- #1
Saladsamurai
- 3,020
- 7
Hi folks
I have an experiment in which I take an image of a flame. I then run a software routine that tells me what the concentrations of OH (hydroxyl) is at different heights above the flame. I first have to give it a calibrated image of a flame with known data and it then is able to give me data about an unknown flame as stated above. Here is what I have done:
1. Give calibrated the software with a known flame image with known data
2. Imaged 2 different flames that are unknown. the software then returns the OH concentrations of these tow flames.
I am curious to know what statistical measures are important? I suppose I should compare the 2 data sets from the unknown flames so I can show that the data sets are statistically similar and not outside of the bounds of experimental error.
Any thoughts? Please let me know if you need more information.
I have an experiment in which I take an image of a flame. I then run a software routine that tells me what the concentrations of OH (hydroxyl) is at different heights above the flame. I first have to give it a calibrated image of a flame with known data and it then is able to give me data about an unknown flame as stated above. Here is what I have done:
1. Give calibrated the software with a known flame image with known data
2. Imaged 2 different flames that are unknown. the software then returns the OH concentrations of these tow flames.
I am curious to know what statistical measures are important? I suppose I should compare the 2 data sets from the unknown flames so I can show that the data sets are statistically similar and not outside of the bounds of experimental error.
Any thoughts? Please let me know if you need more information.