Art Analysis Quiz

Study Notes

Introduction: The ability to define future possibilities and choose among alternatives is crucial in modern society. Knowledge of systems often involves uncertainty, and decisions made based on certainty can have serious consequences. Simulation helps bridge this gap by allowing examination of the outcomes of various options.
Intended Learning Outcomes (ILOs): Define a model and modelling, explain when and why we use models, describe the modelling process, and describe different types of models.
Main Content:
- Definitions: Models are abstract representations of real or imaginary systems.
- Modelling and Simulation: A discipline for understanding system interactions and overall behavior. A powerful tool for evaluating and comparing various options and strategies. Particularly valuable when significant uncertainty exists, complex interactions are present, or testing options directly is impractical/costly.

Introduction: Random numbers are fundamental to modelling and simulation, as many systems involve probabilistic elements.
Intended Learning Outcomes (ILOs): Describe how to generate pseudorandom numbers, Use QBasic RND function and describe how to simulate randomness, Use different Random number generators, Explain properties of good random number generator.
Main Content:
- Random Numbers: Numbers that show no discernible patterns, not predictable from preceding numbers.
- Pseudorandom Number Generators: Methods of generating numbers with random-like properties but determined by algorithms for simulations. Quality depends on period (number of unique numbers generated before repetition).

Introduction: Understanding the methods used to create random or pseudo-random number sequences.
Main Content:
- Methods: Congruential generators (using a multiplicative and addition algorithm on a seed). Alternative approaches, such as shift-register and lagged-Fibonacci methods in conjunction with seed values for improved randomness.

Introduction: A class of computational algorithms using random sampling to estimate results.
Intended Learning Outcomes (ILOs): Describe Monte Carlo method, Trace the origin of Monte Carlo method, Give examples of the application of Monte Carlo method.
Main Content:
- Overview: Using random numbers and probability to estimate results across various scenarios. The ratio of the area of a circle to the area of the square that fully surrounds will approximate π
- History: Originating from probabilistic methods used for games of chance.
- Applications: Widely used in physics, finance, and engineering to analyze processes with uncertainty

Introduction: Statistical distributions are important for understanding how data are spread out and are used when working with probability.
Intended Learning Outcomes (ILOs): Define statistics, Explain statistical distributions, Compute measures of central tendency and variations, Explain the Components of Statistical Distributions.
Main Content:
- Variables: Measures of central tendency (mean, median, mode) and measures of variation (range, standard deviation).
- Distributions: Differences of discrete (finite number of possible values) and continuous (infinite number of possible values) distributions displayed in various shapes. Examples of normal, skewed distributions, percentiles, and transformations to standard curves.