GDP, average age, average lifespan, and any other variables that would appear to be of consequence. These other data may be obtained easily from any number of public data sources, such as the CIA Factbook. Again, if the data is not presented in an easily downloadable format, such as an excel file, our plan is to write simple scripts to scrape the website for the relevant data. The data analysis, in the form of linear regression using gradient descent, will be done in either R or MATLAB. We plan on writing the algorithms ourselves to get a better understanding of how they operate.
In the end we hope to be able to present statistics that show how many different metrics are related to happiness, and hope to gain a better insight into what makes us happy. Using the tools learned in the class we will be able to show how strongly the different measures are correlated, and what seems to contribute the most to overall happiness.