# 1. Prob. Distribution

Generate a Specific Distribution or View Information

Click the

in the**Prob. Distribution**category.**Statistics**

: Preview the code that will be output.**Code View**: Preview the data that will be output.**Data View**: Print and run the code.**Run**

#### Common

#### Discrete Probability Distribution

#### Continuous Probability Distribution

Uniform

## Common

### Action

#### Generate Random Numbers

: Set the number of samples you want to draw from the distribution you want to generate.**Size**: Set your seed.**Random State**: Specify the variable (call name).**Allocate to**: If checked, visualize the distribution of the extracted samples.**Show Sampled Distribution**

#### Show Distribution Plot

: Outputs the probability**Probability Density Function**: Outputs the cumulative distribution function.**Cumulative Distribution Function**

#### Statistics to P-Value

: You will get the probability of getting a value from this distribution that is greater than or equal to the absolute value entered here.**Statistics**:**Alternative****Two-sided***for a*two-tailed test,for**One-sided***a*one-tailed test.

#### P-Value to Statistics

: Enter a p-value; the range of the distribution with values greater than or equal to the entered p-value is calculated and displayed.**Proportional value**: Choose between a two-tailed or one-tailed test.**Alternative**

## Discrete Probability Distribution

### Bernoulli

: Enter the probability of getting 1 out of a binary outcome of 0 and 1.**P**: You can add options other than those provided in Visual Python.**User option**

### Binomial and Multinomial

: Enter the number of trials.**N**: Enter the probability of success for each trial. Use the funnel icon to get the values entered in a specific column of the dataframe. (Note that the values entered are arbitrary in this example).**P**

## Continuous Probability Distribution

### Normal

: Set the mean of the normal distribution.**Loc**: Set the standard deviation of the normal distribution.**Scale**: You can add options other than those provided in Visual Python.**User option**

### Beta

: Set the shape parameter**A****A**for the beta distribution.: Set the shape parameter**B****B**for the beta distribution.: You can add options other than those provided in Visual Python.**User option**

### Gamma

: Set the shape parameter of the gamma distribution.**A**: You can add options other than those provided in Visual Python.**User Option**

### Student's t and Chi2

: Set the degrees of freedom for the t-distribution or chi-squared distribution.**Df**: You can add options other than those provided in Visual Python.**User Option**

### F

: Set the numerator degrees of freedom.**Dfn**: Set the denominator degrees of freedom.**Dfd**

The F-distribution represents the ratio of two chi-squared distributions.

and**Dfn**are the degrees of freedom for the two chi-squared distributions.**Dfd**

: You can add options other than those provided in Visual Python.**User Option**

### Dirichlet

: Enter the importance for three categories (or dimensions) in the format**Alpha**.**(a, b, c)**: If entered, the random number generated will be fixed.**Seed**: You can add options other than those provided in Visual Python.**User Option**

### Multivariate Normal

: Set the mean of the distribution. For**Mean**, the mean of the first distribution is**[a, b]**, and the mean of the second distribution is**a**.**b**: Set the covariance of the distribution. For**Cov**, the covariance of the first distribution is**[a, b]**, and the covariance of the second distribution is**a**.**b**: If True, allows generating the distribution even when the covariance matrix is singular.**Allow Singular**: You can add options other than those provided in Visual Python.**User Option**

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