# Data description

This assignment involves working with the Gapminder World website:

http://www.gapminder.org/tools/ (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 you. For 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?
Can you conclude one variable in the graph is causing the change in the other variable? Explain

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. Child mortality (0-5 yr olds deaths every 1000 births) vs. Income per person (GDP/capita, PPP\$ inflation adjusted) for 1810-2015.

Note: to switch to the Child mortality 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. Life expectancy vs. CO2 emission for 1970-2014.

Note: you can switch to the CO2 emissions by clicking on the horizontal axis and going to Environment”, then Emissions”. Select the variable from the list. You can select the Life expectancy variable similarly from the vertical axis and going to health and selecting the variable from the list.

Graph 3. Exports (% of GDP) vs.Literacy rate, adult total (% people ages 15 and above) for 1982-2011 for China and India only.

Note: to switch to the Exports (% of GDP) variable click on the vertical axis and going to Economy”, then Debt & trade” then select from the list. Switch to the Literacy rate variable by clicking on the horizontal axis and going to Education”, then Literacy” then select from the list. Select China and India from the country list on the right. Select 1981 and hit the Play button. Notice that for China there is only data starting in 1989.

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