WorksheetsModeling and Simulation - Midterm Exam
Total questions: 80
Worksheet time: 27mins
Name
Class
Date
1.
What is a model in the context of simulation?
a)
A simplified representation of a system
b)
The actual system itself
c)
A random data generator
d)
A real-world experiment
2.
Simulation is best described as:
a)
Running experiments on a real system
b)
Manipulating a mathematical model to imitate a real process
c)
A form of data collection
d)
An optimization algorithm
3.
Which of the following is NOT a reason for using simulation?
a)
Cost of real experimentation
b)
Safety concerns
c)
Exact analytical solution is easy to obtain
d)
Time constraints
4.
The process of creating a model that represents a system is called:
a)
Simulation
b)
Modeling
c)
Sampling
d)
Validation
5.
In a simulation study, input data are usually:
a)
Deterministic
b)
Random variables
c)
Constant
d)
Ignored
6.
The first step in simulation modeling is:
a)
Data collection
b)
Problem definition
c)
Model validation
d)
Implementation
7.
Which of the following is an advantage of simulation?
a)
Provides exact analytical results
b)
Handles complex systems
c)
Requires no data
d)
Always faster than real time
8.
Which type of model uses equations to describe relationships?
a)
Physical model
b)
Mathematical model
c)
Iconic model
d)
Descriptive model
9.
Which is NOT a component of a simulation model?
a)
Input variables
b)
Output variables
c)
Random number generator
d)
Database schema
10.
Verification in simulation refers to:
a)
Ensuring the model represents the real system accurately
b)
Checking that the model is implemented correctly
c)
Comparing simulated results with theory
d)
Collecting data
11.
The process of comparing model output with real-world data is called:
a)
Verification
b)
Validation
c)
Randomization
d)
Optimization
12.
A deterministic model is one that:
a)
Has random variables
b)
Produces the same output for a given input
c)
Uses probability distributions
d)
Is unpredictable
13.
A stochastic model differs from a deterministic model in that it:
a)
Is easier to compute
b)
Includes randomness
c)
Uses no variables
d)
Has no parameters
14.
In a simulation, the system state represents:
a)
Random numbers
b)
Set of variables describing the system at a time
c)
Output data
d)
Simulation clock
15.
The simulation clock keeps track of:
a)
Real-world time
b)
Elapsed simulated time
c)
CPU time
d)
Model iterations
16.
Continuous simulation models are used when:
a)
Events occur at discrete times
b)
State variables change continuously
c)
Randomness dominates
d)
There are no equations
17.
Discrete-event simulation models:
a)
Change continuously over time
b)
Change only at specific events
c)
Have no time element
d)
Are purely theoretical
18.
Which of the following is an example of a physical model?
a)
A mathematical equation
b)
A scale model of a car
c)
A simulation program
d)
A probability curve
19.
A conceptual model is:
a)
A set of equations
b)
A high-level description of the system
c)
The code of the simulation
d)
A data table
20.
The main purpose of random number generation in simulation is:
a)
To generate deterministic data
b)
To model uncertainty
c)
To reduce computation
d)
To fix model parameters
21.
A pseudo-random number is:
a)
Completely unpredictable
b)
Generated by a deterministic algorithm
c)
Drawn from real-life data
d)
Generated from physical noise
22.
The seed in random number generation is used to:
a)
Change probability
b)
Produce the same sequence again
c)
Avoid repetition
d)
Increase randomness
23.
The Linear Congruential Generator (LCG) is commonly used for:
a)
Optimization
b)
Random number generation
c)
Data sampling
d)
Regression modeling
24.
The modulus (m) in LCG determines:
a)
Period length
b)
Output mean
c)
Variance
d)
Probability
25.
Good random numbers should be:
a)
Predictable
b)
Correlated
c)
Uniformly distributed
d)
Dependent
26.
Probability distributions in simulation are used to:
a)
Assign random outcomes to model inputs
b)
Replace validation
c)
Fix constants
d)
Simplify equations
27.
A uniform distribution has:
a)
Equal probability for all values in a range
b)
One peak value
c)
Many clusters
d)
No mean
28.
A normal distribution is also known as:
a)
Gaussian distribution
b)
Uniform distribution
c)
Exponential distribution
d)
Poisson distribution
29.
The mean of a distribution represents:
a)
Center or average value
b)
Variation
c)
Randomness
d)
Skewness
30.
A Poisson distribution is typically used for:
a)
Counting number of events in a time interval
b)
Continuous measurements
c)
Random sampling
d)
Normalized data
31.
In a mathematical model, parameters are:
a)
Variables that change
b)
Fixed values describing system characteristics
c)
Random errors
d)
Simulation outputs
32.
A statistical model is based on:
a)
Empirical data and probability
b)
Physical measurements
c)
Deterministic rules
d)
Theoretical constants
33.
Regression models are examples of:
a)
Simulation models
b)
Statistical models
c)
Iconic models
d)
Conceptual models
34.
Which of the following is an example of a linear model?
a)
y = mx + b
b)
y = ax² + bx + c
c)
y = log(x)
d)
y = sin(x)
35.
Which type of model involves both mathematical equations and random variables?
a)
Deterministic
b)
Stochastic
c)
Static
d)
Physical
36.
A population growth model attempts to describe:
a)
Birth and death rates in a system
b)
Random sampling
c)
Motion of particles
d)
Market equilibrium
37.
The exponential growth model assumes:
a)
Constant birth and death rate difference
b)
Limited resources
c)
Random fluctuations
d)
Constant carrying capacity
38.
The logistic growth model adds:
a)
A random factor
b)
A carrying capacity limit
c)
Negative time
d)
A decay constant
39.
When population exceeds carrying capacity, the logistic model predicts:
a)
Unlimited growth
b)
Decline or stabilization
c)
Constant increase
d)
Random output
40.
In the logistic growth equation, K represents:
a)
Growth rate
b)
Carrying capacity
c)
Birth rate
d)
Time constant
41.
Population models are often used in:
a)
Ecology and resource planning
b)
Cryptography
c)
Random number generation
d)
Queue simulation
42.
A static model does not include:
a)
Random variables
b)
Time as a variable
c)
Equations
d)
Parameters
43.
A dynamic model is one that:
a)
Changes over time
b)
Is purely mathematical
c)
Ignores randomness
d)
Has no feedback
44.
The term Monte Carlo simulation refers to:
a)
Analytical method
b)
Simulation using random sampling
c)
Optimization model
d)
Deterministic algorithm
45.
Which is a limitation of simulation?
a)
Handles complex systems
b)
Provides approximate results
c)
Models uncertainty
d)
Tests hypotheses
46.
The process of ensuring input data are accurate is called:
a)
Data validation
b)
Verification
c)
Randomization
d)
Calibration
47.
Which step comes before implementation in simulation study?
a)
Verification
b)
Documentation
c)
Validation
d)
Analysis
48.
In simulation, replications are used to:
a)
Repeat experiments to estimate variability
b)
Fix random seed
c)
Speed up computation
d)
Reduce randomness
49.
The result of a simulation experiment is usually:
a)
A single number
b)
A statistical summary
c)
A constant
d)
A deterministic output
50.
Which of the following best defines model abstraction?
a)
Simplifying reality while retaining essential features
b)
Ignoring all real-world data
c)
Focusing on implementation
d)
Maximizing complexity
51.
A scaled-down replica of an airplane used for wind tunnel testing.
a)
Physical Model
b)
Mathematical Model
c)
Conceptual Model
52.
A flowchart showing the steps of an enrollment process in a university.
a)
Physical Model
b)
Mathematical Model
c)
Conceptual Model
53.
An equation describing the spread of a disease in a population.
a)
Physical Model
b)
Mathematical Model
c)
Conceptual Model
54.
A 3D printed model of a new bridge design.
a)
Physical Model
b)
Mathematical Model
c)
Conceptual Model
55.
A diagram showing how different departments in a company interact.
a)
Physical Model
b)
Mathematical Model
c)
Conceptual Model
56.
The formula for calculating the area of a circle.
a)
Physical Model
b)
Mathematical Model
c)
Conceptual Model
57.
A miniature model of a solar system used in a science class.
a)
Physical Model
b)
Mathematical Model
c)
Conceptual Model
58.
A mind map illustrating factors affecting employee performance.
a)
Physical Model
b)
Mathematical Model
c)
Conceptual Model
59.
A regression model predicting sales based on advertising spend.
a)
Physical Model
b)
Mathematical Model
c)
Conceptual Model
60.
A scaled physical model of a proposed condominium project.
a)
Physical Model
b)
Mathematical Model
c)
Conceptual Model
61.
A pie chart showing the market share of smartphone brands in 2024.
a)
Static Model
b)
Dynamic Model
62.
A simulation of traffic flow in Metro Manila throughout the day.
a)
Static Model
b)
Dynamic Model
63.
A company’s organizational chart.
a)
Static Model
b)
Dynamic Model
64.
A weather forecast model predicting rainfall for the next week.
a)
Static Model
b)
Dynamic Model
65.
A Gantt chart showing the project schedule at one specific date.
a)
Static Model
b)
Dynamic Model
66.
A population growth model projecting changes over 10 years.
a)
Static Model
b)
Dynamic Model
67.
A seating plan of students in a classroom.
a)
Static Model
b)
Dynamic Model
68.
An economic model showing inflation trends month by month.
a)
Static Model
b)
Dynamic Model
69.
A map of the Philippines with current provinces.
a)
Static Model
b)
Dynamic Model
70.
A simulation of a rocket launch trajectory.
a)
Static Model
b)
Dynamic Model
71.
Number of cars arriving at a toll gate per hour.
a)
Discrete Model
b)
Continuous Model
72.
The amount of fuel decreasing in a tank while driving.
a)
Discrete Model
b)
Continuous Model
73.
The number of students entering a classroom.
a)
Discrete Model
b)
Continuous Model
74.
The change in water level in a dam over time.
a)
Discrete Model
b)
Continuous Model
75.
The number of emails received per day.
a)
Discrete Model
b)
Continuous Model
76.
The growth of a tree’s height over years.
a)
Discrete Model
b)
Continuous Model
77.
The number of patients arriving at a hospital emergency room.
a)
Discrete Model
b)
Continuous Model
78.
The temperature of a room throughout the day.
a)
Discrete Model
b)
Continuous Model
79.
The number of calls made to a call center per minute.
a)
Discrete Model
b)
Continuous Model
80.
The speed of a moving car on a highway.
a)
Discrete Model
b)
Continuous Model
100 %
