This a reflection for PSDA course (PROBABILITY & STATISTICAL DATA ANALYSIS) Project2.
The difference between this project 2 and the previous project 1 is that we have collected the data on our own for the first project, but now I'm using secondary data and some tests and analysis will be used as well.
For the Dataset, I have picked (World happiness score 2019) from keggle.com, I'm interested to study more about the world happiness to know what are the things the influence the happiness of the countries and the relationships between the factors. I have also get a bit deeply and divided the world into three contents, also the European countries into Eu members and non Eu members to do more accurate tests
After doing the tests and analysis, I could come with a conclusion that the Global Happiness Index does not depend on one thing but on several things combined, but the most influential was the income of the individuals (GDP per capita).
I loved dealing with the datasets.. I predict something then I find out that I was totally wrong, which is the most interesting thing.
This project helped me a lot to deal with data, so all datasets are useless if you don't know what to do or how to deal with it, so after doing this project I got to know so many things - especially R language- and how to come with a conclusion from any data I have.
Hopefully, I will deal with other datasets as well and I will publish my reports here.
The report for this project can be viewed from the following link:
Also, here are some graphs for the realtionships between some factors: