(My research is about development of FR intumescent coating by addition of additives, my goal is to have a more thermal resistive sample than the controlled sample without neglecting the time(aim: lesser average temp or better "temperature vs time" relationship( negatively related)). I performed a horizontal burner fire test for 8 different samples, recording the temperatures in 1 hour per minute from each samples.What statistical tool/method do I use the data gathered( time vs temp) to conculde that sample a is better than the controlled sample? I am not quite sure to use correlation and not knowledgeable enough to other tests thank you.
Firstly you will need a graphical representation of each time taken to help you have clearer view of the whole situation after which you will apply a time series analysis or regression analysis.
Úse a 2 sample t test with the mean of test minus mean of control. See any standard statistics book for details. If you need one see Rosner Fundamentals of Biostatistics in the z-library. google instructions for the z-library. Best wishes, David Booth
Dou you have several readings (Y) per sample over time (T), like you put a sample in fire and measure every minute over one hour, so you have about 60 readings per sample?
If so:
Is there some theory specifying the functional relationship between Y and T (e.g. should that be an exponential or linear relationship)?
Plot Y against T for each sample and see what the relationship in your sample data might be, or if it is ain accordance with theory.
Fit the relationship per sample, using a regression model. The model should include some parameter of interest, e.g. a slope or a half-time. Collect the fitted values of this parameter.
You can finally test hypotheses about the difference or the ratio of this parameter depending on the additive. For instance, if the regression model is a simple linear regression and the relevant parameter is the slope of the regression line, you can use a t-test. If the regression model is exponential and the parameter of ionterest is the half-time, you can use a t-test with the logarithm of the half-time.
You can get the original data distribution fit and compare the logged data with that or something like this. The tool you can use is the Gamlss or fitdistrplus in the R software.
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