Numerical error = No. of dice remaining X %error
For the 1st shuffle: 84 x 10% = 8% (round up to the nearest integer since the number of dice must be an integral value)
Based on the table above, the number of dice remaining is plotted against the number of times shuffled to produce a graph of decay model of dice. Note that an exponential trend line is plotted to obtain an equation in the form of N = N0 e –λt.
From the graph, the radioactive equation is found to be:
y = 127.0e-0.22x
The following table shows the necessary values to plot a ln graph of the decay model of dice.
Justification
N/N0 = Number of dice remaining / Initial total number of dice
For 1st shuffle: 84 / 100 = 0.84
-ln(N/N0) = -ln(0.84) = 0.17 (2s.f.)
Note: negative ln is used to produce a trend line with a positive slope
The following table shows the necessary values for the maximum and minimum lines of best fit.
Justification
For 1st shuffle:
Max line of best fit = -ln(N - numerical error /N0) = -ln[(84-8)/100] = 0.27 (2s.f.)
Max line of best fit = -ln(N + numerical error /N0) = -ln[(84+8)/100] = 0.08 (2s.f.)
Note: negative ln is used to produce a trend line with a positive slope
Percentage error = (Max – min) / 2 = (0.27 – 0.08) /2 = 9.6% (2s.f.)
From the equation of the ln line plotted, the value for the decay constant, λ is found to be 0.22.
The half-life, t1/2, of the decay model can be calculated from t1/2 = (ln2/λ), which is derived from N = N0 e –λt.
Data collection
Class data
Data processing
Justification of errors
The percentage error is larger at first (10%) since there is more dice involved in the shuffling of the container, so there is a higher possibility that more dice are not rolled probably. The percentage error becomes smaller gradually since the number of dice in the container decreases when more shuffles are made, so the possibility that the dice are not rolled probably decreases at the same time.
Numerical error = No. of dice remaining X %error
For the 1st shuffle: 79 x 10% = 8% (round up to the nearest integer since the number of dice must be an integral value)
Based on the table above, the number of dice remaining is plotted against the number of times shuffled to produce a graph of decay model of dice. Note that an exponential trend line is plotted to obtain an equation in the form of N = N0 e –λt.
From the graph, the radioactive equation is found to be:
y = 97.11e-0.22x
The following table shows the necessary values to plot a ln graph of the decay model of dice.
Justification
N/N0 = Number of dice remaining / Initial total number of dice
For 1st shuffle: 79 / 100 = 0.79
-ln(N/N0) = -ln(0.79) = 0.24 (2s.f.)
Note: negative ln is used to produce a trend line with a positive slope
The following table shows the necessary values for the maximum and minimum lines of best fit.
Justification
For 1st shuffle:
Max line of best fit = -ln(N - numerical error /N0) = -ln[(79-8)/100] = 0.34 (2s.f.)
Max line of best fit = -ln(N + numerical error /N0) = -ln[(84+8)/100] = 0.14 (2s.f.)
Note: negative ln is used to produce a trend line with a positive slope
Percentage error = (Max – min) / 2 = (0.34 – 0.14) /2 = 10% (2s.f.)
From the equation of the ln line plotted, the value for the decay constant, λ is found to be 0.22. This decay constant calculated from class data is the same as the decay constant calculated from our group’s data. This can possibly due to the large number of shuffles made (20), which evens out the error involved with the differences in the speed and direction of shuffling by different students.
The half-life, t1/2, of the decay model can be calculated again from t1/2 = (ln2/λ), which is derived from N = N0 e –λt.
Since the decay constant calculated from class data is the same as that calculated from our group’s data, the half-life of the decay model is also the same.