The following is simply a conveniently formatted list of learning goals. More likely than not, some might be hard to assess through a quiz or exam. Students should prioritize their studying based on:

- how much time in lecture was spent discussing or understanding the topic,
- how much the topic was stressed in learning group assignments
- and the student's comfort level with the topic

# Assessable Learning Goals covered for Quiz 2

Know **Fisher-Yates** shuffling algorithm, be able to apply it to sampling tasks in simulations.

Know the **reservoir sampling** algorithm for selecting *n* random elements from an unkown sized population.

Know the several different properties and attributes of sampling and shuffling algorithms presented in the text.

Know how to use *accept/reject* techniques for uniformly random (geometric) point generation.

Know how to write Monte Carlo simulations for estimating a discrete data histogram of many events.

Know how to write Monte Carlo simulations for estimating the *Pr(A)* of an event A.

Know the pitfalls associated with common (naive) methods of random point generation.

Know the tell-tale feature(s) of spatial plots produced by faulty point generation algorithms.

Describe *S*, the system state. What does "system" refer to?

How is the **simulation clock** different than the **wall clock** or an accelerated, virtual clock?

Understand the basic steps in augmenting a pre-existing NES with additional **system** and **meta** events.

What constraint(s) are placed on events being inserted into the event list?

What is the difference between **system events** and **meta events**?

Cite pros and cons for at least two "pure" event list data structures commonly used for event list management.

Cite pros and cons for at least two **hybrid** data structures commonly used for event list management.

Understand that skewed results are obtained from SIS simulations (w/ delivery delays) with **syncronized** demand and delay random sequences.

What is a distribution's `idf()`

?

What is special about **outliers** in the context of computer simulation results?

Know the general form of the integral equations for CRVs (average and deviation calculations).

Know what constitutes a **histogram** and how it is connected to CDFs.

How is the height of a continuous histogram bin calculated?

Know the general approach to binning continuous data, about how many bins are needed for a sample of `n`

?

How is the approach of MSOD regression lines different than the approach taken for conventional "least squares" regression lines?

What is a **mean-square orthogonal-distance** (MSOD) linear regression line?

Given Welford's discrete and integral mean and variance equations (Thms 4.1.2, 4.1.4), be able to apply them to a set of data.

Know that **Welford's Equations** exist, why they are superior to the "one-pass" algorithm common in statistical texts.

Know which of the two (** non Welford**) standard equations for calculating

*s*or

^{2}*s*is flawed.

DES people don't need integrals and anti-differentiation when integrating. Why not?

Know that ** uniform arrival times** and

**are not the same thing.**

*uniform interarrivals*Know the **inversion** technique for non-stationary arrival times.

Know the relationships between PMF/PDF and CDF for both discrete and continuous distributions.

Know the relationships between PMF/PDF, CDF, and InvDF. How do you derive one from another? Can this always be done?

Know how to maniuplate and derive CDFs and PDFs of variables that are functions of a **Uniform(a,b)** input.

Know the *CDF search* technique for creating discrete random variates.

Know the *Exponential(mu)* random variate and the interpretation of its parameter *mu* in the context of arrival times.

Know the *F(x) inversion* technique for constructing random variates. What is the requirement on *F(x)*?

Know the three techniques for constructing a discrete random variate.

Know what the *construction technique* means for random variates (of which *summation* is one example).

What does the parameter *u* in random variates represent? Computationally, how do we get a value for *u* in code?

Know the criteria by which variates are judged: portability, efficiency, clarity, syncronicity, ...

Know the two techniques for ** truncating** pre-existing random variates.