Created the scipy-stats concept file (#5877)

* Created the scipy stats file

* syntax update

* Update scipy-stats.md

* Formating fixes

* Update scipy-stats.md

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content/scipy/concepts/scipy-stats/scipy-stats.md

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+---
+Title: 'scipy.stats'
+Description: 'scipy.stats is a Python module offering statistical functions, distributions, and hypothesis tests for data analysis.'
+Subjects:
+  - 'Data Science'
+  - 'Machine Learning'
+Tags:
+  - 'Distributions'
+  - 'Hypothesis Testing'
+  - 'Python'
+  - 'Statistics'
+CatalogContent:
+  - 'learn-python'
+  - 'paths/data-science'
+---
+
+The **`scipy.stats`** module is part of the broader [SciPy](https://www.codecademy.com/resources/docs/scipy) library for scientific computing in Python. It provides functionality for working with various probability distributions, conducting hypothesis tests, and computing descriptive statistics. By leveraging `scipy.stats`, data scientists and analysts can quickly explore their data, model it using theoretical distributions, and draw meaningful conclusions through statistical inference.
+
+## Probability Distributions
+
+`scipy.stats` provides a wide range of distributions (e.g., Normal, Exponential, Binomial) with methods to work with them. For example, for the Normal distribution:
+
+```pseudo
+stats.norm.pdf(x)       # Probability Density Function
+stats.norm.cdf(x)       # Cumulative Distribution Function
+stats.norm.rvs(size=n)  # Generate random samples
+```
+
+- `pdf`: Returns the probability density function (PDF) value at a given point for continuous distributions..
+- `cdf`: Gives the probability that a random variable is less than or equal to a certain value.
+- `rvs`: Draws random samples from the specified distribution.
+
+These methods can be used with other distributions available in `scipy.stats` by replacing norm with the desired distribution (e.g., `expon`, `binom`).
+
+## Descriptive Statistics
+
+Compute common statistical measures with both `numpy` and `scipy.stats`:
+
+```pseudo
+np.mean(data)
+np.median(data)
+stats.mode(data)
+stats.describe(data)
+```
+
+- `mean()`: Computes the average value of the data.
+- `median()`: Finds the middle value separating the higher and lower halves of the data.
+- `mode()`: Returns the most frequently occurring value (for multi-modal data, it returns the smallest mode).
+- `describe()`: Provides a quick summary of the data, including count, min, max, mean, variance, skewness, and kurtosis.
+
+> **Note**: While `mean` and `median` are part of `numpy`, `mode` and `describe` belong to `scipy.stats`.
+
+## Hypothesis Testing
+
+Perform a variety of statistical tests to assess differences or relationships:
+
+```pseudo
+stats.ttest_ind(group1, group2)       # Independent t-test
+stats.chisquare(observed, expected)   # Chi-square test
+stats.mannwhitneyu(group1, group2)    # Mann-Whitney U test
+```
+
+- `ttest_ind()`: Checks if the means of two independent samples differ significantly.
+- `chisquare()`: Compares observed frequencies to expected frequencies for a goodness-of-fit test.
+- `mannwhitneyu()`: Tests for differences in the distribution of two independent samples (non-parametric).
+
+## Correlation and Regression
+
+Evaluate relationships between variables:
+
+```pseudo
+stats.pearsonr(x, y)   # Pearson correlation
+stats.spearmanr(x, y)  # Spearman rank correlation
+stats.kendalltau(x, y) # Kendall’s Tau correlation
+```
+
+- `pearsonr()`: Measures linear correlation between two datasets.
+- `spearmanr()`: Measures rank-based correlation, less sensitive to non-linear relationships.
+- `kendalltau()`: Measures the association between two measured quantities using rank correlation.