The figure below shows how stonefly pushups increase at low oxygen stress and then decrease at the high oxygen stress to where they are similar to the control. The difference between the middle bar and the first bar is significant, as the error bars would not overlap. The pattern makes sense because as the organisms were stressed, they did more pushups, but at extreme oxygen deprivation, they are unable to do as many pushups. Figure 1. Differences in average number of pushups (n=43) for small individuals of the Common Stonefly (order) found in a mixed hardwood forest in Pennsylvania, that were exposed to three different oxygen levels. Treatment 1 (a) was the highest oxygen treatment; treatment 2 (b) was the median oxygen treatment; and treatment
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Correlation between Oxygen Level and Number of Pushups performed by small individuals of the common stonefly (order) (n=129) with corresponding regression data in a scatter plot format. The regression line equation is y=0.4073x+38.847, and the r2 value is 0.0011, which signifies that the relationship between the oxygen level and the number of pushups performed by the common stonefly is not significant.
Although, we originally expected the relationship between soil and air temperature to be one of the strongest, we later discovered that this thinking was because of an error. Both farenheit and celsius temperatures were included in the original data. When this error is corrected, there is no statistical relationship between soil temperature and air temperature. Figure 4. Correlation between Air Temperature and Soil Temperature (both in degrees Celsius) from 15 samples at 4 locations (Green Lee, Croyle Run, Roaring Run, and Standing Stone) with corresponding regression data in a scatter plot format. The regression line equation is y=0.244x+11.189, and the r2 value is 0.0597, which signifies, despite prior thought, that the relationship between air temperature and soil temperature is not actually …show more content…
Additionally, it is notable that 1 group of data, a set of 4 points, all have a higher salinity than the other data points. They also have (aside from one point) the highest pH. Figure 5. Correlation between pH and Salinity (degrees Celsius) from 15 samples at 4 locations (Green Lee, Croyle Run, Roaring Run, and Standing Stone) with corresponding regression data in a scatter plot format. The regression line equation is y=17.811x-76.25, and the r2 value is 0.5053, which signifies that the relationship between pH and salinity is significant.
Below it is obvious that there is a positive correlation as well between pH and diversity index. Diversity Index is highest where the pH is neutral or slightly basic and lowest where pH is slightly acidic. Similar to how before there was one region with an evidently higher salinity, here it is evident that there is 1 group of data (5 points) with an evidently lower pH. Figure 6. Correlation between pH and Diversity Index from 15 samples at 4 locations (Green Lee, Croyle Run, Roaring Run, and Standing Stone) with corresponding regression data in a scatter plot format. The regression line equation is y=.1377x-.2616, and the r2 value is 0.6609, which signifies that the relationship between pH and diversity index is
Correlation between Oxygen Level and Number of Pushups performed by small individuals of the common stonefly (order) (n=129) with corresponding regression data in a scatter plot format. The regression line equation is y=0.4073x+38.847, and the r2 value is 0.0011, which signifies that the relationship between the oxygen level and the number of pushups performed by the common stonefly is not significant.
Although, we originally expected the relationship between soil and air temperature to be one of the strongest, we later discovered that this thinking was because of an error. Both farenheit and celsius temperatures were included in the original data. When this error is corrected, there is no statistical relationship between soil temperature and air temperature. Figure 4. Correlation between Air Temperature and Soil Temperature (both in degrees Celsius) from 15 samples at 4 locations (Green Lee, Croyle Run, Roaring Run, and Standing Stone) with corresponding regression data in a scatter plot format. The regression line equation is y=0.244x+11.189, and the r2 value is 0.0597, which signifies, despite prior thought, that the relationship between air temperature and soil temperature is not actually …show more content…
Additionally, it is notable that 1 group of data, a set of 4 points, all have a higher salinity than the other data points. They also have (aside from one point) the highest pH. Figure 5. Correlation between pH and Salinity (degrees Celsius) from 15 samples at 4 locations (Green Lee, Croyle Run, Roaring Run, and Standing Stone) with corresponding regression data in a scatter plot format. The regression line equation is y=17.811x-76.25, and the r2 value is 0.5053, which signifies that the relationship between pH and salinity is significant.
Below it is obvious that there is a positive correlation as well between pH and diversity index. Diversity Index is highest where the pH is neutral or slightly basic and lowest where pH is slightly acidic. Similar to how before there was one region with an evidently higher salinity, here it is evident that there is 1 group of data (5 points) with an evidently lower pH. Figure 6. Correlation between pH and Diversity Index from 15 samples at 4 locations (Green Lee, Croyle Run, Roaring Run, and Standing Stone) with corresponding regression data in a scatter plot format. The regression line equation is y=.1377x-.2616, and the r2 value is 0.6609, which signifies that the relationship between pH and diversity index is