In this tab, you can generate new datasets and choose distribution types. You can create multiple distributions simultaneously by collecting them in the command box.
This tutorial assumes that you already selected a project and imported data. For more information please visit on Project and Import Data section.
This is a detailed description of all settings you can define for this operation.
Type a positive integer greater than 0 as size for the dataset.
Select the distribution you want to create. Depending on the distribution, you need to provide one or up to four parameters; see details in the table Distributions and parameters. You can also create a preview of your distribution settings with the Show button.
Collect all distributions you want to generate in this box.
As in all Data Enrichment Tabs, you can select only a part of the data by using a Filter. A more detailed description of Filters can be found here Since you are generating new data, the filter will have no effect.
Preview button will do the operation but will not save the data, while Apply will give you the option to save the data in a folder in content. More information is available in here
Creating new datasets is easy and follows the same rules:
The following animation shows an example usage, where you create a Normal and Weibull distribution with 1000 rows simultaneously.
| Distribution | Parameter 1 | Parameter 2 | Parameter 3 | Parameter 4 | Link |
|---|---|---|---|---|---|
| Beta | alpha (>0) | beta (>0) | - | - | Link |
| Binomial | n (>0) | p (0<=p<=1) | - | - | Link |
| Birnbaum Saunders | c (>0) | - | - | - | Link |
| Burr Type XII | c (>0) | d (>0) | - | - | Link |
| Chi-Square | df (>0) | - | - | - | Link |
| Exponential | scale | - | - | - | Link |
| Generalized Extreme Value | mu | beta (>=0) | - | - | Link |
| F | df numerator (>0) | df denumerator (>0) | - | - | Link |
| Gamma | shape (>=0) | scale (>=0) | - | - | Link |
| Generalized Pareto | shape | scale (>=0) | loc | - | Link |
| Geometric | p (0<=p<=1) | - | - | - | Link |
| Half-Normal | loc | scale (>0) | - | - | Link |
| Hypergeometric | number of good (0<=ngood<=1e9) | number of bad (0<=nbad<=1e9) | - | - | Link |
| Inverse Gaussian | mean (>0) | scale (>0) | - | - | Link |
| Logistic | loc | scale (>0) | - | - | Link |
| Loglogistic | shape (>0) | loc | scale (>=0) | - | Link |
| Lognormal | mean | standard deviation (>=0) | - | - | Link |
| Nakagami | nu (>0) | scale (>=0) | - | - | Link |
| Negative Binomial | n (>0) | p (0<=p<=1) | - | - | Link |
| Noncentral F | df numerator (>0) | df denumerator (>0) | nonc (>=0) | - | Link |
| Noncentral t | df (>0) | nc | - | - | Link |
| Noncentral Chi-Squared | df (>0) | nonc (>0) | - | - | Link |
| Normal | mean | standard deviation (>=0) | - | - | Link |
| Poisson | lambda (>=0) | - | - | - | Link |
| Rayleigh | scale (>=0) | - | - | - | Link |
| Rician | shape (>0) | loc | scale | - | Link |
| Stable | alpha (0< alpha<=2) | beta (-1 <= beta <= 1) | loc | scale (>0) | Link |
| Student's t | df (>0) | - | - | - | Link |
| t Location-Scale | df (>0) | loc | scale (>0) | - | Link |
| Uniform (Continuous) | lower boundary | upper boundary | - | - | Link |
| Uniform (Discrete) | lower boundary | upper boundary | - | - | Link |
| Weibull | scale | shape | - | - | Link |
| Linspace | start | stop | - | - | Link |
| Logspace | start (>0) | stop (>0) | - | - | Link |
| Geomspace | start (>0) | stop (>0) | - | - | Link |
| Arange | start | space | - | - | Link |