BME100 f2015:Group2 1030amL2

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Owwnotebook icon.png BME 100 Fall 2015 Home
Lab Write-Up 1 | Lab Write-Up 2 | Lab Write-Up 3
Lab Write-Up 4 | Lab Write-Up 5 | Lab Write-Up 6
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Name: Cynthia Crockett
Name: Amar Joshi
Name: Gage Schrantz
Name: Xiaoyu Chen
Name: Colleen Rice
Name: Anton Voronov


Descriptive Statistics

Human Study

0 mg 5 mg 10 mg 15 mg
Average (mg) 3.834 8.932 61.622 657.941
Standard Deviation (mg) 1.523 1.594 30.111 212.943
Standard Error (mg) .482 .504 9.522 67.338

Rat Study

0 mg 10 mg
Average 10.814 11.112
Standard Deviation 2.226 7.403
Standard Error .9953 3.311


Human Experiment

Rat Experiment

Experiment 2 BME 100 Lab 1 ASV.png


Human Experiment

• Using the ANOVA Test we determined the results were significant

• Using the post-hoc test we determined that there was a significance between each of the groups, as they relate to each other

Rat Experiment
• Used the T-test instead of the ANOVA, as we only had two variables to compare.

• Since the significant value we got from the T-test was greater than 0.05, it was not significant.

• The P-test was relatively close to -1, of which indicated that there was a negative correlation with the data.


As the dosage increased by 5mg the inflammation protein levels also increased exponentially in humans. We decided to choose the ANOVA test for this this experiment because we had more than two variables. When we did the post-hoc to see if the results were significant, we found out that they were significant between the groups with greater than 95% confidence. The medicine does significantly increases inflammation protein levels in humans. In contrast, in the lab rat experiment, we used the T-test as there were only two variables. We then calculated that the results were not significant as it had less than 95% confidence. Additionally, the P-Value indicated that there was a general negative correlation between the two variables. In other words, the medicine does not significantly increase the inflammation protein levels in rats.