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Predicting Local Precipitation and Temperature from Oceanic Niño Index

It seems that this year is one of the colder and wetter years in my recent memory, at least in Colorado. Several ski areas have stayed open or have reopened every weekend past the original closing date because of additional snow fall. At least one ski area was still open this June, which is traditionally biking, hiking, and mountain climbing season. Trail Ridge Road in early June reportedly had 20 foot deep snowbanks in places, which is some of the highest I can remember. I recall back in the spring hearing about winter 2016 being one of the strongest El Niño years. So, I began to wonder, did we receive more precipitation this year because of the El Niño? Does Colorado normally receive more precipitation in El Niño years? And, because I love to see actual data and graphs “proving” the results, how can I visualize this?

I started by collecting precipitation data from NOAA for the entire state of Colorado. The data only went through the end of April, 2016 so I wasn’t able to evaluate the last 6 weeks of data. I compiled the data from 1950 to 2016, using only the data from January through the end of April. I then separated the data into El Niño years, La Niña years, and Normal years, based on the oceanic niño index information. I then created a bar chart in Grapher displaying the data. I was surprised to discover that it didn’t seem to affect precipitation over the entire state whether the ocean temperatures were cool (blue bars) or warm (red bars).

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Verifying Reported Statistics with Grapher

I recently read about the decreasing gender wage gap for newly graduated college students in an article on the Liberty Street Economics, the blog of the Federal Reserve Bank of New York. In the article, there is a statement that “among recent college graduates, men earn about 3 percent more than women overall” but that the gap “widens significantly as workers approach mid-career”. The article summarizes this mid-career group of individuals, where they find that “men earn about 15 percent more than women.” Being the inquisitive person that I am, I wondered how these statements could be made – what evidence was there supporting these statements? I could have taken the table on their blog and calculated a quick mathematical average using a spreadsheet program. But, the table only included a sub-section of the full data being used and the data was already in statistical summaries instead of original polling numbers. I decided to find some raw data and create some graphs that would allow me to perform some analysis myself.

Much data is available for download and analysis from the US Census Bureau. This data can be downloaded and edited. It can then be used for graphing or mapping in many Golden Software programs. The data can also be used for analysis, such as creating statistics, comparing values, or providing supporting evidence for other statistics. In this case, I thought some simple bar charts for various populations would help support or overthrow the claims of the decreasing gender wage gap in young workers.

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