Update mixedlm.md

Please, I have done the correction and also added the note as you specified. 

I will still need to exemplify that the three models can be combined in an operation, but that will be later.  I just want you to work on the aspect that is important to you and that I have already completed, then later I will do that.
 
Review the changes, and if you think I didn't still get some things right, always feel free to reach out. Thanks

content/python/concepts/statsmodel/terms/mixedlm/mixedlm.md

@@ -20,13 +20,50 @@ CatalogContent:
 ## Application of mixedlm model
 
 - **Random Intercept Model**: Captures group-level variability in the intercept while keeping the slope fixed.
+- Parameter:
+- endog: A 1D array representing the dependent variable.
+- exog: A 2D array of variable or covariate that determine the mean structure
+- groups: A 1D array-like and a vector of label that indepence of data from different group.
+- exog_re: (optional) A 2D array of covariates that define the covariance structure e.g random intercept covariance, if not provided, the default is a random intercept forneach group.
+- use-sqrt: (optional), if true, optimization is performed using the lower triangle of the random effect covariance matrix. otherwisebl, it uses the lower triangle of the covariance matrix directly. 
+- 
 - **Random Slope Model**: Allows the slope to vary across groups to account for differences in trends.
+- parameters:
+- endog: A dependent variable represented as a 1D array.
+
+exog: A 2D array of covariates or variables that determine the mean structure.
+
+groups: A 1D array-like vector of labels ensuring that data from different groups remain independent.
+
+re_formula: A string formula specifying the structure of the random effects.
+
+use-sqrt: (Optional) If True, optimization is performed using the lower triangle of the square root of the random effects covariance matrix. Otherwise, it uses the lower triangle of the covariance matrix directly.
+
+missing: (Optional) A string parameter specifying the method for handling missing data.
 - **Variance Components Model**: Decomposes variance into different components to understand variability sources.
+- parameters:
+- endog: A dependent variable represented as a 1D array.
+
+exog: A 2D array of covariates or variables that determine the mean structure.
+
+groups: A 1D array-like vector of labels ensuring independence of data across groups.
+
+vc_formula: A dictionary where keys represent the names of variance components, and the values are formulas specifying the structure of each variance component.
+
+use-sqrt: (Optional) If True, optimization is performed using the lower triangle of the square root of the random effects covariance matrix. Otherwise, it uses the lower triangle of the covariance matrix directly.
+
+missing: (Optional) A string parameter specifying the method for handling missing data.
+Note: You can combine the:
+random intercept model.
+random slope model. 
+and covariance component model. 
+For a For a single operation, which also depends on type of data that is intended for measurement 
 
 ## Syntax
 
 ```
 import statsmodels.api as sm
+import statsmodels.formula.api as smf
 
 model = sm.MixedLM(endog, exog, groups, exog_re=None)
 ```
@@ -41,8 +78,9 @@ model = sm.MixedLM(endog, exog, groups, exog_re=None)
 In this example, a mixed-effects model is fitted to NBA team performance data, with `'Minutes'` as a fixed effect and `'Team'` as a random effect to analyze points scored:
 
 ```
-import pandas as pd
-from statsmodels.formula.api import mixedlm
+import statsmodels.api as sm
+import statsmodels.formula.api as smf
+
 
 # An example of the performance between two NBA teams' (team names: TeamG, TeamR)
 data = pd.DataFrame({