use a spreadsheet to do an exponential regression
I have one question.
Usually when we have a set of data to find the rate of decay,
T = T0 - Tm e^(-kt) + Tm
T0 - Initial temperature
Tm - Surrounding temperature (At 20'C)
k - Constant value
T - Temperature
For eg. when time = 1, T = 90 (Knowing that t= 0 T=100)
So, we will write
90 = 100 - 20 e^(-k*1) + 20
90-20 = 80 e^(-k)
7/8 = e^(-k)
Take natural logs on both sides,
ln 7/8 = -k
k = -0.133531
And because we have t=1,2,3,4,5,6,7,8....n
So I found all the k values and averaged them to find a mean value
However, I was thinking just by averaging the rate of decay and find
an averaged constant value to find the final function that models
the real data (Experimented data) is not accurate enough.
So I wish to seek for some help on this. Can you please tell me
another way to find a good 'k' value that has higher accuracy to
model the data?
The problem with the method you've described - that of calculating a value of for each pair of values of and and then finding the mean of the answers - is that it will give equal weight to all the experimental data, without rejecting any values that are (through experimental error, perhaps) inconsistent with the majority of the others.
A better method would probably be to manipulate the equation much in the way you have already done, like this:
and then plot values of against , the graph of which will (theoretically) be a straight line with gradient . You'll then be able to plot the best straight line through these points, and reject any data that are clearly in error.
Can you please show me how to actually do it? (I understand the concept)
But its just that.. it doesnt make sense for me to find all the k values and average it at the end..
Can you please show me how to find a good single k value for this?
T=(To-Tm) e^(-kt) + Tm
With this model!
Thank you so much.. Im so confused - -
But Im trying to do a different thing. With all the different T and t values!
I want to find a single k value without having to go thru,
77e^-kt = 90
sub t = 2
then find k
and do this again and again for all other datas then average all the k value.
I want to find the k value more accurate by one method.. can you please please explain ? please.. Im really confused and frustrated! Argh
I've done it 'manually' using your data (with ), by plotting against , as I suggested. The graph is shown, with the line indicating what I estimate to be the line of best fit.
Since the equation of this line is , its gradient gives a (single!) estimate of the value of (and, of course, the intercept gives the value of ).
The gradient, then, is approx , giving the value of as .