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 15 groups were engaged in the data set, allowing for 15 different trials. For the temperature with the thermometer taken within the the inside data set, the average reading was 97.575333 degrees with a standard deviation of 0.77804924 and a standard error of 0.06352745. For the data set taken with the sensor inside, the average reading was 94.484667 degrees with a standard deviation of 2.064618419 and a standard error of 0.168575388. While taking a t-test to determine the results, 1.22112E-39, which determines that there is definitely a difference within the data sets. Upon taking Pearson's r correlation, a positive correlation of 0.165252944 between the temperatures and the sensor readings. Because the correlation is not significantly high, a conclusion can be drawn that the two data sets are different. Regarding the temperatures taken within the outside data set, the average reading with the temperature was 97.49666667 degrees and the standard deviation of 0.897227262 and a standard error of 0.066875372. The temperature readings taken with sensor with the outside data, the average reading was 95.579444 degrees with a standard deviation of 1.4794948 and a standard error of 0.1102750. When taking the T-test to compare values between the thermometer and the sensor, the value came out to 4.04259E-38, which signifies a large difference between both data sets. Taking into account Pearson's r correlation test, the value came out to 0.225273129, also proving that there is definitely a difference between both data sets. Taking the outside and inside data set into account, a t-test was done to show a value of 4.18844E-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. (Descriptive statistics for the data set) Results (I got this, just at work. -Ryan) Analysis Personal Group Data: File:3jehrkj4wrb 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. (Perform inferential statistics described in assignment.) Summary/Discussion (Please discuss the results and statistical analysis. State your conclusion as well as design flaws and recommendations.) Discussion: what we found is that the device not accurate and reading temperature was changing with surrounding 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