Added terms and probability-distributions folders and file

content/scipy/concepts/scipy-stats/terms/probability-distributions.md

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+---
+Title: 'probability-distributions.md'
+Description: 'This entry provides an introduction to probability distributions, their syntax, and an example using SciPy.'
+Subjects:
+  - 'Bash/Shell'
+  - 'Data Visualization'
+  - 'Information Technology'
+Tags: 
+  - 'Data Structures'
+  - 'Functions'
+  - 'Probability'
+CatalogContent: 
+  - 'docs/content/scipy/concepts/scipy-stats/terms/probability-distributions/probability-distributions.md'
+  - 'Python:SciPy'
+---
+
+The element of surprise. **Probability distributions** describe how the structure of random variables are allocated.  In the context of SciPy, the scipy.stats module provides various functions for working with different probability distributions that deliver consistent information such as Cumulative Distribution Functions (CDF), Probability Density Functions (PDF), and other statistical metrics.They convey the probabilities of various outcomes, and are fundamental to statistics and data analysis;.
+
+## Syntax
+from scipy.stats import distribution_name
+rv = distribution_name(parameters)
+cdf_value = rv.cdf(x)
+pdf_value = rv.pdf(x)
+pmf_value = rv.pmf(x)
+mean_value = rv.mean()
+variance_value = rv.var()
+
+## Example: Normal distribution using SciPy
+from scipy.stats import norm
+
+rv = norm(loc=0, scale=1)
+
+cdf_value = rv.cdf(1)
+
+pdf_value = rv.pdf(1)
+
+mean_value = rv.mean()
+
+variance_value = rv.var()
+
+print("CDF at x=1:", cdf_value)
+print("PDF at x=1:", pdf_value)
+print("Mean of the distribution:", mean_value)
+print("Variance of the distribution:", variance_value)