BME100 f2014:Group33 L2

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Lab Write-Up 1 | Lab Write-Up 2 | Lab Write-Up 3
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Contents

OUR TEAM

Name: Emigdio Ruiz Esquivel
Name: Emigdio Ruiz Esquivel
Name: John Cunningham
Name: John Cunningham
Name: Wandasun Sihenath
Name: Wandasun Sihenath
Name: Chris Adams
Name: Chris Adams
Name: Tim Snelling
Name: Tim Snelling
Name: Breanna Falcon
Name: Breanna Falcon

LAB 2 WRITE-UP

Descriptive Statistics

Experiment 1 (Rats)
Chart containing the Rat Descriptive Data


Experiment 2 (Humans)
Chart containing the Human Descriptive Data




Results

Experiment 1 (Rats)
Graph containing Rats Results


Experiment 2 (Humans)
Graph containing Human Results




Analysis

Experiment 1 Rats
Description of image


Experiment 2 Humans
Description of image




We used a t-test for the rats because they can be monitored when given the dosage and also because we are estimating the standard deviation based off of the sample data and so it accounts for the extra variability, since the data points might differ from each other. The Anova test was used on the humans in order to see what the differences in the variables were in the numerical data which would be able to show if the increase of inflammotin protein was attributed to the dosage that was given to both the humans and rats.

Summary/Discussion

The results indicated that the inflammotin protein increased in both rats and humans as the dosage increased. However, the extent to which the dosage affected each test group varied. In the rats, the inflammotin levels increased from just above 10 pg/ml to approximately 11 pg/ml after changing the dosage from 5mg to 10mg. The effect of LPS on Humans was higher when compared to the rat scale since rats and humans have different body structures and do not function in the same way. There was a significant jump in the human body just by changing the dosage from 10mg to 15mg. The standard error is also higher in humans than rats because the rats can be contained, meaning they can be properly monitored. Whereas in humans, the standard error is greater because they could be doing other things such as: taking medication, vitamins or eating certain foods, introducing a far greater number of variables that could affect the experiment and skew the data.

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