fixed file path as suggested (#5710)

* Create statsmodels.md 'MarioSuperFui'

* Create logit.md

* Delete docs/content/python/concepts/statsmodels directory

* Update logit.md

* Updated file path logit.md

* Removed logit.md

* logit.md in the correct filepath directory

* Formatted logit.md using Prettier

* Fix formatting with Prettier

* Delete logit.md

* Minor changes

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content/python/concepts/statsmodels/terms/logit/logit.md

@@ -0,0 +1,70 @@
+---
+Title: 'Logit'
+Description: 'Returns the log-odds of a binary outcome using logistic regression.'
+Subjects:
+  - 'Python'
+  - 'Statistics'
+Tags:
+  - 'Logit'
+  - 'Logistic Regression'
+  - 'Statsmodels'
+CatalogContent:
+  - 'learn-statistics'
+  - 'paths/data-science-inf'
+---
+
+**Logit** is a term used in statistics, specifically in the context of logistic regression. It represents the log-odds of a binary outcome, mapping probabilities from the 0 to 1 range to the entire real number line. The **`.Logit()`** function is a key part of many statistical models, particularly in binary classification tasks.
+
+## Syntax
+
+```pseudo
+statsmodels.api.Logit(endog, exog)
+```
+
+- `endog`: The dependent (binary) variable, which must be a binary outcome (0 or 1).
+- `exog`: The independent variables (features or predictors).
+
+## Example
+
+This example demonstrates how to use the `.Logit()` function in the `statsmodels` library to perform logistic regression:
+
+```py
+import statsmodels.api as sm
+
+# Example data
+X = sm.add_constant([[1], [2], [3], [4], [5]])  # Adding a constant for the intercept
+y = [0, 0, 1, 1, 1]
+
+# Fitting the logistic regression model
+model = sm.Logit(y, X)
+result = model.fit()
+
+# Output the results
+print(result.summary())
+```
+
+> **Note:** The dependent variable (`y`) must contain only binary values (0 or 1) for the logistic regression to be valid.
+
+This example produces a summary of the logistic regression model's results, showing coefficients, standard errors, p-values, and other statistics relevant to evaluating the model fit:
+
+```shell
+                           Logit Regression Results
+==============================================================================
+Dep. Variable:                      y   No. Observations:                    5
+Model:                          Logit   Df Residuals:                        3
+Method:                           MLE   Df Model:                            1
+Date:                Tue, 24 Dec 2024   Pseudo R-squ.:                   1.000
+Time:                        12:28:45   Log-Likelihood:            -5.0138e-10
+converged:                      False   LL-Null:                       -3.3651
+Covariance Type:            nonrobust   LLR p-value:                  0.009480
+==============================================================================
+                 coef    std err          z      P>|z|      [0.025      0.975]
+------------------------------------------------------------------------------
+const       -110.4353   2.23e+05     -0.000      1.000   -4.38e+05    4.38e+05
+x1            44.2438   9.07e+04      0.000      1.000   -1.78e+05    1.78e+05
+==============================================================================
+
+Complete Separation: The results show that there iscomplete separation or perfect prediction.
+In this case the Maximum Likelihood Estimator does not exist and the parameters
+are not identified.
+```

documentation/tags.md

@@ -209,6 +209,7 @@ Lists
 Logic
 Logical
 Logistic Regression
+Logit
 Loops
 Map
 Machine Learning
@@ -315,6 +316,7 @@ SQL Server
 Stacks
 Static Site
 Statistics
+Statsmodels
 Storage
 Stringr
 Strings