BME100 f2013:W900 Group15 L3

BME 100 Fall 2013	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: Gage Bebak Role(s) Name: Justin Dombrowski Role(s) Name: Saiswathi Javangula Role(s) Name: abdulrahman alruwaythi Role(s) Name: Ryan Fisher Role(s) Name: student Role(s) LAB 3A WRITE-UP Descriptive Statistics In order to determine accurate values for the data set, statistical values were taken from the set of values collected from the inside data set as well as outside. A total of 330 different trials were engaged in this data, allowing for 330 sets of data. For the temperature with the thermometer taken within the the inside data set, the average reading was 97.575 degrees For the data set taken with the sensor inside, the average reading was 94.485 degrees. This determines that there is definitely a difference within the data sets. Regarding the temperatures taken within the outside data set, the average reading with the temperature was 97.496 degrees. The temperature readings taken with sensor with the outside data, the average reading was 95.579 degrees This also signifies a large difference between both data sets. Taking the outside and inside data set into account, a t-test was done to show a value of 2.09E-72, which signifies a difference between the sensor and the thermometer. Taking Pearson's r correlation test into account, the value ended up with 0.167395055 proving that there is a significant difference between both the sensor and the thermometer. Results Average temperatures of the Oral Thermometer and Sensor for the outside trial. Average temperatures of the Oral Thermometer and Sensor for the inside trial before the outside trial. Average temperatures of the Oral Thermometer and Sensor for the inside trial after the outside trial. Analysis Personal Group Data: Because a p-value of 2.09E-72 was obtained and it is less than the given 0.05, it can be inferred that there is a statistical difference between both groups only when all the data is averaged together. When separated into three trials(inside pre-out, out, and inside post-out) It is found that there is no statistical difference between the devices, meaning they act very similarly. When a Pearson's r Test was conducted to calculate the correlation coefficient, It was concluded that the r value given is 0.167395055, which ultimately shows a weak, positive relationship between the values, verifying the results from the separated trials. Based upon the data received from the lab, it can be inferred that there is quite a difference between the readings of the thermometer and the sensor. The readings from the sensor were always lower than the ones received from the oral thermometer. While the oral temperatures given proved that the tester was healthy, the ones given by the oral readings showed a normally considered minimal temperature. For example, the initial reading that the tester's temperature was 93.7 is not possible. Summary/Discussion Discussion: what we found is that the device not accurate and reading temperature was changing with surrounding temperature Given the inferential statistics, it can be concluded that there is not a significant difference in the data according to the tests. The difference in the readings showed that the sensor acted like the tested and proven thermometer, but after the Pearson's Test it was shown that the device was not nearly as accurate or reliable as the proven thermometer. This is due do the variability of the sensor to correctly read the body temperature. Conclusion: We have concluded that the device we tested today is not not accurate, reliable, and the variability is high. The data has shown no correlation to that of the oral thermometer making it unusable and unreliable. Some flaws that we found while using the device were that it was way below the actual body temperature. The outside temperature and the temperature of the surroundings had a direct affect on the device's calculations. It also needed to be placed in an uncomfortable spot and when it was removed it would cause a bit of pain. The reading on the device itself vs the reading that was shown through the app were two completely different readings causing confusion as to which of the readings was correct. Honestly, the idea of a thermometer ran by an app is nice, however the practicality of it is not very high. The device needs to be remodeled and taken back to the drawing board, it has too many flaws to make it a viable product, even with some tweaking. LAB 3B WRITE-UP Target Population and Need Device Design Inferential Statistics Graph