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)1420236.50.00029327.50.00048420.50.000785150.001356130.001557110.002418100.00297990.003591080.004131170.00484 v Part 2: Discovering Limits to the simple modelExperiments like the one in the previous section have lead scientist to conclude that the force between two charged objects oftenvaries as the inverse square of their distance. This relationship, known as the inverse square law, pops up all over physics. Forexample, 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 Distance0.0050.0040.0030.0020.00110152025303540Distance (cm)Display Curve Fit UncertaintiesForce Curve:F = A/P?A: 0.263 N - cm²RMSE : 0.000259 NForce (N)

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.

	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