The Buggy lab
9/12/21
This is the Buggy Lab, a lab where we let a buggy run for a certain period of time and saw how it effected it's position
Overview
Research Question: How does time effect the position of our Buggy?
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Independent Variable: The time the Buggy was allowed to traverse
Dependent Variable: The final position of our Buggy
Control Variable: Keeping the initial position the same
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The Controlled Variables
To keep this experiment Controlled, we the same Buggy in out trials, as well as keeping the same initial position to avoid drastic inaccuracies in our results.
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Collection of Data
To collect our data we used a smartphone timer to set certain intervals of time the buggy was allowed to traverse. This allowed us to collect data based on the change in position of the buggy in a certain period of time.
Meet The Team
Procedure
- Firstly we set the Buggy up on the floor with 2 meter sticks placed in front of it.
- We used the smartphone timer to measure the change in position of the buggy from its initial position.
- We hovered the Buggy over the initial position, dropping the Buggy and picking it back up at the end of the time interval.
- We collected data from the change in position over 5 time intervals: 1 second, 2 seconds, 5 seconds, 10 seconds and 15 seconds.
- We preformed 3 trials for each interval in this experiment.
- Link to ppt of detailed procedure can be found below
Lab Setup
Buggy
Meter Stick
Raw Data
We were able to calculate these data points by taking the average of each of our trials.
Better quality CSV file containing all raw data is also embedded below
Processed Data
For our experiment there wasn't a need particularly to process the Raw Data that we collected, as our data went directly from the collection process into a graphical representation. We were able to create a graph using our data set: a position-time graph (below)
Figure 1
The position-time graph (above) shows the direct result of transitioning our raw data into a XY plot, where the X-axis represents distance in meters and the Y-axis (or T) represents time in seconds.
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For this experiment we went for a linear fit for the data (y = mx + b), taking into account that the buggy was travelling at a constant speed and not accelerating as it went along. The slop of the data above represents our speed, as an interpretation of it would be that a slope of 11.99 (the slope of our graph) would mean that the buggy was travelling 11.99 centimetres per second. We can also derive our velocity from this graph, as the sign of the slope shows positive, meaning we were travelling forwards and not backwards.
Download of .cmbl file for position-time graph is below
The purpose of this lab was to calculate the velocity of the Buggy during certain intervals of time. As shown above, we were able to do this by calculating the average distance the buggy was able to travel in one second for each of the data points. This then translated into the slope of our position time graph (figure 1) as the velocity of the Buggy. The results of this lab can be generalised to other situation in order to determine the velocity of an object over a period of time.
Conclusion
In conclusion, we can say that an interpretation of the data would be that, because the buggy was travelling at a constant speed, the velocity of the buggy (as reflected by the position time graph) would be around 12 meters per second. We did have some moments where the Buggy would loose power halfway through a trail and stop, which would lead to some uncertainties, but in general this assumption would hold. Due to the amount of data points that we were able to collect (5 time intervals and 3 trials each) we can say that we were fairly confident in our results. If given more time, perhaps our confidence in our results would be even higher. Our lab was not flawless, however, as can be seen by the fact that our initial position is not (0,0). Uncertainties regarding our lab and improvements to our lab will be discussed in the section below.
Uncertainties and Limitations
Syncing up of the dropping of the Buggy and timer - the times at which the Buggy was dropped and the timer was started might not completely be the same, therefore leading to some uncertainties in the measurement.
Measurement error - we used a meter stick for our measurements, with the smallest unit being centimetres, this generates uncertainty in measurement as we would not be able to know the exact final position of the Buggy.
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Improvements? - perhaps using a more precise meter stick to improve the accuracy of our data, or preforming more trials to yield the same result.