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1) Return to your data table above and continue to collect data for those points for when the scale reading exceeds 0.002N. Make a graph of force vs 1/(distance^2) and click the "linear regression" checkbox to fit the data to a line. Then deselect any force greater than 0.002 N so that it won't be included in the linear fit. You can deselect any point that you want to exclude from the fit simply by clicking on that point on the graph. Does your new data match the trend established by the previous data? If not, do the new data points show a lower-than-expected or higher-than-expected force? Think of at least 4 tentative explanations as to why the data collected towards the end of the video would not show the same pattern as the data collected towards the beginning of the video.

1) Return to your data table above and continue to collect data for those points for when the scale reading exceeds 0.002N. Make a graph of force vs 1/(distance^2) and click the "linear regression" checkbox to fit the data to a line. Then deselect any force greater than 0.002 N so that it won't be included in the linear fit. You can deselect any point that you want to exclude from the fit simply by clicking on that point on the graph. Does your new data match the trend established by the previous data? If not, do the new data points show a lower-than-expected or higher-than-expected force? Think of at least 4 tentative explanations as to why the data collected towards the end of the video would not show the same pattern as the data collected towards the beginning of the video.

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1) Return to your data table above and continue to collect data for those points for when the scale reading exceeds 0.002N.

Make a graph of force vs 1/(distance^2) and click the "linear regression" checkbox to fit the data to a line. Then deselect any force greater than 0.002 N so that it won't be included in the linear fit. You can deselect any point that you want to exclude from the fit simply by clicking on that point on the graph.

Does your new data match the trend established by the previous data? If not, do the new data points show a lower-than-expected or higher-than-expected force? Think of at least 4 tentative explanations as to why the data collected towards the end of the video would not show the same pattern as the data collected towards the beginning of the video.

 

  Distance (cm) Force (N)
1 42 0
2 36.5 0.00029
3 27.5 0.00048
4 20.5 0.00078
5 15 0.00135
6 13 0.00155
7 11 0.00241
8 10 0.00297
9 9 0.00359
10 8 0.00413
11 7 0.00484

 

v Part 2: Discovering Limits to the simple model Experiments like the one in the previous section have lead scientist to conclude that the force between two charged objects often varies as the inverse square of their distance. This relationship, known as the inverse square law, pops up all over physics. For example, it describes how the light intensity of a star decreases with distance and also how its gravitational field decreases. In this section, we'll explore a situation where that simple model starts to fall short. 2r
Transcribed Image Text:v Part 2: Discovering Limits to the simple model Experiments like the one in the previous section have lead scientist to conclude that the force between two charged objects often varies as the inverse square of their distance. This relationship, known as the inverse square law, pops up all over physics. For example, it describes how the light intensity of a star decreases with distance and also how its gravitational field decreases. In this section, we'll explore a situation where that simple model starts to fall short. 2r
Force vs Distance 0.005 0.004 0.003 0.002 0.001 10 15 20 25 30 35 40 Distance (cm) Display Curve Fit Uncertainties Force Curve: F = A/P? A: 0.263 N - cm² RMSE : 0.000259 N Force (N)
Transcribed Image Text:Force vs Distance 0.005 0.004 0.003 0.002 0.001 10 15 20 25 30 35 40 Distance (cm) Display Curve Fit Uncertainties Force Curve: F = A/P? A: 0.263 N - cm² RMSE : 0.000259 N Force (N)
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