We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Typo in CanCorr documentation and docstring
CanCorr's documentation and docstring say that CanCorr has attributes x_cancoeff and y_cancoeff. However, they should say x_cancoef and y_cancoef. Should I submit a PR?

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `statsmodels/multivariate/cancorr.py`
Content:
```
1 # -*- coding: utf-8 -*-
2 
3 """Canonical correlation analysis
4 
5 author: Yichuan Liu
6 """
7 import numpy as np
8 from numpy.linalg import svd
9 import scipy
10 import pandas as pd
11 
12 from statsmodels.base.model import Model
13 from statsmodels.iolib import summary2
14 from .multivariate_ols import multivariate_stats
15 
16 
17 class CanCorr(Model):
18     """
19     Canonical correlation analysis using singular value decomposition
20 
21     For matrices exog=x and endog=y, find projections x_cancoef and y_cancoef
22     such that:
23 
24         x1 = x * x_cancoef, x1' * x1 is identity matrix
25         y1 = y * y_cancoef, y1' * y1 is identity matrix
26 
27     and the correlation between x1 and y1 is maximized.
28 
29     Attributes
30     ----------
31     endog : ndarray
32         See Parameters.
33     exog : ndarray
34         See Parameters.
35     cancorr : ndarray
36         The canonical correlation values
37     y_cancoeff : ndarray
38         The canonical coefficients for endog
39     x_cancoeff : ndarray
40         The canonical coefficients for exog
41 
42     References
43     ----------
44     .. [*] http://numerical.recipes/whp/notes/CanonCorrBySVD.pdf
45     .. [*] http://www.csun.edu/~ata20315/psy524/docs/Psy524%20Lecture%208%20CC.pdf
46     .. [*] http://www.mathematica-journal.com/2014/06/canonical-correlation-analysis/
47     """  # noqa:E501
48     def __init__(self, endog, exog, tolerance=1e-8, missing='none', hasconst=None, **kwargs):
49         super(CanCorr, self).__init__(endog, exog, missing=missing,
50                                       hasconst=hasconst, **kwargs)
51         self._fit(tolerance)
52 
53     def _fit(self, tolerance=1e-8):
54         """Fit the model
55 
56         A ValueError is raised if there are singular values smaller than the
57         tolerance. The treatment of singular arrays might change in future.
58 
59         Parameters
60         ----------
61         tolerance : float
62             eigenvalue tolerance, values smaller than which is considered 0
63         """
64         nobs, k_yvar = self.endog.shape
65         nobs, k_xvar = self.exog.shape
66         k = np.min([k_yvar, k_xvar])
67 
68         x = np.array(self.exog)
69         x = x - x.mean(0)
70         y = np.array(self.endog)
71         y = y - y.mean(0)
72 
73         ux, sx, vx = svd(x, 0)
74         # vx_ds = vx.T divided by sx
75         vx_ds = vx.T
76         mask = sx > tolerance
77         if mask.sum() < len(mask):
78             raise ValueError('exog is collinear.')
79         vx_ds[:, mask] /= sx[mask]
80         uy, sy, vy = svd(y, 0)
81         # vy_ds = vy.T divided by sy
82         vy_ds = vy.T
83         mask = sy > tolerance
84         if mask.sum() < len(mask):
85             raise ValueError('endog is collinear.')
86         vy_ds[:, mask] /= sy[mask]
87         u, s, v = svd(ux.T.dot(uy), 0)
88 
89         # Correct any roundoff
90         self.cancorr = np.array([max(0, min(s[i], 1)) for i in range(len(s))])
91 
92         self.x_cancoef = vx_ds.dot(u[:, :k])
93         self.y_cancoef = vy_ds.dot(v.T[:, :k])
94 
95     def corr_test(self):
96         """Approximate F test
97         Perform multivariate statistical tests of the hypothesis that
98         there is no canonical correlation between endog and exog.
99         For each canonical correlation, testing its significance based on
100         Wilks' lambda.
101 
102         Returns
103         -------
104         CanCorrTestResults instance
105         """
106         nobs, k_yvar = self.endog.shape
107         nobs, k_xvar = self.exog.shape
108         eigenvals = np.power(self.cancorr, 2)
109         stats = pd.DataFrame(columns=['Canonical Correlation', "Wilks' lambda",
110                                       'Num DF','Den DF', 'F Value','Pr > F'],
111                              index=list(range(len(eigenvals) - 1, -1, -1)))
112         prod = 1
113         for i in range(len(eigenvals) - 1, -1, -1):
114             prod *= 1 - eigenvals[i]
115             p = k_yvar - i
116             q = k_xvar - i
117             r = (nobs - k_yvar - 1) - (p - q + 1) / 2
118             u = (p * q - 2) / 4
119             df1 = p * q
120             if p ** 2 + q ** 2 - 5 > 0:
121                 t = np.sqrt(((p * q) ** 2 - 4) / (p ** 2 + q ** 2 - 5))
122             else:
123                 t = 1
124             df2 = r * t - 2 * u
125             lmd = np.power(prod, 1 / t)
126             F = (1 - lmd) / lmd * df2 / df1
127             stats.loc[i, 'Canonical Correlation'] = self.cancorr[i]
128             stats.loc[i, "Wilks' lambda"] = prod
129             stats.loc[i, 'Num DF'] = df1
130             stats.loc[i, 'Den DF'] = df2
131             stats.loc[i, 'F Value'] = F
132             pval = scipy.stats.f.sf(F, df1, df2)
133             stats.loc[i, 'Pr > F'] = pval
134             '''
135             # Wilk's Chi square test of each canonical correlation
136             df = (p - i + 1) * (q - i + 1)
137             chi2 = a * np.log(prod)
138             pval = stats.chi2.sf(chi2, df)
139             stats.loc[i, 'Canonical correlation'] = self.cancorr[i]
140             stats.loc[i, 'Chi-square'] = chi2
141             stats.loc[i, 'DF'] = df
142             stats.loc[i, 'Pr > ChiSq'] = pval
143             '''
144         ind = stats.index.values[::-1]
145         stats = stats.loc[ind, :]
146 
147         # Multivariate tests (remember x has mean removed)
148         stats_mv = multivariate_stats(eigenvals,
149                                       k_yvar, k_xvar, nobs - k_xvar - 1)
150         return CanCorrTestResults(stats, stats_mv)
151 
152 
153 class CanCorrTestResults:
154     """
155     Canonical correlation results class
156 
157     Attributes
158     ----------
159     stats : DataFrame
160         Contain statistical tests results for each canonical correlation
161     stats_mv : DataFrame
162         Contain the multivariate statistical tests results
163     """
164     def __init__(self, stats, stats_mv):
165         self.stats = stats
166         self.stats_mv = stats_mv
167 
168     def __str__(self):
169         return self.summary().__str__()
170 
171     def summary(self):
172         summ = summary2.Summary()
173         summ.add_title('Cancorr results')
174         summ.add_df(self.stats)
175         summ.add_dict({'': ''})
176         summ.add_dict({'Multivariate Statistics and F Approximations': ''})
177         summ.add_df(self.stats_mv)
178         return summ
179
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/statsmodels/multivariate/cancorr.py b/statsmodels/multivariate/cancorr.py
--- a/statsmodels/multivariate/cancorr.py
+++ b/statsmodels/multivariate/cancorr.py
@@ -34,9 +34,9 @@
         See Parameters.
     cancorr : ndarray
         The canonical correlation values
-    y_cancoeff : ndarray
+    y_cancoef : ndarray
         The canonical coefficients for endog
-    x_cancoeff : ndarray
+    x_cancoef : ndarray
         The canonical coefficients for exog
 
     References

{"golden_diff": "diff --git a/statsmodels/multivariate/cancorr.py b/statsmodels/multivariate/cancorr.py\n--- a/statsmodels/multivariate/cancorr.py\n+++ b/statsmodels/multivariate/cancorr.py\n@@ -34,9 +34,9 @@\n         See Parameters.\n     cancorr : ndarray\n         The canonical correlation values\n-    y_cancoeff : ndarray\n+    y_cancoef : ndarray\n         The canonical coefficients for endog\n-    x_cancoeff : ndarray\n+    x_cancoef : ndarray\n         The canonical coefficients for exog\n \n     References\n", "issue": "Typo in CanCorr documentation and docstring\nCanCorr's documentation and docstring say that CanCorr has attributes x_cancoeff and y_cancoeff. However, they should say x_cancoef and y_cancoef. Should I submit a PR?\n", "before_files": [{"content": "# -*- coding: utf-8 -*-\n\n\"\"\"Canonical correlation analysis\n\nauthor: Yichuan Liu\n\"\"\"\nimport numpy as np\nfrom numpy.linalg import svd\nimport scipy\nimport pandas as pd\n\nfrom statsmodels.base.model import Model\nfrom statsmodels.iolib import summary2\nfrom .multivariate_ols import multivariate_stats\n\n\nclass CanCorr(Model):\n    \"\"\"\n    Canonical correlation analysis using singular value decomposition\n\n    For matrices exog=x and endog=y, find projections x_cancoef and y_cancoef\n    such that:\n\n        x1 = x * x_cancoef, x1' * x1 is identity matrix\n        y1 = y * y_cancoef, y1' * y1 is identity matrix\n\n    and the correlation between x1 and y1 is maximized.\n\n    Attributes\n    ----------\n    endog : ndarray\n        See Parameters.\n    exog : ndarray\n        See Parameters.\n    cancorr : ndarray\n        The canonical correlation values\n    y_cancoeff : ndarray\n        The canonical coefficients for endog\n    x_cancoeff : ndarray\n        The canonical coefficients for exog\n\n    References\n    ----------\n    .. [*] http://numerical.recipes/whp/notes/CanonCorrBySVD.pdf\n    .. [*] http://www.csun.edu/~ata20315/psy524/docs/Psy524%20Lecture%208%20CC.pdf\n    .. [*] http://www.mathematica-journal.com/2014/06/canonical-correlation-analysis/\n    \"\"\"  # noqa:E501\n    def __init__(self, endog, exog, tolerance=1e-8, missing='none', hasconst=None, **kwargs):\n        super(CanCorr, self).__init__(endog, exog, missing=missing,\n                                      hasconst=hasconst, **kwargs)\n        self._fit(tolerance)\n\n    def _fit(self, tolerance=1e-8):\n        \"\"\"Fit the model\n\n        A ValueError is raised if there are singular values smaller than the\n        tolerance. The treatment of singular arrays might change in future.\n\n        Parameters\n        ----------\n        tolerance : float\n            eigenvalue tolerance, values smaller than which is considered 0\n        \"\"\"\n        nobs, k_yvar = self.endog.shape\n        nobs, k_xvar = self.exog.shape\n        k = np.min([k_yvar, k_xvar])\n\n        x = np.array(self.exog)\n        x = x - x.mean(0)\n        y = np.array(self.endog)\n        y = y - y.mean(0)\n\n        ux, sx, vx = svd(x, 0)\n        # vx_ds = vx.T divided by sx\n        vx_ds = vx.T\n        mask = sx > tolerance\n        if mask.sum() < len(mask):\n            raise ValueError('exog is collinear.')\n        vx_ds[:, mask] /= sx[mask]\n        uy, sy, vy = svd(y, 0)\n        # vy_ds = vy.T divided by sy\n        vy_ds = vy.T\n        mask = sy > tolerance\n        if mask.sum() < len(mask):\n            raise ValueError('endog is collinear.')\n        vy_ds[:, mask] /= sy[mask]\n        u, s, v = svd(ux.T.dot(uy), 0)\n\n        # Correct any roundoff\n        self.cancorr = np.array([max(0, min(s[i], 1)) for i in range(len(s))])\n\n        self.x_cancoef = vx_ds.dot(u[:, :k])\n        self.y_cancoef = vy_ds.dot(v.T[:, :k])\n\n    def corr_test(self):\n        \"\"\"Approximate F test\n        Perform multivariate statistical tests of the hypothesis that\n        there is no canonical correlation between endog and exog.\n        For each canonical correlation, testing its significance based on\n        Wilks' lambda.\n\n        Returns\n        -------\n        CanCorrTestResults instance\n        \"\"\"\n        nobs, k_yvar = self.endog.shape\n        nobs, k_xvar = self.exog.shape\n        eigenvals = np.power(self.cancorr, 2)\n        stats = pd.DataFrame(columns=['Canonical Correlation', \"Wilks' lambda\",\n                                      'Num DF','Den DF', 'F Value','Pr > F'],\n                             index=list(range(len(eigenvals) - 1, -1, -1)))\n        prod = 1\n        for i in range(len(eigenvals) - 1, -1, -1):\n            prod *= 1 - eigenvals[i]\n            p = k_yvar - i\n            q = k_xvar - i\n            r = (nobs - k_yvar - 1) - (p - q + 1) / 2\n            u = (p * q - 2) / 4\n            df1 = p * q\n            if p ** 2 + q ** 2 - 5 > 0:\n                t = np.sqrt(((p * q) ** 2 - 4) / (p ** 2 + q ** 2 - 5))\n            else:\n                t = 1\n            df2 = r * t - 2 * u\n            lmd = np.power(prod, 1 / t)\n            F = (1 - lmd) / lmd * df2 / df1\n            stats.loc[i, 'Canonical Correlation'] = self.cancorr[i]\n            stats.loc[i, \"Wilks' lambda\"] = prod\n            stats.loc[i, 'Num DF'] = df1\n            stats.loc[i, 'Den DF'] = df2\n            stats.loc[i, 'F Value'] = F\n            pval = scipy.stats.f.sf(F, df1, df2)\n            stats.loc[i, 'Pr > F'] = pval\n            '''\n            # Wilk's Chi square test of each canonical correlation\n            df = (p - i + 1) * (q - i + 1)\n            chi2 = a * np.log(prod)\n            pval = stats.chi2.sf(chi2, df)\n            stats.loc[i, 'Canonical correlation'] = self.cancorr[i]\n            stats.loc[i, 'Chi-square'] = chi2\n            stats.loc[i, 'DF'] = df\n            stats.loc[i, 'Pr > ChiSq'] = pval\n            '''\n        ind = stats.index.values[::-1]\n        stats = stats.loc[ind, :]\n\n        # Multivariate tests (remember x has mean removed)\n        stats_mv = multivariate_stats(eigenvals,\n                                      k_yvar, k_xvar, nobs - k_xvar - 1)\n        return CanCorrTestResults(stats, stats_mv)\n\n\nclass CanCorrTestResults:\n    \"\"\"\n    Canonical correlation results class\n\n    Attributes\n    ----------\n    stats : DataFrame\n        Contain statistical tests results for each canonical correlation\n    stats_mv : DataFrame\n        Contain the multivariate statistical tests results\n    \"\"\"\n    def __init__(self, stats, stats_mv):\n        self.stats = stats\n        self.stats_mv = stats_mv\n\n    def __str__(self):\n        return self.summary().__str__()\n\n    def summary(self):\n        summ = summary2.Summary()\n        summ.add_title('Cancorr results')\n        summ.add_df(self.stats)\n        summ.add_dict({'': ''})\n        summ.add_dict({'Multivariate Statistics and F Approximations': ''})\n        summ.add_df(self.stats_mv)\n        return summ\n", "path": "statsmodels/multivariate/cancorr.py"}], "after_files": [{"content": "# -*- coding: utf-8 -*-\n\n\"\"\"Canonical correlation analysis\n\nauthor: Yichuan Liu\n\"\"\"\nimport numpy as np\nfrom numpy.linalg import svd\nimport scipy\nimport pandas as pd\n\nfrom statsmodels.base.model import Model\nfrom statsmodels.iolib import summary2\nfrom .multivariate_ols import multivariate_stats\n\n\nclass CanCorr(Model):\n    \"\"\"\n    Canonical correlation analysis using singular value decomposition\n\n    For matrices exog=x and endog=y, find projections x_cancoef and y_cancoef\n    such that:\n\n        x1 = x * x_cancoef, x1' * x1 is identity matrix\n        y1 = y * y_cancoef, y1' * y1 is identity matrix\n\n    and the correlation between x1 and y1 is maximized.\n\n    Attributes\n    ----------\n    endog : ndarray\n        See Parameters.\n    exog : ndarray\n        See Parameters.\n    cancorr : ndarray\n        The canonical correlation values\n    y_cancoef : ndarray\n        The canonical coefficients for endog\n    x_cancoef : ndarray\n        The canonical coefficients for exog\n\n    References\n    ----------\n    .. [*] http://numerical.recipes/whp/notes/CanonCorrBySVD.pdf\n    .. [*] http://www.csun.edu/~ata20315/psy524/docs/Psy524%20Lecture%208%20CC.pdf\n    .. [*] http://www.mathematica-journal.com/2014/06/canonical-correlation-analysis/\n    \"\"\"  # noqa:E501\n    def __init__(self, endog, exog, tolerance=1e-8, missing='none', hasconst=None, **kwargs):\n        super(CanCorr, self).__init__(endog, exog, missing=missing,\n                                      hasconst=hasconst, **kwargs)\n        self._fit(tolerance)\n\n    def _fit(self, tolerance=1e-8):\n        \"\"\"Fit the model\n\n        A ValueError is raised if there are singular values smaller than the\n        tolerance. The treatment of singular arrays might change in future.\n\n        Parameters\n        ----------\n        tolerance : float\n            eigenvalue tolerance, values smaller than which is considered 0\n        \"\"\"\n        nobs, k_yvar = self.endog.shape\n        nobs, k_xvar = self.exog.shape\n        k = np.min([k_yvar, k_xvar])\n\n        x = np.array(self.exog)\n        x = x - x.mean(0)\n        y = np.array(self.endog)\n        y = y - y.mean(0)\n\n        ux, sx, vx = svd(x, 0)\n        # vx_ds = vx.T divided by sx\n        vx_ds = vx.T\n        mask = sx > tolerance\n        if mask.sum() < len(mask):\n            raise ValueError('exog is collinear.')\n        vx_ds[:, mask] /= sx[mask]\n        uy, sy, vy = svd(y, 0)\n        # vy_ds = vy.T divided by sy\n        vy_ds = vy.T\n        mask = sy > tolerance\n        if mask.sum() < len(mask):\n            raise ValueError('endog is collinear.')\n        vy_ds[:, mask] /= sy[mask]\n        u, s, v = svd(ux.T.dot(uy), 0)\n\n        # Correct any roundoff\n        self.cancorr = np.array([max(0, min(s[i], 1)) for i in range(len(s))])\n\n        self.x_cancoef = vx_ds.dot(u[:, :k])\n        self.y_cancoef = vy_ds.dot(v.T[:, :k])\n\n    def corr_test(self):\n        \"\"\"Approximate F test\n        Perform multivariate statistical tests of the hypothesis that\n        there is no canonical correlation between endog and exog.\n        For each canonical correlation, testing its significance based on\n        Wilks' lambda.\n\n        Returns\n        -------\n        CanCorrTestResults instance\n        \"\"\"\n        nobs, k_yvar = self.endog.shape\n        nobs, k_xvar = self.exog.shape\n        eigenvals = np.power(self.cancorr, 2)\n        stats = pd.DataFrame(columns=['Canonical Correlation', \"Wilks' lambda\",\n                                      'Num DF','Den DF', 'F Value','Pr > F'],\n                             index=list(range(len(eigenvals) - 1, -1, -1)))\n        prod = 1\n        for i in range(len(eigenvals) - 1, -1, -1):\n            prod *= 1 - eigenvals[i]\n            p = k_yvar - i\n            q = k_xvar - i\n            r = (nobs - k_yvar - 1) - (p - q + 1) / 2\n            u = (p * q - 2) / 4\n            df1 = p * q\n            if p ** 2 + q ** 2 - 5 > 0:\n                t = np.sqrt(((p * q) ** 2 - 4) / (p ** 2 + q ** 2 - 5))\n            else:\n                t = 1\n            df2 = r * t - 2 * u\n            lmd = np.power(prod, 1 / t)\n            F = (1 - lmd) / lmd * df2 / df1\n            stats.loc[i, 'Canonical Correlation'] = self.cancorr[i]\n            stats.loc[i, \"Wilks' lambda\"] = prod\n            stats.loc[i, 'Num DF'] = df1\n            stats.loc[i, 'Den DF'] = df2\n            stats.loc[i, 'F Value'] = F\n            pval = scipy.stats.f.sf(F, df1, df2)\n            stats.loc[i, 'Pr > F'] = pval\n            '''\n            # Wilk's Chi square test of each canonical correlation\n            df = (p - i + 1) * (q - i + 1)\n            chi2 = a * np.log(prod)\n            pval = stats.chi2.sf(chi2, df)\n            stats.loc[i, 'Canonical correlation'] = self.cancorr[i]\n            stats.loc[i, 'Chi-square'] = chi2\n            stats.loc[i, 'DF'] = df\n            stats.loc[i, 'Pr > ChiSq'] = pval\n            '''\n        ind = stats.index.values[::-1]\n        stats = stats.loc[ind, :]\n\n        # Multivariate tests (remember x has mean removed)\n        stats_mv = multivariate_stats(eigenvals,\n                                      k_yvar, k_xvar, nobs - k_xvar - 1)\n        return CanCorrTestResults(stats, stats_mv)\n\n\nclass CanCorrTestResults:\n    \"\"\"\n    Canonical correlation results class\n\n    Attributes\n    ----------\n    stats : DataFrame\n        Contain statistical tests results for each canonical correlation\n    stats_mv : DataFrame\n        Contain the multivariate statistical tests results\n    \"\"\"\n    def __init__(self, stats, stats_mv):\n        self.stats = stats\n        self.stats_mv = stats_mv\n\n    def __str__(self):\n        return self.summary().__str__()\n\n    def summary(self):\n        summ = summary2.Summary()\n        summ.add_title('Cancorr results')\n        summ.add_df(self.stats)\n        summ.add_dict({'': ''})\n        summ.add_dict({'Multivariate Statistics and F Approximations': ''})\n        summ.add_df(self.stats_mv)\n        return summ\n", "path": "statsmodels/multivariate/cancorr.py"}]}