Group Members: Moon Ding, Jenna Kim Question: How does time affect the position of the buggy? Variables:
Conclusion: In this lab, we looked at how a change in time would affect the position of an electric buggy moving at a constant pace.Along with a constant speed, we kept the surface and steepness of the surface the same to create a consistent environment for each trial. Additionally, we found many data points in a large range to raise confidence in our data. However, we did not do repeated trials. By looking at our data, we can see that the buggy does, in fact, move at a constant speed, leading to a linear line of best fit representing the relationship between position and time. The slope in this graph is positive, showing that the buggy was moving away from the origin or predicted Y-intercept. From our raw data, we could predict that the slope or average speed of the buggy would be about 10 cm/second. Based on the line of best fit, we now know that the average speed of our buggy was 9.383 cm/second. So, for each second that passed, the position of the buggy moved 9.383 cm farther from the origin. In discussing the meaning of the Y-intercept, we conducted a slightly different experiment with the buggy. Instead of the initial position of the buggy being at the origin, the initial position was -100 cm. We repeated the same procedure for the first experiment, but with a smaller range in our data points and adjusting the initial position. The results for this experiment are shown below: From the line of best fit, we can see that the slope is very close to the slope from the original experiment. However, the Y-intercept of this graph is much lower: -89.99 cm, versus -7.721 cm. While the speed of the buggy, environment, and methods were kept consistent for the two experiments, the change of the initial position, and thus, the Y-intercept of the second graph, led to a very different equation for the line of best fit. Weaknesses/Uncertainties:
While there were many things done in our lab to increase confidence in our data and results, there are still uncertainties. Because all of the timing was done by one person, and the start and stop of the buggy was also done by another person, reaction time definitely had an effect on our results. Thus, it cannot be guaranteed that our data is very accurate-- however, we can provide a general idea of the average speed of the buggy. While it is not a crucial step to collecting accurate data, not doing repeated trials could also have affected our data accuracy. Improvement: An improvement to our method and procedure is to include repeated trials. As mentioned previously, it does not necessarily play a crucial part in determining how accurate data is, but because of the uncertainty due to fluctuations in reaction time, having multiple data points for one time could increase confidence in our data.
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