![]() ![]() Do not hang masses greater than 600 grams from the springs, or they may be permanently stretched out of shape. Important: The springs you are working with are quite delicate. The stretching force is just the weight (in Newtons) of the hanging mass. Use LabQuest App, Logger Pro, or graph paper. Plot a graph of the average acceleration (y-axis) vs. Note that x is the hypotenuse of a right triangle. Using trigonometry and your values of x and h in the data table, calculate the sine of the incline angle for each height. The stretch distance is the spring length minus the reading for zero added mass. Fit parameters and parameter errors from bootstrap method (20x error): pfit 2.54029171e-02 3.84313695e+01 2.55729825e+00 perr 6.41602813 13. Experiment 4: Determining g on an Incline ANALYSIS. Record your observations in the Table below.ģ. Choose a reference point somewhere near the bottom of the spring or masses from which to record the differences. Notice the resting position of the spring and record it first.Ģ. ![]() To determine the spring constant of the spring, hang masses from 100 to 600 grams, in 100-gram steps, on the spring. You can assume that the force applied on the spring by the hanging mass is proportional to the restoring force applied by the spring on the massĬalculation Ia: (1 pts) For each value of hanging mass on the spring, calculate the force applied 0n the spring:ġ. ![]() Question Ia: (1 pts) Based on your Graph 1a, can you conclude that the mass-spring system follows Hooke's law? Why or why not?Ĭalculation Ib: (2 pts) Using the slope of the linear regression in Graph 1a, calculate an experimental value for the spring constant and its uncertainty (using propagation oferror) for the conical spring: NB. Graph Ia: (3 pts) Using the data from Table 1 in the experimental details file and your Calc: 1a results, plot a graph of the position of the platform as a function of the force 'applied on the spring: Use the linear regression tool to fit your data and be sure to show the uncertainty ofyour linear fit parameters You should also show your data table in your graph file by using the "print" function in Logger Pro (and not "print graph") Be sure to give your graph an appropriate title that describes the data shown: My ultimate goal is simply to have the values and associated uncertainties that I can then put into an array and use further in my code.SOLVED: Calculation Ia: (1 pts) For each value of hanging mass on the spring, calculate the force applied 0n the spring: I can get a covariance matrix but I don't know what order this is being displayed in. I tried to just print the uncertainty of the parameter using the stderr as in the last line of code above but this just returns 'None'. My problem is calling them and using them. When I generate a fit report, I get uncertainty values so they are clearly being computed. With Blockly, students can create custom data collection parameters. Graphical Analysis and Spectral Analysis wont launch if there is no internet connection. If students are collaborating on a lab activity across devices, they can set up a. M_lm2 = lmfit.Parameter('m', value=m, vary=False)Ĭ_lm2 = lmfit.Parameter('c', value=c, vary=False) What are the differences between Graphical Analysis Pro and Logger Pro Graphical Analysis app wont open my. Sig_4_lm2 = lmfit.Parameter('sig_4', value=gaus1) Sig_3_lm2 = lmfit.Parameter('sig_3', value=gaus1) Sig_2_lm2 = lmfit.Parameter('sig_2', value=gaus1) Sig_1_lm2 = lmfit.Parameter('sig_1', value=gaus1) Sig_core_lm2 = lmfit.Parameter('sig_core', value=gaus1) X0_core_lm2 = lmfit.Parameter('x0_core', value=gaus1) ![]() With lmfit it doesn't seem to be so simple.Ī_lm2 = lmfit.Parameter('a', value=a_est)ī_lm2 = lmfit.Parameter('b', value=b_est) With spo.curve_fit, we just get the covariance matrix when we fit and we can take the diagonal and square root to find the uncertainties. I'm looking for the easiest way of outputting the uncertainty in the fitted parameters. ![]()
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