DOC: Correct a few formatting issues in Working with Data Tree (#112)

Author: star1327p

docs/source/tutorial/WorkingWithDataTree.ipynb

@@ -13501,7 +13501,7 @@
    "source": [
     "We use \"posterior pushfoward quantities\" to refer to quantities that are not variables in the posterior but deterministic computations using posterior variables. \n",
     "\n",
-    "You can use xarray for these pushforward operations and store them as a new variable in the posterior group. You'll then be able to plot them with ArviZ functions, calculate stats and diagnostics on them (like the {func}`~arviz.mcse`) or save and share the inferencedata object with the pushforward quantities included. \n",
+    "You can use xarray for these pushforward operations and store them as a new variable in the posterior group. You'll then be able to plot them with ArviZ functions, calculate stats and diagnostics on them (like the {func}`~arviz.mcse`) or save and share the `InferenceData` object with the pushforward quantities included. \n",
     "\n",
     "Compute the rolling mean of $\\log(\\tau)$ with {meth}`xarray.DataArray.rolling`, storing the result in the posterior"
    ]
@@ -13519,14 +13519,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Using xarray for pushforward calculations has all the advantages of working with xarray. It also inherits the disadvantages of working with xarray, but we believe those to be outweighed by the advantages, and we have already shown how to extract the data as NumPy arrays. Working with InferenceData is working mainly with xarray objects and this is what is shown in this guide.\n",
+    "Using xarray for pushforward calculations has all the advantages of working with xarray. It also inherits the disadvantages of working with xarray, but we believe those to be outweighed by the advantages, and we have already shown how to extract the data as NumPy arrays. Working with `InferenceData` is working mainly with xarray objects and this is what is shown in this guide.\n",
     "\n",
     "Some examples of these advantages are specifying operations with named dimensions instead of positional ones (as seen in some previous sections), \n",
     "automatic alignment and broadcasting of arrays (as we'll see now),\n",
     "or integration with Dask (as shown in the {ref}`dask_for_arviz` guide).\n",
     "\n",
     "In this cell you will compute pairwise differences between schools on their mean effects (variable `theta`).\n",
-    "To do so, substract the variable theta after renaming the school dimension to the original variable. \n",
+    "To do so, subtract the variable `theta` after renaming the school dimension to the original variable. \n",
     "Xarray then aligns and broadcasts the two variables because they have different dimensions, and\n",
     "the result is a 4d variable with all the pointwise differences.\n",
     "\n",