Creating Graphs for your data-Module4
In this module I am doing a visual representation of Gapminder data for the variables I have chosen.
Here Univariate analysis has been done for each of the selected variables ,Incomeperperson, urbanrate, alcconsumption and life expectancy.
Also done Bivariate analysis for the variables. Summary also been provided for better understanding.
In Univariate analysis, income is right skewed, means having lower income levels with few having higher income.
Here in this Univariate analysis of Alcohol consumption most countries have low to moderate alcohol consumption with few showing higher levels and have several outliers, it is right skewed showing high consumption is less common.
In this Univariate analysis, most countries have life expectancy between 60 and 80 years, with a peak around 70–75 years. The median life expectancy is high, but there are several countries with significantly lower values (outliers).
The urban rate distribution is bimodal, with peaks around 40% and 70–80%, suggesting two clusters of countries—those with lower and higher urbanization. It Shows a wide spread with some outliers, indicating variability in urbanization levels across countries.
*Scatter plot shows a positive trend as countries with higher urbanization tend to have higher life expectancy. *Regression line reinforce this relationship,though the spread of datapoints suggests other influences. *This indicates better access to health care ,infrastructure and services in urbanized regions leading to higher life expectancy.
*Scatter plot shows a clear positive correlation: countries with higher income per person tend to have higher life expectancy. *The regression line confirms this trend, though there is some spread in the data indicating that income is a good predictor of life expectancy. *This reflects that economic prosperity enables better healthcare, nutrition and living conditions.