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Random Numbers Generator and Statistics Set
Random Numbers Generator and Statistics Set provides 12 random numbers generators that allow
you to generate histogram from the probability distribution given the parameters you have specified.
You also have the option to output random numbers from the distribution on the spreadsheet.
| Software Information |
System Requirements |
| Version: | New |
- Windows 8/7/XP/Vista
- Pentium or similar processor
- 512 MB RAM
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| File Size: | 611 KB |
| License: | Free to try, $29.95 to buy |
| Screenshot: | View Screenshot |
| Rating: |  |
Do you want to see how random numbers from different probability distribution are generated?
The Random Numbers Generator and Statistics Set can show you how.
It contains practical and well explained examples of:
- Random Number Generator - Normal Distribution
The normal distribution is the most commonly used probability distribution in statistics.
Many other probability distributions are related to this distribution. As the number of
random variables increases, the distribution becomes a bell shaped curve. This curve is
called the normal curve or Gaussian curve (in honor of the German mathematician Karl Friedrick
Gauss, 1777-1855). The normal distribution is defined with mean and standard deviation.
- Random Number Generator - Log Normal Distribution
The log-normal distribution is often assumed to be the distribution of a stock price.
A distribution is log-normally distributed when the natural log of the set of the random variables
in that distribution is a normally distributed. In plain English, if you take the natural log of
each of the random numbers from a log-normal distribution, the new number set will be normally distribution.
Like the normal distribution, log-normal distribtuion is also defined with mean and standard deviation.
- Random Number Generator - Chi-Square Distribution
The most common use of the chi-square distribution is to test the difference between proportions. It has a
positive skew. The skew decreases when degree of freedom increases as the distribution approaches normal.
The mean of a chi-square distribution is its degree of freedom.
- Random Number Generator - F-Distribution
The F distribution is commonly used for ANOVA (analysis of variance), to test whether the variances
of two or more populations are equal. For every F deviate, there are two degrees of freedom, one in
the numerator and one in the denominator. It is the ratio of the dispersions of the two Chi-Square distributions.
As both of the degree of freedom increase, the percentile value is approaching to one. F is also used in tests of
explained variance and is referred to as the variance ration, Explained variance/Unexplained variance.
- Random Number Generator - Student-T Distribution
Student T distribution is used commonly for small sample size -usually a sample size less than 30.
A t distribution shares some common characteristics with the standard normal distribution.
Both distributions are symmetrical, both range in value from negative infinity to positive infinity,
and both have a mean of zero and standard derivation of one. However, a t distribution has a greater
dispersion than the standard normal distribution.
- Random Number Generator - Log Pearson Type III Distribution
The Log Pearson Type III distribution is commonly used in hydraulic studies. It is somehow
similar to normal distribution, except instead of two parameters, stanand deviation and mean,
it also has skew. When the skew is small, Log Pearson Type III distribution approximates normal.
- Random Number Generator - Multivariate Standard Normal Distribution
This program is a derivation of the Multivariate Standard Normal Probability Distribution example.
Users will be able to populate random multivariate standard normal deviates on the spreadsheet for analysis.
For detail on this distribution, please refer to the Multivariate Standard Normal Probability Distribution example.
- Random Number Generator - Gamma Distribution
The Gamma distribution is most often used to describe the distribution of the amount of time until
the nth occurrence of an event in a Poisson process. For example, customer service or machine repair.
The Gamma distribution is related to many other distributions. For example, when a Gamma distribution
has an alpha of 1, Gamma(1, b), it becomes an Exponential distribution with scale parameter of b, Expo(b).
And a Chi-Square distribution with k df is the same as the Gamma(k/2,2) distribution.
- Random Number Generator - Beta Distribution
The Beta distribution can be used in the absence of data. Possible applications are estimate the
proportion of defective items in a shipment or time to complete a task. The Beta distribution has
two shape parameters, a1 and a2. When the two parameters are equal, the distribution is symmetrical.
- Random Number Generator - Hypergeometirc Distribution
The Hypergeometric distribution is a discrete distribution. It is alike the Binomial distribution.
Both of the Hypergeometric distribution and the Binomial distribution describe the number of times
an event happens in a fixed number of trials. The difference between the two distributions is that
Binomial distribution trials are independent, while Hypergeometric distribution trials change the
probability for each subsequent trial and are called sampling without replacement.
- Random Number Generator - Triangular Distribution
The Triangular distribution is often used when no or little data is available. It has 3 parameters,
the minimum and the maximum that defines the range, and the more likely (the peak). The distribution
is skewed to the left when the peak is closed to the minimum and to the right when the peak is closed
to the maximum. It is a simple distribution that as its name implied, has a triangular shape.
- Random Number Generator - Binomial Distribution
The Binomial distribution describes the number of successes in t independent Bernoulli (yes or no)
trails with probability p of success on each trial. It is used to answer questions such as how many
times a head will come up when a coin is flipped 5 times or how many defective items will be found in 20 items.
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