# BME100 f2014:Group6 L2

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# OUR TEAM

 Name: Laura Stokes Name: Gavin White Name: Paul Chua Name: Galt Goettl Name: Yamilex Bustamante Name: Prakriti Shukla

# LAB 2 WRITE-UP

## Descriptive Statistics

Experiment 1

The data table above shows the average of inflammation for each dosage, the standard deviation which is the distance from each data point from the mean, the endpoint number and the measurement of accuracy called the standard error (SD Error). This data only represents human experimentation.

Experiment 2

These two data sets show the Mean (average), Standard Deviation (SD), and Standard Error (SE). These data sets are shown by the amount of protein dosed. For Humans:0mg, 5mg,10mg,and 15mg. For Rats: 0mg and 10mg.

## Results

Experiment 1

The graph presents the average inflammatory protein amount per dosage. The standard deviation shows the average distance from each data point from the average output of inflammatory protein.

Experiment 2

These are the graphs for the human study and rat study. These graphs show the the average Inflammatory protein amount per dosage in mg. The error bar is one standard deviation above and below the average of both data sets.

## Analysis

Experiment 1

The ANOVA for the Human study for experiment 1 showed that the p-value was less than 0.05 thus not statistically relevant. This correlates to the large jump between the 10mg dosage and the 15mg dosage of lipopolysaccharide.

Experiment 2

The ANOVA for the Human Study for Experiment 2 showed a p-value of 1.4008E-16 which is significantly less than the desired p-value of 0.05; this means that between some group in the human data set there is a significant difference between 2 or more doses (0mg, 5mg, 10mg, and 15mg) of lipopolysaccharide- it is unknown between exactly which two doses because individual t-tests were not conducted. However, in the rat study the t-test p-value turned out to be 0.867405; this is significantly higher than the desired 0.05 p-value; meaning there is no statistical difference between 0mg and 10mg in rats. Overall, the human experiment proved to have a statistically significant difference between dosages.

After the Post-Test for ANOVA was completed, it is determined that all categories (0mg-5mg,0mg-10mg,0mg-15mg,5mg-10mg,5mg-15mg, and 10mg-15mg) are all statistically significant due to the p-value being below the set 0.05 value; the p-value for these numbers was 0.00833.

Therefore, the rat experiment proved to have no statistically significant differences between doses while the human experiment was completely statistically significant.

## Summary/Discussion

In both experiments with the rats and the humans, it can be seen that the 10mg dose of LPS is the most effective drug rather than the 0mg, 5mg or 15mg. The 0mg and 5mg do not have much of an effect on the humans and contrastingly the 15mg had too much of an effect. The ANOVA test was done on the humans due to the fact that the humans had more than two groups; subsequently, individual t-tests were conducted to find the precise groups that had a statistical difference. The t-test was done on the rats due the fact that the group had two populations. Pearson's R Correlation value or the p value showed that the tests performed on Humans revealed all dosage comparisons (as previously stated in the analysis) showed significant differences proven by ANOVA and T-Tests, while the Rat Study was not significantly different because the p-value was significantly larger than 0.05. As seen in the previous experiment, the 10mg dosage had proved to have some effect on the elderly population as proved by the graph, however, the 15mg dosage provided for too much of the protein in the body. Furthermore, due to monetary restrictions as set forth in previous information, 10mg would be the least amount of Lipopolysaccharide that had some relevant affect on the body. In both the human and rat study, inflammation protein levels did increase as dosages increase, and a spike was seen after 10mg of the protein. However, in both the data sets there were outliers that did not correlate with the rest of the data.