Cumulative Distribution Function
Cumulative Distribution Function |
Description | The Cumulative Distribution Function (CDF) helps to learn about the distribution of data by calculating the cumulative probability of a given variable value in a dataset. |
Why to use | To calculate the probability of a given variable value by trying to fit it with different distribution functions. |
When to use | For numerical variables having positive or negative values. | When not to use | — |
Prerequisites | The data should not contain any missing values. |
Input | Numerical variable having any positive or negative value. | Output | - CDF curve
- Cumulative Probability Function predicted by CDF
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Statistical Methods used | Cumulative Probability | Limitations | It can be used only on numerical data.
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CDF takes input as a random variable value (either discrete or continuous) and then determines the cumulative probability of that variable. By default, the data is sorted and then sent to the algorithm. Also, in the output Data tab, the resultant data appears in a sorted manner.
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