Repository: statsmodels/statsmodels

Issue:
Interpret MANOVA result
I have successfully executed a MANOVA test (using the data from [here](http://chymera.eu/debug/statsmodels/manova.zip)), and with the following script:

```
import numpy as np
import pandas as pd
from statsmodels.multivariate.manova import MANOVA

df = pd.read_csv('data/trajectories.csv')
columns = df.columns
value_columns = [i for i in columns if '/' in i]

# convert exog to dummy
# https://github.com/statsmodels/statsmodels/issues/6155#issuecomment-530637971
exog = (np.asarray(df['session'])[:,None] == np.unique(df['session'])).astype(np.float64)
endog = np.asarray(df[value_columns])

model = MANOVA(endog=endog, exog=exog)
res = model.mv_test()
print(res.summary())
``` 

I get results which look like this:
```
                 Multivariate linear model
============================================================
                                                            
------------------------------------------------------------
           x0           Value  Num DF  Den DF F Value Pr > F
------------------------------------------------------------
          Wilks' lambda 0.6207 6.0000 55.0000  5.6007 0.0001
         Pillai's trace 0.3793 6.0000 55.0000  5.6007 0.0001
 Hotelling-Lawley trace 0.6110 6.0000 55.0000  5.6007 0.0001
    Roy's greatest root 0.6110 6.0000 55.0000  5.6007 0.0001
------------------------------------------------------------
                                                            
------------------------------------------------------------
           x1           Value  Num DF  Den DF F Value Pr > F
------------------------------------------------------------
          Wilks' lambda 0.1771 6.0000 55.0000 42.5804 0.0000
         Pillai's trace 0.8229 6.0000 55.0000 42.5804 0.0000
 Hotelling-Lawley trace 4.6451 6.0000 55.0000 42.5804 0.0000
    Roy's greatest root 4.6451 6.0000 55.0000 42.5804 0.0000
------------------------------------------------------------
                                                            
------------------------------------------------------------
           x2           Value  Num DF  Den DF F Value Pr > F
------------------------------------------------------------
          Wilks' lambda 0.4381 6.0000 55.0000 11.7547 0.0000
         Pillai's trace 0.5619 6.0000 55.0000 11.7547 0.0000
 Hotelling-Lawley trace 1.2823 6.0000 55.0000 11.7547 0.0000
    Roy's greatest root 1.2823 6.0000 55.0000 11.7547 0.0000
------------------------------------------------------------
                                                            
------------------------------------------------------------
           x3           Value  Num DF  Den DF F Value Pr > F
------------------------------------------------------------
          Wilks' lambda 0.2886 6.0000 55.0000 22.5942 0.0000
         Pillai's trace 0.7114 6.0000 55.0000 22.5942 0.0000
 Hotelling-Lawley trace 2.4648 6.0000 55.0000 22.5942 0.0000
    Roy's greatest root 2.4648 6.0000 55.0000 22.5942 0.0000
------------------------------------------------------------
                                                            
------------------------------------------------------------
           x4           Value  Num DF  Den DF F Value Pr > F
------------------------------------------------------------
          Wilks' lambda 0.2402 6.0000 55.0000 28.9928 0.0000
         Pillai's trace 0.7598 6.0000 55.0000 28.9928 0.0000
 Hotelling-Lawley trace 3.1629 6.0000 55.0000 28.9928 0.0000
    Roy's greatest root 3.1629 6.0000 55.0000 28.9928 0.0000
============================================================
```

Since they are five in number, I assume these are results for the levels of the session variable. What I find puzzling is that:

1. All session levels are significant, which is quite strange. Shouldn't at least one be treated as the baseline?
2. I would have expected MANOVA to give me some sort of estimates regarding the multivariate outcome --- i.e. does the variable have a significant effect on all of the outcomes, or some? Any idea how I could address this question?


Discussion:

Resolving patch:
diff --git a/statsmodels/discrete/discrete_model.py b/statsmodels/discrete/discrete_model.py
--- a/statsmodels/discrete/discrete_model.py
+++ b/statsmodels/discrete/discrete_model.py
@@ -279,6 +279,8 @@ def fit_regularized(self, start_params=None, method='l1',
 
         Notes
         -----
+        Using 'l1_cvxopt_cp' requires the cvxopt module.
+
         Extra parameters are not penalized if alpha is given as a scalar.
         An example is the shape parameter in NegativeBinomial `nb1` and `nb2`.
 
diff --git a/statsmodels/regression/linear_model.py b/statsmodels/regression/linear_model.py
--- a/statsmodels/regression/linear_model.py
+++ b/statsmodels/regression/linear_model.py
@@ -139,6 +139,9 @@
         zero_tol : float
             Coefficients below this threshold are treated as zero.
 
+        The cvxopt module is required to estimate model using the square root
+        lasso.
+
         References
         ----------
         .. [*] Friedman, Hastie, Tibshirani (2008).  Regularization paths for


Test patch: