Microtheme Based on EIA Dataset
I need 2 different essays on the same topic listed below. So, in the end, you will be preparing 2 documents with 2 different writing styles. These are the same assignment for me and my friend.
PLEASE FOLLOW THE INSTRUCTIONS. THE DATA AND GRAPH CONTAINED IN THE BELOW WEBSITES CAN BE FOUND IN THE LOWER PART OF THE ATTACHMENT
Write a microtheme (a 2-page or 600-word essay) in MLA style referencing one of the following datasets from the Energy Information Administration website:
1) U.S. Electricity Profiles: http://www.eia.gov/electricity/state/
2) Petroleum Imports to U.S. by Country: http://www.eia.gov/petroleum/imports/companylevel/
3) U.S. State Energy Profiles: gov/state/index.cfm?ref=bookshelf”>http://www.eia.gov/state/index.cfm?ref=bookshelf
(look at raw data under map)
4) State-Level Energy-Related CO2 Emissions: http://www.eia.gov/environment/emissions/state/analysis/
(if you select #4, select only one of the available datasets on the right of the page).
How to approach this assignment:
a) Study the table you select and identify, discuss, and reference the patterns or “stories” in the dataset source. See the section in Chapter 9 called “How Tables Contain a Variety of Stories” (202-204).
b) Provide a brief summary of the data, including the main observations and conclusions represented in the dataset.
c) Based on the patterns or “stories” you have identified from the dataset, develop a supportable claim.
d) To support your claim, create and embed a graph into your argument by making inferences from your dataset, doing calculations, and converting the tabular data (dataset) into a bar or line graph. Be sure to label and title your graph (see “Special Strategies for Framing Statistical Evidence” on pp. 100-101). Follow the rule of independent redundancy: “the graphic should be understandable without the text; the text should be understandable without the graphic; the text should repeat the most important information in the graphic” (207).
e) Argue/defend the overall conclusions you have interpreted from the dataset.
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