Making Data Management Decisions 2

For LE, the most commonly endorsed response was 4 (35.68%), meaning that the life expectancy in about one-third of the countries is 65 – 75 years old. 

For IP, the most commonly endorsed response was 3 (69.95%), meaning that  70% people’s  income per person is less then 6000 .

For WEALTHstatus, the most commonly endorsed response was 3 (61.50%), meaning that 61.50% people are poor. 

For EHALTHstatus, the most commonly endorsed response was 1 (85.31%), meaning that 85.31% people are healthy. 

Making Data Management Decisions

Hello,

here I submit the Assignment 3 of course  Data Management and Visualization by Wesleyan University.

I use the responses for urbanrate,WEALTHstatus, HEALTHstatus, incomeperperson and lifeexpectancy to create three new variables:IP ,LE ,urban, WEALTHstatus and HEALTHstatus.

CODE:

GapMinder Database Analysis

Hello,

here I submit the Assignment 2 of course  Data Management and Visualization by Wesleyan University.

here I take 20 observation to show an output of the code.also in this relationship frequency processes are not useful because every observation has unic value.

Review of references:-

here,

     each bubble represents a country,

     each color of  the bubble  represents world regions like,

                Red  represents Asia;

                yellow  represents Europe;

                green  represents  America;

                Skyblue represents  Africa;

     each bubble Size represents a population.

this graph shows that the country has an income less than $5000 they have the shortest life expectancy. the country has an income greater than $50000 they have the longest life expectancy.on the same income level there is a huge difference in lifespan. 

GapMinder Database Analysis

Hello,

here I submit the Assignment 1 of course  Data Management and Visualization by Wesleyan University. 

After going through the codebooks and datasets provided the Institute for statistical analysis I have selected the Gpminder codebooks and datasets for my Analysis.

What Gapminder does?

With the Ignorance Project Gapminder identify the specific global statistical trends that have not reached a broad public audience. they ask people questions about major trends and patterns. The questions cover major aspects of global development such as: Environment, Health, Energy, Gender, Economy, Demography and Governance.

My research question is: relation between “ HEALTH AND WEALTH IN THE WORLD”.

In GapMinder Data Analysis, I am particularly Interested in understanding the relationship between life expectancy and  income per person For understanding the same I have selected the following variables in my Code Books 

      1)  incomeperperson 

      2)  lifeexpectancy

      3)  urbanrate

The 2nd Topic I am Interested in understanding the relationship between life expectancy and co2 emissions   For understanding the same I have selected the following variables in my Code Books 

     1)  co2emissions

     2)  lifeexpectancy

     3)  urbanrate

literature review :-

I have reviewed paper regarding Gapminder

http://www.gapminder.org/GapminderMedia/GapPowerpoints/LifeExpectancy/LifeExpectancyBackground.pdf

http://www.gapminder.org

This Paper focuses on two of these characteristics:

1. Life expectancy is an average. Most people live either much longer or much shorter than what the life expectancy indicates.
2. When life expectancy is low, this is mostly due to a very high child mortality rate. Those that survive the dangers of childhood can expect to live to a relatively old age, even in countries with very low life expectancy.

there is three Question that related to my research question. it is helpful for my Gapminder analysis.

1) How many are rich and how many are poor?

2) How many are healthy and how many are sick?

3) How Does Income Relate to Life Expectancy?

According to Professor Hans Rosling he says that people live longer in countries with a high GDP per capita. No high-income countries have a short life expectancy, and no low-income countries have longer life expectancy. Still, there is a huge difference in life expectancy between countries on the same income level, depending on how the money is distributed and how it is used.