BME100 f2013:W900 Group17 L3: Difference between revisions

From OpenWetWare
Jump to navigationJump to search
Line 38: Line 38:
==Results==
==Results==


(Well-labeled graph with error bars and significance)
 
[[Image:G2Inside.jpg]]
[[Image:G2Inside.jpg]]
[[Image:G2Outside.jpg]]
[[Image:G2Outside.jpg]]

Revision as of 10:50, 25 September 2013

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: Jeremy Becker
Role(s)
Name: Luis Hernandez
Role(s)
Name: Alison Llave
Role(s)
Name: Naazaneen Maududi
Role(s)

LAB 3A WRITE-UP

Descriptive Statistics

In order to describe this data set, statistical values were taken from both the data set of values collected inside and the data set collected outside as well as the entire data set of the product test. In total, temperature readings from the inside and outside were taken by 15 groups for a total of 15 trials. For the subset of temperature readings taken with the thermometer within the inside data set the average reading was 97.57533333 degrees accross with a standard deviation of 0.778049244 and a standard error of 0.063527455. Then for the subset of temperature readings taken with the RAIIN sensor within the inside data set the average reading was 94.48466667 degrees with a standard deviation of 2.064618419 and a standard error of 0.168575388. Taking a T test comparing the thermometer readings set of results with the RAIIN sensor set of results the result was 1.22112E-39 which signifies that there is a high level of confidence in a difference existing within the two data sets. Furthermore, upon taking the Pearson's r correlation test it was found that there is only a positive correlation level of 0.165252944 between the temperature readings data set and the RAIIN sensor data set within the context of the larger inside data set. This is not a high correlation and so it is safe to conclude that the two data sets are significantly different.


Now the subset of temperature readings taken with the thermometer within the outside data set the average reading was 97.49666667 degrees with a standard deviation of 0.897227262 and a standard error of 0.066875372. Then for the subset of temperature readings taken with the RAIIN sensor within the outside data set the average reading was 95.57944444 degrees with a standard deviation of 1.479494751 and a standard error of 0.110275028. Then taking a T test comparing the thermometer readings set of results with the RAIIN sensor set of results the result was 4.04259E-38 which signifies that there is a high level of confidence in a difference existing within the two data sets. Also, upon taking the Pearson's r correlation test it was found that there is only a positive correlation level of 0.225273129 between the temperature readings data set and the RAIIN sensor data set within the context of the larger inside data set. This is not a high correlation and so it is safe to conclude that the two data sets are significantly different.

Now taking the outside data set and inside data set togetherer with the new set separation being solely the set of readings taken with the thermometer and the set of readings taken with the RAIIN sensor, a t-test result was found, 4.18844E-72, which again indicates that there is a significant difference between the readings taken with the thermometer and those taken with the RAIIN sensor. And with a correlation of only 0.167395055 found between these two sets, it is safe to further strengthen the conclusion that the readings taken with the two devices are not similar.




Results



Analysis

(Perform inferential statistics described in assignment.)





Summary/Discussion

Errors: May lose signal; connects to other devices on bluetooth; indecipherable, ambiguous instructions; different name for bluetooth
(Please discuss the results and statistical analysis. State your conclusion as well as design flaws and recommendations.)






LAB 3B WRITE-UP

Target Population and Need

Athletes Parents with young children so that they could monitor the body temperature of their kids




Device Design

Smaller, variety of colors, one color




Inferential Statistics



Graph