Uncertainties in my Experiment

In summary: He has a graph of temperature against time from which he will determine, after processing, whether or not rate of cooling is proportional to the (excess temperature)5/4. However, when he decided to go log his rate of cooling, he noted that it is negative. How am Peter supposed to do a graph of the log of excessive temperature against the log of the rate of cooling if his gradient for the rate of cooling was negative? Thanks, Peter G.
  • #1
Peter G.
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0
I am finished collecting data for my experiment and now I am processing it.

I have a graph of temperature against time from which I will determine, after processing, whether or not rate of cooling is proportional to the (excess temperature)5/4

I've produced my tables, calculated uncertainties and etc. but when I decided to go log my rate of cooling, I noted that it is negative. How am I supposed to do a graph of the log of excessive temperature against the log of the rate of cooling if my gradient for the rate of cooling was negative?

Thanks,
Peter G.

EDIT: Anyone has any ideas? I am happy to explain it more clearly if needed, give more information etc... I am really confused and I am doing everything within my reach to try and figure this out but I am helpless!
 

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  • #2
Actually, the rate of "heating" is negative.
The rate of "cooling" is positive, so you can take a log.
 
  • #3
Hi, thanks!

But would you mind explaining? Because in my graph, the gradient is negative. When the temperature is high, the rate of cooling is faster in it.
 

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  • #4
Peter G. said:
Hi, thanks!

But would you mind explaining? Because in my graph, the gradient is negative. When the temperature is high, the rate of cooling is faster in it.

The rate of heating is the "increase in temperature per unit of time".
The rate of cooling is the "decrease in temperature per unit of time".
So the rate of cooling is the opposite of the rate of heating.

Your gradient is the rate of heating, so you need to apply a minus sign.

At a high excess temperature the rate of cooling is indeed higher than at a low temperature.
This is exactly what you need to model, that is, the rate of cooling versus the excess temperature.
Your graph shows the temperature versus the time.

If you would make a separate column for the excess heat, and another separate column for the rate of cooling, you can make a new graph from that.

If you furthermore take the log of the rate of cooling, and graph that versus the excess heat, you should find a slope that according to your problem statement might be 5/4.
 
  • #5
Thanks a lot for your help :smile:

I did all the calculations with several of my attempts and the best line I got was of gradient of 1.73 as an oppose to 1.25, which, I think is not that far off :redface:

Peter G.
 

1. What is an uncertainty in an experiment?

An uncertainty in an experiment refers to the potential error or variation in the results due to factors such as measurement limitations, experimental conditions, or human error. It represents the range of possible values that the true result could fall within.

2. How do I calculate uncertainties in my experiment?

To calculate uncertainties, you need to identify potential sources of error and estimate the range of values they could contribute to the final result. Then, use statistical methods such as standard deviation or propagation of errors to determine the overall uncertainty.

3. Why is it important to consider uncertainties in an experiment?

Considering uncertainties is crucial in order to accurately interpret and communicate the results of an experiment. It helps to assess the reliability and validity of the data and provides a more complete understanding of the experiment's outcomes.

4. How can I reduce uncertainties in my experiment?

To reduce uncertainties, you can use more precise measurement tools, increase the sample size, or repeat the experiment multiple times to account for any random errors. It is also important to carefully control experimental conditions and minimize human error.

5. What is the difference between systematic and random uncertainties?

Systematic uncertainties are consistent and predictable errors that affect the entire set of data, while random uncertainties are unpredictable and can vary from one measurement to another. Systematic uncertainties can be reduced by improving experimental methods, while random uncertainties can be reduced by increasing the sample size or repeating the experiment.

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