Random number from 1 to 10. Excel random number generator in functions and data analysis. Pseudo-random number generator and random number generator

Please help the service with one click: Tell your friends about the generator!

Online number generator in 1 click

The random number generator, which is presented on our website, is very convenient. For example, it can be used in sweepstakes and lotteries to determine the winner. The winners are determined in this way: the program produces one or more numbers in any range specified by you. Fraudulent results can be immediately ruled out. And thanks to this, the winner is determined by an honest choice.

Sometimes it is necessary to obtain a certain number of random numbers at once. For example, you want to fill out a “4 out of 35” lottery ticket, trusting to chance. You can check: if you toss a coin 32 times, what is the probability that 10 reverses will appear in a row (heads/tails may well be assigned the numbers 0 and 1)?

Random number online video instruction - randomizer

Our number generator is very easy to use. It does not require downloading a program to your computer - it can be used online. To get the number you need, you need to set the range of random numbers, the quantity and, if desired, the number separator and eliminate repetitions.

To generate random numbers in a specific frequency range:

• Select a range;
• Specify the number of random numbers;
• The “Number separator” function serves for the beauty and convenience of their display;
• If necessary, enable/disable repetitions using the checkbox;
• Click the "Generate" button.

As a result, you will receive random numbers in a given range. The result of the number generator can be copied or sent by e-mail. It would be best to take a screenshot or video of this generation process. Our randomizer will solve any of your problems!

We have a sequence of numbers consisting of practically independent elements that obey a given distribution. As a rule, uniform distribution.

You can generate random numbers in Excel in different ways and methods. Let's consider only the best of them.

Random Number Function in Excel

1. The RAND function returns a random, uniformly distributed real number. It will be less than 1, greater than or equal to 0.
2. The RANDBETWEEN function returns a random integer.

Let's look at their use with examples.

Sampling random numbers using RAND

This function requires no arguments (RAND()).

To generate a random real number in the range from 1 to 5, for example, use the following formula: =RAND()*(5-1)+1.

The returned random number is distributed uniformly over the interval.

Each time the worksheet is calculated or the value in any cell in the worksheet changes, a new random number is returned. If you want to save the generated population, you can replace the formula with its value.

1. Click on the cell with a random number.
2. In the formula bar, select the formula.
3. Press F9. AND ENTER.

Let's check the uniformity of the distribution of random numbers from the first sample using a distribution histogram.

The range of vertical values ​​is frequency. Horizontal - “pockets”.



RANDBETWEEN function

The syntax for the RANDBETWEEN function is (lower bound; upper bound). The first argument must be less than the second. Otherwise the function will throw an error. The boundaries are assumed to be integers. The formula discards the fractional part.

Example of using the function:

Random numbers with precision 0.1 and 0.01:

How to make a random number generator in Excel

Let's make a random number generator that generates a value from a certain range. We use a formula like: =INDEX(A1:A10,INTEGER(RAND()*10)+1).

Let's make a random number generator in the range from 0 to 100 in steps of 10.

You need to select 2 random ones from the list of text values. Using the RAND function, we compare text values ​​in the range A1:A7 with random numbers.

Let's use the INDEX function to select two random text values ​​from the original list.

To select one random value from the list, use the following formula: =INDEX(A1:A7,RANDBETWEEN(1,COUNT(A1:A7))).

Normal distribution random number generator

The RAND and RANDBETWEEN functions produce random numbers with a uniform distribution. Any value with the same probability can fall into the lower limit of the requested range and into the upper one. This results in a huge spread from the target value.

A normal distribution implies that most of the generated numbers are close to the target number. Let's adjust the RANDBETWEEN formula and create a data array with a normal distribution.

The cost of product X is 100 rubles. The entire batch produced follows a normal distribution. A random variable also follows a normal probability distribution.

Under such conditions, the average value of the range is 100 rubles. Let's generate an array and build a graph with a normal distribution with a standard deviation of 1.5 rubles.

We use the function: =NORMINV(RAND();100;1.5).

Excel calculated which values ​​were within the probability range. Since the probability of producing a product with a cost of 100 rubles is maximum, the formula shows values ​​close to 100 more often than others.

Let's move on to plotting the graph. First you need to create a table with categories. To do this, we divide the array into periods:

Based on the data obtained, we can generate a diagram with a normal distribution. The value axis is the number of variables in the interval, the category axis is periods.

Numbers surround us from birth and play an important role in life. For many people, their work itself is connected with numbers; some rely on luck, filling out lottery tickets with numbers, while others attach even mystical meaning to them. One way or another, sometimes we cannot do without using a program such as random number generator.

For example, you need to organize a prize draw among your group’s subscribers. Our online random number generator will help you quickly and honestly select winners. You just need, for example, to set the required number of random numbers (based on the number of winners) and the maximum range (based on the number of participants, if numbers are assigned to them). Fraud in this case is completely excluded.

This program can also serve as a random number generator for lotto. For example, you bought a ticket and want to rely entirely on chance and luck in choosing the numbers. Then our number randomizer will help you fill out your lottery ticket.

How to generate a random number: instructions

Random number program It works very simply. You don't even need to download it to your computer - everything is done in the browser window where this page is open. Random numbers are generated in accordance with the specified number of numbers and their range - from 0 to 999999999.

To generate a number online, you need to:

1. Select the range in which you want the result. Perhaps you want to cut off numbers up to 10 or, say, 10,000;
2. Eliminate repetitions - by selecting this option, you will force the **number randomizer** to offer you only unique combinations within a certain range;
3. Select the number of numbers – from 1 to 99999;
4. Click the “Generate numbers” button.

No matter how many numbers you want to get as a result, the prime number generator will produce the entire result at once and you can see it on this page by scrolling through the field with numbers using the mouse or touchpad.

Now you can use the ready-made numbers the way you need. From the number field, you can copy the result to publish in a group or send by mail. And so that the result does not raise any doubts, take a screenshot of this page, on which the parameters of the number randomizer and the results of the program will be clearly visible. It is impossible to change the numbers in the field, so the possibility of manipulation is excluded. We hope our website and random number generator helped you.

Have you ever wondered how Math.random() works? What is a random number and how is it obtained? Imagine an interview question - write your random number generator in a couple of lines of code. So, what is it, an accident and is it possible to predict it?

I am very fascinated by various IT puzzles and tasks, and the random number generator is one of these tasks. Usually in my Telegram channel I analyze all sorts of puzzles and various tasks from interviews. The random number generator problem has gained great popularity and I wanted to perpetuate it in the depths of one of the authoritative sources of information - that is, here on Habré.

This material will be useful to all those front-end and Node.js developers who are on the cutting edge of technology and want to get into a blockchain project/startup, where even front-end developers are asked questions about security and cryptography, at least at a basic level.

Pseudo-random number generator and random number generator

In order to get something random, we need a source of entropy, a source of some chaos from which we will use to generate randomness.

This source is used to accumulate entropy and then obtain from it an initial value (seed), which is necessary for random number generators (RNG) to generate random numbers.

The Pseudo-Random Number Generator uses a single seed, hence its pseudo-randomness, while the Random Number Generator always generates a random number by starting with a high-quality random variable that is drawn from various sources of entropy.

Entropy is a measure of disorder. Information entropy is a measure of the uncertainty or unpredictability of information.

It turns out that in order to create a pseudo-random sequence we need an algorithm that will generate a certain sequence based on a certain formula. But such a sequence can be predicted. However, let's imagine how we could write our own random number generator if we didn't have Math.random()

PRNG has some algorithm that can be reproduced.
RNG is the process of obtaining numbers entirely from some kind of noise, the ability to calculate which tends to zero. At the same time, the RNG has certain algorithms for equalizing the distribution.

We come up with our own PRNG algorithm

Pseudorandom number generator (PRNG) is an algorithm that generates a sequence of numbers whose elements are almost independent of each other and obey a given distribution (usually uniform).

We can take a sequence of some numbers and take the modulus of the number from them. The simplest example that comes to mind. We need to think about which sequence to take and the module from what. If you just directly from 0 to N and modulus 2, you get a generator of 1 and 0:

Function* rand() ( const n = 100; const mod = 2; let i = 0; while (true) ( ​​yield i % mod; if (i++ > n) i = 0; ) ) let i = 0; for (let x of rand()) ( if (i++ > 100) break; console.log(x); )
This function generates the sequence 01010101010101... and it cannot even be called pseudo-random. For a generator to be random, it must pass the next bit test. But we don’t have such a task. Nevertheless, even without any tests we can predict the next sequence, which means that such an algorithm is not suitable, but we are in the right direction.

What if we take some well-known but non-linear sequence, for example the number PI. And as the value for the module we will take not 2, but something else. You can even think about the changing value of the module. The sequence of digits in Pi is considered random. The generator can operate using Pi numbers starting from some unknown point. An example of such an algorithm, with a PI-based sequence and a variable module:

Const vector = [...Math.PI.toFixed(48).replace(".","")]; function* rand() ( for (let i=3; i<1000; i++) { if (i >99) i = 2; for (let n=0; n But in JS, the PI number can only be displayed up to 48 digits and no more. Therefore, it is still easy to predict such a sequence, and each run of such a generator will always produce the same numbers. But our generator has already started showing numbers from 0 to 9.

We got a generator of numbers from 0 to 9, but the distribution is very uneven and it will generate the same sequence every time.

We can take not the number Pi, but time in numerical representation and consider this number as a sequence of numbers, and in order to ensure that the sequence does not repeat each time, we will read it from the end. In total, our algorithm for our PRNG will look like this:

Function* rand() ( let newNumVector = () => [...(+new Date)+""].reverse(); let vector = newNumVector(); let i=2; while (true) ( ​​if ( i++ > 99) i = 2; let n=-1; while (++n< vector.length) yield (vector[n] % i); vector = newNumVector(); } } // TEST: let i = 0; for (let x of rand()) { if (i++ >100) break; console.log(x)
This already looks like a pseudo-random number generator. And the same Math.random() is a PRNG, we’ll talk about it a little later. Moreover, each time we get a different first number.

Actually, using these simple examples you can understand how more complex random number generators work. And there are even ready-made algorithms. As an example, let’s look at one of them — this is the Linear Congruent PRNG (LCPRNG).

Linear congruent PRNG

Linear congruent PRNG (LCPRNG) is a common method for generating pseudorandom numbers. It is not cryptographically strong. This method consists of calculating the terms of a linear recurrent sequence modulo some natural number m, given by the formula. The resulting sequence depends on the choice of starting number — i.e. seed. With different seed values, different sequences of random numbers are obtained. An example of implementing such an algorithm in JavaScript:

Const a = 45; const c = 21; const m = 67; var seed = 2; const rand = () => seed = (a * seed + c) % m; for(let i=0; i<30; i++) console.log(rand())
Many programming languages ​​use LCPRNG (but not exactly this algorithm(!)).

As mentioned above, such a sequence can be predicted. So why do we need PRNG? If we talk about security, then PRNG is a problem. If we talk about other tasks, then these properties can be a plus. For example, for various special effects and graphics animations, you may need to frequently call random. And this is where the distribution of meanings and performance are important! Secure algorithms cannot boast of speed.

Another property is reproducibility. Some implementations allow you to specify a seed, and this is very useful if the sequence must be repeated. Reproduction is needed in tests, for example. And there are many other things that do not require a secure RNG.

How Math.random() works

The Math.random() method returns a pseudo-random floating point number from the range = crypto.getRandomValues(new Uint8Array(1)); console.log(rvalue)
But, unlike the Math.random() PRNG, this method is very resource-intensive. The fact is that this generator uses system calls in the OS to gain access to entropy sources (mac address, CPU, temperature, etc...).