Physics Asked on August 8, 2021
I’m doing an experiment investigating the Mpemba effect. I measured some factors that may have contributed to the time it took the water to freeze (defined as the point where it becomes a total solid, i.e. at the end of the phase change), such as the time it took to reach 0 degrees Celsius, the lowest temperature it reached before freezing (i.e. how much it supercooled), and the time it spent at the freezing temperature.
I’m not sure what to call those variables since I couldn’t vary them, which means it’s not an independent variable. I’m not sure if they are dependent on the initial temperature because there are still so many things unknown about the Mpemba effect and factors which I couldn’t control such as the roughness of the container that it was in (which contributes to the supercooling). And they’re obviously not controlled variables since I couldn’t control them.
But my best guess is that it barely falls under either independent or dependent variable.
What you call them is not important. What is important is how you use them, what statistical methods you use and so forth.
The most important thing to consider is how accurately they are known. In a standard linear model or least squares regression, the explanatory variables are assumed to have no uncertainty. So if you plan on using such techniques then they should be things that you can know far more precisely than your predicted variable.
If that is not the case then you may need to use other methods, such as error in variables techniques.
Answered by Dale on August 8, 2021
Here are two suggestions - if you read about each, you can decide which bests fits your situation.
Hope this helps.
Answered by John Hunter on August 8, 2021
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