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Python and Web Development Classes for Beginners at an affordable fee of 700-1500 INR. Classes will be 3 times a week with a doubt session every alternate week.

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Decimal in Python The default number system in python is the decimal. To understand the need for decimal over floating-point values, let...

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Python Basics Are you new to programming? Interested in learning python from the basics? then this post is for you. Here we are going t...

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Python for Beginners - Learn Python Now Without IDE - 2

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[toeney]

Relationship between Internet User Rate, Electrical Energy Consumption and Suicide Rate

Description of my Output

I generated a sub data set that included nations with suicide rates between 2 and 20 individuals per 100,000, because these suicide rates were more common than higher rates, and then I calculated the frequency of the variables, which included the counts and percentages of each of the variables' constituents.

In addition, I built the code to explain each of the variables, including their mean values, standard deviations, minimum and maximum values, and percentile distributions for the 25th, 50th, and 75th percentiles. These descriptions provided me with an overview of the data distributions. To illustrate additional correlation between the variables, I established groups of 10 for the Internet User Rate (internetuserate) and Suicide Rate per 100,000 People (suicideper100th) variables, as well as a group of 8 for the Residential Electricity Consumption Rate per person (relectricperperson). Following that, I created the code to display a univariate graphical representation of each variable. This revealed the following:

1. Although Internetuserate has a modal class of 10, the data distribution in the variable is uniform on average.

2. Suicideper100th contains two modal classes, each with a count of 16 persons: 20 and 60 in every 100 000 people. The chart's spread was also devoid of any discernible pattern.

3. Relectricperperson revealed a nearly uniform distribution, but three major classes: 500kWh, 2000 kWh, and 4000 kWh per person per year.

I did a bivariate analysis between my variables of choice after the univariate data visualization to highlight the relationship between them. The sparsely scattered points on the graph, as well as the line of a very minor and practically imperceptible slope that is almost straight, indicated that, while the internetuserate may be changing, it had no major effect on the suicide rate. On the other hand, the densely packed points and the positively sloped line that showed the relationship between the electric consumption per person and the internet user rate, on the other hand, revealed a very close relationship in the bivariate graph between the electric consumption per person and the internet user rate. This meant that as energy consumption per person increased, so did the percentage of residents in the study countries who used the internet.

Conclusion

Based on the preceding facts, it can be concluded that there is no correlation between the number of citizens who use the internet and a country's suicide rate. This could also imply that there are a variety of other factors at play when it comes to suicide rates in different countries. Also extracted from the data is the conclusion that, while an individual's electricity consumption rate impacts their internet usage rate, it has no effect on their suicide rate.

Recommendation

I urge that other potential reasons of suicide, such as income per person (Incomeperperson), be explored to determine if there are any strategies to lower suicide rates based on the observations and conclusions taken from the data.

Please, find attached, the pictures that show the codes, outputs and the graphs generated from my analysis

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