Research Question - How does time a tumbler buggy operates affect the distance it will travel?
DESIGN |
|
Independent Variable the amount of time the buggy runs
Dependent Variable the distance the buggy travels
Controls the type of buggy and the surface the buggy travels on
The Controlling Variables
We decided to keep the type of the buggy a constant because different buggies have different set speed, which can affect the result by a lot. We also decided to keep the surface the buggy travels on as a constant because different surfaces gives different fractions, which will affect the speed the buggy travels and therefore make the result not as accurate.
Lab Materials
- Computer (w/LoggerPro)
- Video Camera (smartphone)
- Meter Stick (2.275m)
- LabQuest Mini
- Motion Sensor
- Timer/Stopwatch
Data Collection
For this particular lab, we collected data in 3 different ways to improve our confidence in our results, which includes
Stopwatch
Motion Sensor
Video Analysis
Different ways of collecting data require different lab set-ups and procedures. Our procedures and results of all three methods are listed below.
Stopwatch
Procedures
#1 Start the buggy in the air at the edge of the meterstick and set the buggy down as the timer starts timing
#2 Let the buggy run for 0.5 seconds
#3 At the 0.5 seconds, record the position of the buggy
#4 Repeat steps #1-#3 three times then move on to the next set of time
#5 Increase times until we have collected all measurements
Lab Set-up
#1 Start the buggy in the air at the edge of the meterstick and set the buggy down as the timer starts timing
#2 Let the buggy run for 0.5 seconds
#3 At the 0.5 seconds, record the position of the buggy
#4 Repeat steps #1-#3 three times then move on to the next set of time
#5 Increase times until we have collected all measurements
Lab Set-up
Raw and Processed Data
In this experiment, we used a table to record our chosen time period in seconds, and the distance the buggy traveled in meters. In order to get the result as accurate as possible, we did 3 trials for each set of time, the raw data collected in the experiment are under the columns of Trial 1, Trial 2, and Trial 3. To process our raw data, we find the average distance traveled of the three trials by adding them together and divide the sum by 3.
Example of data processing:
For time=0.5 seconds,
Average Distance = (0.39 m+0.33 m+0.36 m) / 3 = 0.36 meters
Example of data processing:
For time=0.5 seconds,
Average Distance = (0.39 m+0.33 m+0.36 m) / 3 = 0.36 meters
Graph of the Collected Data
This graph of the collected data clearly shows a strong linear progression.
Slope = velocity of the buggy = 0.411 meters/second
Y-intercept = initial position = 0.172 meters
Equation:
Position(m) = 0.411m/s * Time(s) + 0.172m
Uncertainties
We think that the data collected by a stopwatch will have the greatest amount of uncertainties among all three methods of data collection, because using a stopwatch involves many types of uncertainties such as the reaction time of the person timing and the reaction time of the person stoping the buggy.
Slope = velocity of the buggy = 0.411 meters/second
Y-intercept = initial position = 0.172 meters
Equation:
Position(m) = 0.411m/s * Time(s) + 0.172m
Uncertainties
We think that the data collected by a stopwatch will have the greatest amount of uncertainties among all three methods of data collection, because using a stopwatch involves many types of uncertainties such as the reaction time of the person timing and the reaction time of the person stoping the buggy.
Motion Sensor
Procedures
#1 Connect the motion sensor to the LabQuest mini, and connect the LabQuest mini to the computer and open LoggerPro
#2 Start the buggy in the air at the left edge of the meterstick
#3 Released the buggy and it starts moving towards the motion sensor, then start recording data
#4 Stop data collection before the buggy hits the motion sensor
Lab Set-up
#1 Connect the motion sensor to the LabQuest mini, and connect the LabQuest mini to the computer and open LoggerPro
#2 Start the buggy in the air at the left edge of the meterstick
#3 Released the buggy and it starts moving towards the motion sensor, then start recording data
#4 Stop data collection before the buggy hits the motion sensor
Lab Set-up
Graph of Raw Data
Similar to the graph of data collected by stopwatch, this graph shows a strong linear progression as well. However, the difference between the two graphs is that this linear graph as a negative slope, which means the buggy is moving in a constant negative velocity. This makes sense because in our experiment, the buggy is moving towards the motion sensor, which makes the position smaller and smaller proportionally as time goes on to about 5 seconds.
Slope = velocity = -0.4193 meters/second
Y-intercept = the initial position = 2.071 meters
Equation:
Position(m) = -0.4193 m/s * Time(s) +2.071 m
Uncertainties
The result of the motion sensor should be more accurate than the result of the stopwatch experiment because the motion sensor is able to collect a bigger amount of data points in very small intervals of time which eventually lines up and present us a graph with a strong linear progression. In particular, the motion sensor collects data every 0.05 seconds in our experiment, human timers can never be able to do this using a stop watch.
Slope = velocity = -0.4193 meters/second
Y-intercept = the initial position = 2.071 meters
Equation:
Position(m) = -0.4193 m/s * Time(s) +2.071 m
Uncertainties
The result of the motion sensor should be more accurate than the result of the stopwatch experiment because the motion sensor is able to collect a bigger amount of data points in very small intervals of time which eventually lines up and present us a graph with a strong linear progression. In particular, the motion sensor collects data every 0.05 seconds in our experiment, human timers can never be able to do this using a stop watch.
Video Analysis
Procedures
#1 Set up the camera to look at a side of the buggy so it moves across the screen from left to right
#2 Include a meterstick in the frame so we can scale the video analysis later
#3 Record a video of the buggy running across the screen
#4 Save the video and import it to computer
#5 Use LoggerPro to run a video analysis
Recorded Video
Below shows the video that is going to be analyzed
#1 Set up the camera to look at a side of the buggy so it moves across the screen from left to right
#2 Include a meterstick in the frame so we can scale the video analysis later
#3 Record a video of the buggy running across the screen
#4 Save the video and import it to computer
#5 Use LoggerPro to run a video analysis
Recorded Video
Below shows the video that is going to be analyzed
Graph of Raw Data
Very similar to the first two graphs of the stopwatch experiment and the motion sensor analysis, this graph also shows us a very strong linear progression.
Slope = velocity = 0.4618 meters/second
Y-intercept = initial position = -0.0001 meters
Equation:
Position (m) = 0.4618 m/s * Time(s) - 0.0001m
Uncertainties
We think that the uncertainties involved in video analysis should be less than the stopwatch experiment but more than the motion sensor experiment. Because of the shooting angle of the video, the motion of the buggy might be not as accurate when we are analyzing the video; however, it is still more credible since we are analyzing the real situation recorded by a camera. The fact that the y-intercept in this graph is about 0.00 meters is a very accurate result, because in the real lab, the position of the buggy is exact 0 meters when we haven't started timing.
Slope = velocity = 0.4618 meters/second
Y-intercept = initial position = -0.0001 meters
Equation:
Position (m) = 0.4618 m/s * Time(s) - 0.0001m
Uncertainties
We think that the uncertainties involved in video analysis should be less than the stopwatch experiment but more than the motion sensor experiment. Because of the shooting angle of the video, the motion of the buggy might be not as accurate when we are analyzing the video; however, it is still more credible since we are analyzing the real situation recorded by a camera. The fact that the y-intercept in this graph is about 0.00 meters is a very accurate result, because in the real lab, the position of the buggy is exact 0 meters when we haven't started timing.
CONCLUSION |
|
According to the data and graphs from all three methods of data collection, we are very confident to suggest that the position of the buggy is linearly related to the time the buggy runs. The absolute value of the slopes of graphs of all three methods are very similar, they are 0.411 meters/second, 0.4193 meters/second, and 0.4618 meters/second. The slopes indicate the velocity of the buggy, which interprets as the change in position of the buggy for each and every second of time. All linear progressions have constant slopes, which means the buggy is moving in a constant velocity in all three experiments. The y-intercept of the graph, on the other hand, tells us initial position, which is the position of the buggy when time is ZERO. In our experiments, the y-intercepts of the first and third graphs are very similar (0.172 meters and -0.0001 meters); this is because for these two experiments, we designed for the buggy to go away from the origin. On the contrary, the y-intercept of the second graph is 2.071 meters, which is because we designed the experiments for the buggy to go towards where the motion sensor was, so in this case, the y-intercept is actually how far the buggy is from the origin.
We generated graphs and equations for all three experiments, which tells us that graphs and equations are very useful in modeling a motion. In this particular experiment, we found out that the general equation to model the relationship between position and a constant moving object is
Position = Velocity * Time + Initial Position
We generated graphs and equations for all three experiments, which tells us that graphs and equations are very useful in modeling a motion. In this particular experiment, we found out that the general equation to model the relationship between position and a constant moving object is
Position = Velocity * Time + Initial Position
Evaluating Procedures and Uncertainties
Stopwatch - One of the uncertainties we had was reaction time. Even though we had the same person timing and stopping the buggy to reduce the
amount of reaction time, and we did 3 trials for each chosen time, it is impossible to stop the buggy and the stopwatch at a certain set of
time. We also had to set the buggy in the air and release it to the track because the track doesn't fit the distance traveled by the buggy
when longer time periods are measured. Also, since our buggy is so fast that we have to stop it very aggressively, sometimes I can feel
that the buggy is moving slower than the previous time since we pushed on it really hard to stop it at a specific point. It would be
awesome if we have buggies that we can set the amount of time it runs and at that amount of time the buggy just stops by itself
automatically, so we don't have to break anything.
Motion Sensor - One of the biggest uncertainties we had in this lab was in this experiment. Since we got a very fast buggy, we wasn't be able to have the
buggy moving on the track for 5 seconds, the track was too short for the buggy to run 5 seconds, therefore, I had to stop the buggy
before it hits the motion sensor on the other end of the meterstick, so the motion of my hand stopping the buggy might have been part
of the final graph.
Video Analysis - The angle the video was taken is a big part of the uncertainties here. Different angle can result in very different data and analysis.
However, it is impossible to take a video of perfect angle without any shifts from the center or tilting.
Improving the Investigation
#1 Combine two tracks/metersticks together to create a longer space for the buggy to move
#2 For better comparison and contrast, design the experiment for the buggy to move away from the motion sensor. Therefore, we will have a positive
slope and it will make it easier to compare all three graphs
#3 Maybe we should just use a slower buggy, this way, the data will be much accurate for the stopwatch experiment.
Stopwatch - One of the uncertainties we had was reaction time. Even though we had the same person timing and stopping the buggy to reduce the
amount of reaction time, and we did 3 trials for each chosen time, it is impossible to stop the buggy and the stopwatch at a certain set of
time. We also had to set the buggy in the air and release it to the track because the track doesn't fit the distance traveled by the buggy
when longer time periods are measured. Also, since our buggy is so fast that we have to stop it very aggressively, sometimes I can feel
that the buggy is moving slower than the previous time since we pushed on it really hard to stop it at a specific point. It would be
awesome if we have buggies that we can set the amount of time it runs and at that amount of time the buggy just stops by itself
automatically, so we don't have to break anything.
Motion Sensor - One of the biggest uncertainties we had in this lab was in this experiment. Since we got a very fast buggy, we wasn't be able to have the
buggy moving on the track for 5 seconds, the track was too short for the buggy to run 5 seconds, therefore, I had to stop the buggy
before it hits the motion sensor on the other end of the meterstick, so the motion of my hand stopping the buggy might have been part
of the final graph.
Video Analysis - The angle the video was taken is a big part of the uncertainties here. Different angle can result in very different data and analysis.
However, it is impossible to take a video of perfect angle without any shifts from the center or tilting.
Improving the Investigation
#1 Combine two tracks/metersticks together to create a longer space for the buggy to move
#2 For better comparison and contrast, design the experiment for the buggy to move away from the motion sensor. Therefore, we will have a positive
slope and it will make it easier to compare all three graphs
#3 Maybe we should just use a slower buggy, this way, the data will be much accurate for the stopwatch experiment.