1. Prob. Distribution

Generate a Specific Distribution or View Information

1. Click the Prob. Distribution in the Statistics category.

1. Code View: Preview the code that will be output.

2. Data View: Preview the data that will be output.

3. Run: Print and run the code.

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Common

• Action

Discrete Probability Distribution

• Bernoulli
• Binomial and Multinomial

Continuous Probability Distribution

• Uniform

• Normal
• Beta
• Gamma
• Student's t and Chi2
• F
• Dirichlet
• Multivariate Normal

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Common

Action

Generate Random Numbers

1. Size: Set the number of samples you want to draw from the distribution you want to generate.

2. Random State: Set your seed.

3. Allocate to: Specify the variable (call name).

4. Show Sampled Distribution: If checked, visualize the distribution of the extracted samples.

Show Distribution Plot

1. Probability Density Function: Outputs the probability density function.

2. Cumulative Distribution Function: Outputs the cumulative distribution function.

Statistics to P-Value

1. Statistics: You will get the probability of getting a value from this distribution that is greater than or equal to the absolute value entered here.

2. Alternative: Two-sided for a two-tailed test, One-sided for a one-tailed test.

P-Value to Statistics

1. Proportional value: Enter a p-value; the range of the distribution with values greater than or equal to the entered p-value is calculated and displayed.

2. Alternative: Choose between a two-tailed or one-tailed test.

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Discrete Probability Distribution

Bernoulli

1. P: Enter the probability of getting 1 out of a binary outcome of 0 and 1.

2. User option: You can add options other than those provided in Visual Python.

Binomial and Multinomial

1. N: Enter the number of trials.

2. P: 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).

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Continuous Probability Distribution

Normal

1. Loc: Set the mean of the normal distribution.

2. Scale: Set the standard deviation of the normal distribution.

3. User option: You can add options other than those provided in Visual Python.

Beta

1. A: Set the shape parameter A for the beta distribution.

2. B: Set the shape parameter B for the beta distribution.

3. User option: You can add options other than those provided in Visual Python.

Gamma

1. A: Set the shape parameter of the gamma distribution.

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

Student's t and Chi2

1. Df: Set the degrees of freedom for the t-distribution or chi-squared distribution.

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

F

1. Dfn: Set the numerator degrees of freedom.

2. Dfd: Set the denominator degrees of freedom.

• The F-distribution represents the ratio of two chi-squared distributions. Dfn and Dfd are the degrees of freedom for the two chi-squared distributions.

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

Dirichlet

1. Alpha: Enter the importance for three categories (or dimensions) in the format (a, b, c).

2. Seed: If entered, the random number generated will be fixed.

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

Multivariate Normal

1. Mean: Set the mean of the distribution. For [a, b], the mean of the first distribution is a, and the mean of the second distribution is b.

2. Cov: Set the covariance of the distribution. For [a, b], the covariance of the first distribution is a, and the covariance of the second distribution is b.

3. Allow Singular: If True, allows generating the distribution even when the covariance matrix is singular.

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