Curved to Rectangular Distribution

In summary, the conversation discusses the topic of converting numeric sequences with a curved distribution to a rectangular (uniform) distribution of random numbers. The narrator mentions that this can be useful for selecting lottery numbers and suggests studying modeling and simulation for more information on this topic. They also mention that a high school level education is sufficient for understanding this concept.
  • #1
X_Art_X
15
0
Hi Guys :)
I have become interested in producing true random numbers of uniform distribution,
and have come across this Numberphile video in my travels:


The narration hints at the topic of converting numeric sequences of a curved distribution,
inherently more likely to be closer to a mean peak, than the outer edges of the curve,
to a rectangular (uniform) distribution of random numbers
(which occurs to me more useful if you were selecting lottery numbers or something like that.
The narrator avoids the topic of the math to do the job, presumably too difficult or too boring for the video.

Would anyone lead me to something to study on that topic?
Considering I’m a hobbyist with senior high school level education, the simpler the better.
I presume this is an intermediate question as I am still familiar with high school math.
Thanks, Art.
 
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  • #2
I would recommend picking up a book on modeling and simulation. These books normally cover the different transformations between distributions so that you can simulate a variety of distributions from uniform random number generators.
 

Related to Curved to Rectangular Distribution

1. What is a Curved to Rectangular Distribution?

A Curved to Rectangular Distribution is a type of probability distribution where the data follows a curve, but can be transformed into a rectangular shape through a mathematical process. This distribution is useful for analyzing data that is initially non-normal, but can be transformed to fit a normal distribution.

2. How is a Curved to Rectangular Distribution created?

A Curved to Rectangular Distribution is created by taking the inverse of a cumulative distribution function (CDF) for the data. This transforms the data into a new distribution that appears rectangular in shape. The CDF is a function that maps the probability of a random variable being less than or equal to a certain value.

3. What are the advantages of using a Curved to Rectangular Distribution?

One advantage of using a Curved to Rectangular Distribution is that it allows for easier analysis of non-normal data. It also allows for the use of more traditional statistical methods, such as calculating mean and standard deviation, which are not appropriate for non-normal data. Additionally, this distribution can provide a better fit for data that is not normally distributed.

4. How is a Curved to Rectangular Distribution different from other types of distributions?

A Curved to Rectangular Distribution is different from other distributions because it is not a fixed distribution, but rather a transformation of a distribution. It is also unique in that it is able to transform non-normal data into a more normal distribution, while other distributions are limited to specific shapes or characteristics.

5. In what situations is a Curved to Rectangular Distribution useful?

A Curved to Rectangular Distribution is useful in situations where the data is initially non-normal, but the researcher wants to use traditional statistical methods that assume normality. It is also useful for analyzing data that is skewed or has outliers, as it can provide a better fit for these types of data. This distribution is commonly used in fields such as economics, finance, and engineering.

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