fix patsy and other typos

Author: juanitorduz

examples/generalized_linear_models/GLM-out-of-sample-predictions.ipynb

@@ -30,7 +30,7 @@
    "source": [
     "# GLM in PyMC3: Out-Of-Sample Predictions\n",
     "\n",
-    "In this notebook I explore the [glm](https://docs.pymc.io/api/glm.html) module of [PyMC3](https://docs.pymc.io/). I am particularly interested in the model definition using [pasty](https://patsy.readthedocs.io/en/latest/) formulas, as it makes the model evaluation loop faster (easier to include features and/or interactions). There are many good resources on this subject, but most of them evaluate the model in-sample. For many applications we require doing predictions on out-of-sample data. This experiment was motivated by the discussion of the thread [\"Out of sample\" predictions with the GLM sub-module](https://discourse.pymc.io/t/out-of-sample-predictions-with-the-glm-sub-module/773) on the (great!) forum [discourse.pymc.io/](https://discourse.pymc.io/), thank you all for your input!\n",
+    "In this notebook I explore the [glm](https://docs.pymc.io/api/glm.html) module of [PyMC3](https://docs.pymc.io/). I am particularly interested in the model definition using [patsy](https://patsy.readthedocs.io/en/latest/) formulas, as it makes the model evaluation loop faster (easier to include features and/or interactions). There are many good resources on this subject, but most of them evaluate the model in-sample. For many applications we require doing predictions on out-of-sample data. This experiment was motivated by the discussion of the thread [\"Out of sample\" predictions with the GLM sub-module](https://discourse.pymc.io/t/out-of-sample-predictions-with-the-glm-sub-module/773) on the (great!) forum [discourse.pymc.io/](https://discourse.pymc.io/), thank you all for your input!\n",
     "\n",
     "**Resources**\n",
     "\n",
@@ -201,7 +201,7 @@
    "source": [
     "## Prepare Data for Modeling\n",
     "\n",
-    "I wanted to use the *`classmethod`* `from_formula` (see [documentation](https://docs.pymc.io/api/glm.html)), but I was not able to generate out-of-sample predictions with this approach (if you find a way please let me know!). As a workaround, I created the features from a formula using [pasty](https://patsy.readthedocs.io/en/latest/) directly and then use *`class`* `pymc3.glm.linear.GLM` (this was motivated by going into the [source code](https://github.com/pymc-devs/pymc3/blob/master/pymc3/glm/linear.py))."
+    "I wanted to use the *`classmethod`* `from_formula` (see [documentation](https://docs.pymc.io/api/glm.html)), but I was not able to generate out-of-sample predictions with this approach (if you find a way please let me know!). As a workaround, I created the features from a formula using [patsy](https://patsy.readthedocs.io/en/latest/) directly and then use *`class`* `pymc3.glm.linear.GLM` (this was motivated by going into the [source code](https://github.com/pymc-devs/pymc3/blob/master/pymc3/glm/linear.py))."
    ],
    "cell_type": "markdown",
    "metadata": {}
@@ -390,7 +390,7 @@
   },
   {
    "source": [
-    "So we can get summarize the model predictions (before seeing the data) but taking the mean over the samples. Let us store this in a dataframe to in order to generate some plots."
+    "So we can get summarize the model predictions (before seeing the data) by taking the mean over the samples. Let us store this in a dataframe to in order to generate some plots."
    ],
    "cell_type": "markdown",
    "metadata": {}
@@ -453,7 +453,7 @@
   },
   {
    "source": [
-    "We clearly see that the model can not distinguish between the two classes yet. This makes sense as we have non-informative priors for this synthetic data set.We can also confirm this if we plot each point separately:"
+    "We clearly see that the model can not distinguish between the two classes yet. This makes sense as we have non-informative priors for this synthetic data set. We can also confirm this if we plot each point separately:"
    ],
    "cell_type": "markdown",
    "metadata": {}
@@ -861,4 +861,4 @@
    ]
   }
  ]
-}
+}