BME100 f2014:Group18 L2

BME 100 Fall 2014	Home People Lab Write-Up 1 | Lab Write-Up 2 | Lab Write-Up 3 Lab Write-Up 4 | Lab Write-Up 5 | Lab Write-Up 6 Course Logistics For Instructors Photos Wiki Editing Help
OUR TEAM Name: Norah Alkhamis Name: Jesus Calderon Name: Kevin Couch Name: Jordan Kariniemi Name: Scott Slade Name: Rachel Tomlinson LAB 2 WRITE-UP Descriptive Statistics Experiment 1 shows the effects of the LPS dosage on the inflammotin levels of humans while experiment 2 shows the effects on rats. Experiment 1 Experiment 2 Results The results show bar graphs of the average inflammotin levels at certain LPS dosages, with the standard deviation values serving as the values for the error bars. Experiment 1 ‎Experiment 2 Analysis Our p-value for experiment 1 was less than .05, therefore LPS dosage levels had a significant effect on the inflammotin levels of humans. However, in Experiment 2, our p-value was much higher than .05, which means that our data was insignificant. LPS dosage levels had no effect on the inflammotin levels of rats. Experiment 1 Experiment 2 Summary/Discussion To begin our experiment, we performed an ANOVA on our human study. The data we were provided with described our inflammation levels in pg/ml due to varying LPS dosage levels in mg. First we found the average values of inflammotin levels for each dosage level (ie 0mg-15mg). We then found the standard deviation and standard error. We graphed our average inflammotin levels for each dosage vs the dosage levels, and added in our error bars. The values for our error bars were the standard deviation values calculated for each dosage. Next we ran the actual ANOVA test on the human study. Here, we obtained our P-value, which was less than the targeted 0.05. We then had to run a Bonferroni Correction, because the p-value provided in the ANOVA doesn't tell us what it is comparing. The Bonferonni Correction required us to perform a T-Test for each comparison (ie 0mg vs 5mg, 0mg vs 10mg ect). From here we had to create a corrected p-value. This was done by dividing our original p-value by how many new t-tests we performed. Once we determined that, our new p-value vs t-test comparison values,were less than our new p-value. This shows that our data is statistically significant. In other words, our data was good. This means that the different LPS dosage levels (mg) had significant impacts on inflammotin levels. Finally, we performed a T-test on the Rat Study. We did a t-test because we were only comparing 2 columns of data. This is unpaired because we were testing different subjects(rats in this case). First, we found the average inflammotin levels for 0mg pills and 10mg pill and our standard deviations for each as well. Essentially we had to find all the same values as in the ANOVA (ie avg, std dev, endpoint, std error). Once again we made a graph comparing the LPS Dosages vs our Inflammotin. From here we ran a t-test on our data and obtained a p-value which was compared to the alpha value of 0.05. Our p-value was larger than this, so we concluded that our data was NOT statistically significant. Again, another words our data was no good. The LPS dosage levels had no significant effect on the inflammotin levels of rats.