Write a one page summary in MS Word describing how/where you collected your data, what distribution was the best fit (for both Arrival & Service), and your interpretation of the Chi Square goodness-of-fit test for each.
A refresher on squared error:
Squared error is the difference between an actual observed value and its fitted (or predicted) value in regression analysis. The smaller the squared error (i.e., towards zero), the better. A small squared error value very close to zero implies that the deviation between an observed value and its fitted value is almost zero => variability in the data is small => distribution x provides a good fit to the data. A large squared error implies that the deviation between an observed value and its fitted value is large => variability in the data is large => distribution x is not a good fit to the data.
A refresher on p-values
The results of Chi-square goodness-of-fit tests are shown by Input Analyzer. The results are presented in the form of p-values; the p-value is the largest value of the Type-I error probability (rejecting the null hypothesis when it is, in fact, true) that allows the distribution to fit the data. The null hypothesis states that the distribution fits the data. The alternative hypothesis says it doesn’t. In general, the higher the p-value, the better the fit. For example, if the p-value is greater than 0.05, then we would fail to reject the null hypothesis at significance level = 0.05.