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Hello. Would you by any chance mind answering these questions regarding true or

ID: 3178092 • Letter: H

Question

Hello. Would you by any chance mind answering these questions regarding true or false?

I will appreciate your help in advance.

Crystal ball in Excel, Simulation with Arena, and statistics will be useful for this one.

* True or False?

1. A warm-up period in a discrete event simulation is a period where the simulated system data is collected to understand system performance.

True or False?

2. The simulation of time is an integral part of most Monte Carlo simulations.

True or False?

3. The Kolmogorov-Smirnov test and paired t-test are both commonly used goodness of fit tests for testing the appropriateness of a distribution as a model of some random component in a simulation.

True or False?

4. LCGs that have full-period are always better generators to use in simulations than those that do not have full period. (LCG: Linear Congruential Generators)

True or False?

5. Covariance between two random variables always takes a value between -1 and 1.

True or False?

6. The Kolmogorov-Smirnov test is usually not applicable to discrete distributions.

True or False?

7. In Arena, the separate random number streams that are referenced by integer numbers are non-overlapping segments of one longer random number stream.

True or False?

Explanation / Answer

5. Correlation and Covariance both describe relationships between 2 variables. The Spearman Correlation is considered to be a standardized form of covariance. Here are the differences between the Spearman Correlation and Covariance Analysis: Correlation values fall within the range of -1 to +1.

6.

KS-test is usually not applicable for discrete distributions

The chi-square test is an alternative to the Anderson-Darlingand Kolmogorov-Smirnov goodness-of-fit tests. The chi-square goodness-of-fit test can be applied to discrete distributions such as the binomial and the Poisson. The Kolmogorov-Smirnov and Anderson-Darling tests are restricted to continuous distributions.