understanding experimental data determine which line is better fit for the databut that would be beside the point this is not question that can be answered by statistics after all we could throw out all the data except any two points and know that polyfit would find line that would be perfect fit for those two points one should never throw out experimental results merely to get better fit here we justified throwing out the rightmost points by appealing to the theory underlying hooke' lawi that springs have an elastic limit that justification could not have been appropriately used to eliminate points elsewhere in the data the behavior of projectiles growing bored with merely stretching springswe decided to use one of our springs to build device capable of launching projectile we used the device four times to fire projectile at target yards ( inchesfrom the launching point each timewe measured the height of the projectile at various distances from the launch point the launching point and the target were at the same heightwhich we treated as in our measurements the data was stored in file with the contents distance trial trial trial trial the first column contains distances of the projectile from the target the other columns contain the height of the projectile at that distance for each of the four trials all of the measurements are in inches the code in figure was used to plot the mean altitude of the projectile against the distance from the point of launch it also plots the best linear and quadratic fits to the points (in case you have forgotten the meaning of multiplying list by an integerthe expression [ ]*len(distancesproduces list of len(distances ' which isn' to say that people never do projectile is an object that is propelled through space by the exertion of force that stops after the projectile is launched in the interest of public safetywe will not describe the launching device used in this experiment suffice it to say that it was awesome