Reflection 3: Ziplock Zoomer

     Our most recent investigation was the perfect example of classical mechanics and how it works. We know that the force of an object is equal to mass x acceleration, which in this case the acceleration would be equal to gravity. Knowing this, Newton's second law of motion, we can predict that there will be some sort of linear relationship in everything we interact with on a daily basis, when we measure force of course. 

    In the aforementioned investigation that was cleverly named "Ziplock Zoomer", we tested the relationship of force and mass using quarters, a ziplock back, paper clip, and IOLab. We recorded our data in logger pro and created two graph based on our information. So how did this all go down? Well, we hooked up our IOLab and prepped it by calibrating it, then we used a paper clip to hang a ziplock back from the IOLab and used the ziplock bag to hold the quarters we were using in this experiment. We recorded our results quarter by quarter for a total of 6 runs. The first being the empty ziplock which was our "zero" point, then we inserted 1 quarter at a time until we reached 5 quarters in the ziplock bag. We can view the relationship below in both the chart and graph from our trial:


    From here, we created a line of best fit to get the average force, which is denoted by the slope of the line, in Newtons. We then went on to create a graph using an adjusted force to consider a more proportional relationship, as shown below:
    
    At first, my adjusted force graph looked off in that my line of best fit was not what it should have been. However, with the help of my peers and instructors, I was able to correct the issue, by changing my parameters and limits of my graph, to make it look more pleasing. The more we shared information, classmates and instructors alike, it was easy to see that we all had similar data. Further observing the data, if one was to add, let's say a quarter for each extra run, we could see a linear relationship that would continue and could be predicted by our line(s) of best fit. 
    There are factors that each individual is subjected to that could contribute to having similar data and not the exact same data, but that is the beauty of science. The variation yet commonality of information that we can share and learn from.






Comments

  1. AgnesMarch 23, 2021 at 3:42 PM

    Hey Sarah!
    An excellent description of our last experiment. One thing caught my eye: I like that you included that your best fit did not look like the way it was supposed to do. When writing a report, it is important to take note of the errors encountered through an experiment. I should have mentioned in my reflection that I had calibration issues and therefore, my first run did not show the data as expected. Overall, our data and predictions looks similar.

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