Fall Benchmark 2010
Abstract
Our economic variable in this lab is Marine Fish Catch (in tons); our ecological variable is Freshwater Pollution (in tons/cubic km); and our demographic variable is Urban Population Growth (Annual %). We studied these variables because we thought that Marine Fish Catch would affect Freshwater Pollution, also we believed Urban Population Growth would support these two variables. Our hypothesis is if there is an increase in marine fish catch it would increase the amount of freshwater pollution. We then hypothesized that urban population growth will affect both marine fish catch and freshwater pollution by needing more fish for the population, which in turn would increase the amount of pollution emitted. We used Windows Word and Excel to record our data and we used Nation Master for our research. Our research did not support our hypothesis as we started to see the statistics did not correlate.
Hypothesis
We predicted that marine fish catch would influence freshwater pollution because the more fish caught would affect the water by the boats introducing pollutants. We then predicted that urban population growth would influence both, marine fish catch and freshwater pollution, by increasing the amount of need of fish catch which in turn increase the amount of freshwater pollution.
Procedure
A technological tool that I used for this lab is Microsoft Word, Microsoft Excel, and Nation Master. Some mathematical tools that were used were for finding R-Squared, which is a statistical measure of how well a regression line fits into the real data. We searched for our demographic and ecological variables before we developed our hypothesis because we tried to find a topic to revolve our lab around. We found our demographic and ecological variables by viewing facts and made predicted correlation between the facts, whichever one seemed with the most relations were picked. We found our intermediate variable by predicting what variable would correlate well with the two main points we firstly established.
Background
The first variable used in this experiment was the marine fish catch in tons per country. This variable measures the amount of fish caught in each country examined, thus measures the amount of fishing being done in an area. In other people's research this variable is often times used to measure the amount fishing being done in areas to determine consumption rates. In this experiment this variable was chosen because it could serve as a good economic variable because it shows how much exporting is being done by fisheries in the areas being studied.
[citation sources for other people's use of statistic; http://www.theglobaleducationproject.org/earth/fisheries-and-aquaculture.php , http://ceo.ucsd.edu/publications/fish_statistics.html ]

The second variable used in this experiment was freshwater pollution in tons/cubic km. This variable measures the amount of pollution in freshwater areas in each country with recorded data, simply put it measures the amount of pollution in a countries freshwater locations. In other people's research this variable is often times used to measure the health of aquatic biomes. This variable was used in this experiment as a environmental variable due to its direct impact on the environment and the fact that pollution is often caused by human actions.
[No citation needed]

The final variable used in this experiment was urban population growth in terms of annual percentages. This variable was used to measure the growth rate of urban populations, by measuring urban growth rate it was thought that economic and environmental variables would both be affected. An example of how this variable is used in other researchers work would the US census which seeks to gather various socio-economic data. This variable was used in this experiment as an intermediate variable due to its impact on the environment and on the economy of each country.
[Citation source: http://www.census.gov/population/www/documentation/twps0027/twps0027.html]
Analysis
1. I see no clear relationship between my ecological and demographic variable, in fact the r squared function for these two variables came out extremely weak.
2. I see no clear relationship between my demographic and intermediate variable, the r squared function was also very weak for these two variables.
3. I see no clear relationship between my ecological and intermediate variable, and yet again then r squared function for these two variables was very weak.
4. Given the relationships between my ecological and intermediate variables and my demographic and intermediate variables it would seem safe to say that the ecological and demographic variables have very little if any connection to each other.
5. The relationships i observed do not support my original hypothesis that the variables were connected.
Conclusion
1. Our research for the lab did not support our hypothesis that the variables were connected.
2. Three limitations on our research included the amounts of country with available and accurate data for the variables, unavailability to the most recent data, and lack of availability to data that we wanted to use for reasearch.
3. A new variable that could be used to support our hypothesis would be over all aquatic pollution per country.
Works Cited
"Marine Fish Catch Statistics - Countries Compared - NationMaster." NationMaster - World Statistics, Country Comparisons. Web. 22 Nov. 2010. <http://www.nationmaster.com/graph/env_mar_fis_cat-environment-marine-fish-catch>.
"Water Freshwater Pollution (most Recent) by Country." NationMaster - World Statistics, Country Comparisons. Web. 22 Nov. 2010. <http://www.nationmaster.com/graph/env_wat_fre_pol-environment-water-freshwater-pollution>.
"Annual % Urban Population Growth Statistics - Countries Compared - NationMaster." NationMaster - World Statistics, Country Comparisons. Web. 22 Nov. 2010. <http://www.nationmaster.com/graph/peo_urb_pop_gro_ann-people-urban-population-growth-annual>.