Linear Congruential Generator (LCG)

A linear congruential generator (LCG) is an old algorithm that results in a sequence of pseudo-randomized numbers. Though, the algorithm of linear congruential generator is the oldest but best-known pseudorandom number generator method.

The building block of a simulation study is the ability to generate random numbers where a random number represents the value of a random variable uniformly distributed on (0,1).

The recurrence relation defines the generator:

\[X_{i+1}=(aX_i+C) \text{ Modulo } m\]

where $a$ and $m$ are given positive integers, $X_i$ is either $0,1, \dots, m-1$ and quantity $\frac{X_i}{m}$ is pseudo random number.

Conditions for Linear Congruential Generator

Some conditions are:

  1. $m>0$;  $m$ is usually large
  2. $0<a<m$;  ($a$ is the multiplier)
  3. $0\le c<m$ ($c$ is the increment)
  4. $0\le X_0 <m$ ($X_0$ is seed value or starting value)
  5. $c$ and $m$ are relatively prime numbers (there is no common factor between $c$ and $m$).
  6. $a-1$ is a multiple of every prime factor $m$
  7. $a-1$ is multiple of 4 if $m$ is multiple of 4
Linear Congruential Generator
Source: https://en.wikipedia.org/wiki/Linear_congruential_generator

“Two modulo-9 LCGs show how different parameters lead to different cycle lengths. Each row shows the state evolving until it repeats. The top row shows a generator with $m=9, a=2, c=0$, and a seed of 1, which produces a cycle of length 6. The second row is the same generator with a seed of 3, which produces a cycle of length 2. Using $a=4$ and $c=1$ (bottom row) gives a cycle length of 9 with any seed in [0, 8]. “

If  $c=0$, the generator is often called a multiplicative congruential method, or Lehmer RNG. If $c\neq0$ the generator is called a mixed congruential generator.

FAQs about linear congruential generator (LCG)

  1. What is meant by a linear congruential generator?
  2. How are random numbers generated?
  3. What are the conditions for linear congruential generators?
  4. What is meant by a multiplicative or mixed congruential generator?
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Statistical Simulation: Introduction and Issues (2012)

In this article, you will learn about statistical simulation introduction, use in various fields, and issues.

Simulation is used before an existing system is altered or a new system is built, to reduce the chances of failure to meet specifications, eliminate unforeseen bottlenecks, prevent under or over-utilization of resources, and optimize system performance. Simulation is used in many contexts, such as simulation of technology for performance optimization, safety engineering, training testing, education, and video games. Often, computer experiments are used to study simulation models. Models are simulated versions/results.

Uses of Statistical Simulations

Statistical simulations are widely used in many fields:

  • Science: Scientists use statistical simulations to model complex systems, such as the climate or the spread of disease.
  • Business: Businesses use statistical simulations to forecast sales, evaluate the risks of new investments, and design logistics networks.
  • Government: Governments use simulations to model the effects of economic policies, assess the risks of natural disasters, and plan for future events.
  • Gambling: Casinos use simulations to design games that are fair and profitable.

Statistical Simulation depends on unknown (or external/ impositions/ factors) parameters and statistical tools depend on estimates. In statistics, simulation is used to assess the performance of a method, typically when there is a lack of theoretical background. With simulations, the statistician knows and controls the truth.

Monte Carlo Simulation Application: Statistical Simulations

Statistical Assumptions about Simulated Data

In simulation, data is generated artificially to test out a hypothesis or statistical method. Whenever a new statistical method is developed (or used), some assumptions need to be tested and verified (or confirmed). Statisticians use simulated data to test these assumptions.

  • The simulation follows finite sample properties (have to specify $n$)
  • The reasoning of statistical simulation can’t be proofed mathematically)
  • Simulation is used to illustrate things.
  • Simulation is used to check the validity of methods.
  • Simulation is a technique of representing the real world via a computer program.
  • A simulation is an act of initiating the behavior of some situation or some process utilizing something suitably analogous. (especially for study or some personal training)
  • A simulation is a representation of something (usually on a smaller scale).
  • Simulation is the act of giving a false/artificial appearance.

In summary, statistical simulation is a technique used to imitate the behavior of a system or process under various conditions. It involves creating a computer model of the system and running the model repeatedly with different inputs. The outputs of the model are then analyzed to learn about the behavior of the real system.

Statistical Simulation

Issues In Statistical Simulation

  • What distribution does the random variable have?
  • How do we generate these random variables for simulation?
  • How do we analyze the output of simulations?
  • How many simulation runs do we need?
  • How do we improve the efficiency of the simulation?

FAQS about Statistical Simulations

  1. What is meant by simulation in statistics?
  2. What random data is generated using simulation?
  3. What are the uses of simulations?
  4. What are the issues in Statistical simulations?
  5. What are statistical assumptions about generated data?

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Matlab as a Calculator

MATLAB stands for “Matrix Laboratory” and is an interactive, matrix-based system and fourth-generation programming language from Mathworks Inc., which is mathematics software. Matlab helps to perform statistical analysis and gives the user complete freedom to implement specific algorithms and perform complex custom-tailored operations.

Matlab has a command-driven approach. Commands with appropriate arguments are written after the Matlab command prompt >>. The Matlab program provides the user with a convenient environment for performing many types of calculations. This introduction to Matlab will help users understand its importance and variety of applications in different scientific fields.

Matlab as a Calculator

Matlab has three primary windows.

1) Command windows
2) Graphics Windows
3) Edit Windows used to write M-Files

The common way to operate Matlab is to enter commands in the command window.

Matlab as a Calculator

>> 55 – 16
ans = 39
>> ans + 11
ans =50

Matlab assigns the results ans whenever you do not explicitly assign the calculations to a chosen variable.

>> a = 4                   % assigns a scalar quantity to a
>> a                         % Prints the scalar quantity in command windows
>> a = 4                   % suppressed echo printing
>> a =4; A=6; x=1;  % multiple variable definition

Note: Matlab treats names as Case-Sensitive.

>> format long
>> pi
>> format short
>> pi
Matlab as Calculator

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Measures of Central Tendency: A Comprehensive Guide

Question: What is a measure of central tendency and what are the common measures of central tendency? Also, when is the median preferred over the mean?

A measure of central tendency is the single numerical value considered most typical of the values of a quantitative variable.

The most common measure of central tendency is the mode (i.e., the most frequently occurring number)

The median (i.e., the middle point or fiftieth percentile), and the mean (i.e., the arithmetic average).

The median is preferred over the mean when the numbers are highly skewed (i.e., non-normally distributed).

Measures of Central Tendency

Since, measures of central tendency condense a bunch of information into a single, digestible value that represents the center of the data, this makes measures of central tendencies important for several reasons:

  • Summarizing data: Instead of listing every data point, one can use a central tendency measure to get a quick idea of what is typical in the data set.
  • Comparisons: By computing central tendency measures for different groups or datasets, one can easily compare them to see if there are any differences.
  • Decision making: Central tendency measures can help to make wise decisions. For instance, knowing the average income in an area can help set prices. Imagine an organization is analyzing customer purchases. Knowing the average amount spent can help them tailor promotions or target specific customer groups.
  • Identifying trends: Measures of central tendencies may help in observing the trend over time. This can be done by using different visualizations to see if there are any trends, like a rise in average house prices.

However, it is very important to understand these Measures of Central Tendency (mean, median, mode). Each measure of central tendency has its strengths and weaknesses. Choosing the right measure of central tendency depends on the kind of data and what one’s interest is to extract from and try to understand.

Statistics Help measures of central tendency

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