This assignment involves working with the Gapminder World website:
(Links to an external site.)
You must use the Gapminder World graphical interface to plot three different graphs of development data, analyze them and provide comments within 200 words per graph. During the tutorials the TA’s will help youFor each graph:
make sure that you know the precise meaning of the plotted variables (no need to write it down)
outline the major pattern (e.g., positive, negative, or no association between the plotted variables, etc.) you see:
in the scatterplot across countries
in the variables’ change over time (if applicable)
you may also choose to comment on a particular country or group of countries which you believe deserve special attention or something else on the plot that you find striking or interesting
state briefly what you think is the likely reason(s) for the observed pattern
could you offer a forecast (with brief argumentation) of how you would expect the graph to look 20 years from now?
Instructions: The exact variable names from the Gapminder website are given below. Put the first stated variable on the vertical axis and the second variable (after the vs.”) on the horizontal axis. To see the joint change over time in the plotted variables over time use the slider below the graph (or, the Play” button) as shown in tutorials.
Graph 1. Babies per woman (total fertility) vs. Income per person (GDP/capita, PPP$ inflation adjusted) for 1810-2015.
Note: to switch to the Children per woman variable click on the vertical axis and select the variable from the very top of the list. Use the movie” slider below the plot to see how the pattern evolves over the years 1960 to 2015.
Graph 2. Income vs. CO2 emission for 2014.
Note: you can switch to the CO2 emissions variable by clicking on the vertical axis and going to Environment”, then Emissions”. Select the variable from the list.
Graph 3. Life Expectancy vs. Income per person (GDP/capita, PPP$ inflation adjusted).
To answer with economic knowledge as much as possible
Writing Ideas:
per woman (total fertility) vs. Income per person (GDP/capita, PPP$ inflation
for 1810-2015.$model$markers$bubble$encoding$y$data$concept=children_per_woman_total_fertility&space@=country&=time;;&scale$domain:null&zoomed:null&type:null;;&frame$value=2014;;;;;&chart-type=bubbles&url=v1
first graph can be explained by the data seen from 1810 to 2015, which shows an
overall increase in income and a decrease in the birth rate of children. And
you can see that in the past two hundred years, the yellow European region
first occurred this change, followed by the green American region, followed by
the red, the Asian region, the reasons behind the change can be that with the
increase in income, employment opportunities, health care, social and cultural,
more equality between men and women, resulting in a significant reduction in the
birth rate of children, for example, the health of the mother will have a great
impact on the survival of the child that the more modern For example, the more
modern the mother’s health will affect the survival of the child, the more
modern the mother’s body nutrition and health will actually be improved. The
more modern the mother’s health, the better the nutrition and health of the
mother will be, and the health of the child will be improved. Another example
is the increase in employment opportunities for women, because of social
progress and improved education, the opportunity cost of having children
becomes larger, so more women are willing to pursue work and have fewer
children. Women are willing to pursue work and have fewer children. You don’t have
to talk about the overall projection, you can talk about certain regions,
certain countries, and then you can talk about the actual situation.
EMISSION FOR 2014 VS INCOME PER PERSON$model$markers$bubble$encoding$y$data$concept=co2_emissions_tonnes_per_person&space@=country&=time;;&scale$domain:null&zoomed:null&type:null;;&frame$value=2014;;;;;&chart-type=bubbles&url=v1
rightward trend is even more pronounced, as all countries are basically in this
straight line up to the right. As a country’s per capita income rises, the
amount of pollution increases. This is the response. With the economic
development of human beings, more and more industrial production, energy use,
will lead to an increase in the amount of CO2 pollution. As income rises, buy a
car. The more obvious is the blue, Africa region in general, the amount of
pollution per capita is not as high as in Asia. Asia is not as high as the
Americas. The Americas are not as high as Europe, the graph, China’s per capita
pollution is not as high as the United States, the United States is considered
one of the highest per capita pollution of several countries.
Expectancy vs. Income per person (GDP/capita, PPP$ inflation adjusted).$model$markers$bubble$encoding$y$data$concept=life_expectancy_years&space@=country&=time;;&scale$domain:null&zoomed:null&type:null;;&frame$value=2016;;;;;&chart-type=bubbles&url=v1
third graph tells a similar story to the first one. First you can talk about
the big picture, the whole world from the past to the present, is that as
income increases, life expectancy also increases, so the overall is a UPWARD
TREND can also be specific to the changes in each region, Europe changes,
America then, Asia then, and finally Africa so you can reflect a very obvious
positive link, the higher the income, the higher the life expectancy. There are
many right reasons behind this: for example, as the country’s technology
improves, the wealth grows, the people’s standard of living improves, they have
more wealth, they live better, then their health and nutrition will be higher,
and their life expectancy will naturally increase. Improved technology can lead
to more medical advances, and with the development of the country, the
government will also provide a better pension system, the length of life

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