TR edits to Chapter 4

DS-100 · Apr 26, 2023 · acee66c · acee66c
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diff --git a/content/ch/04/modeling_intro.ipynb b/content/ch/04/modeling_intro.ipynb
@@ -12,7 +12,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We have seen in {numref}`Chapter %s <ch:data_scope>`  the importance of data scope and in {numref}`Chapter %s <ch:theory_datadesign>` the importance of data generation mechanisms, such as one that can be represented by an urn model.  Urn models address one aspect of modeling: they describe chance variation and ensure the data are representative of the target. Good scope and representative data lay the groundwork for extracting useful information from data, which is the other part of modeling.  This information is often referred to as the *signal* in the data. We use models to approximate the signal with the simplest of these being the constant model, where the signal is approximated by a single number, like the mean or median. Other more complex models summarize relationships between features in the data, such as humidity and particulate matter in air quality ({numref}`Chapter %s <ch:pa>`), upward mobility and commute time in communities ({numref}`Chapter %s <ch:linear>`), height and weight of animals({numref}`Chapter %s <ch:donkey>`). These more complex models are also approximations built from data.\n",
+    "We have seen in {numref}`Chapter %s <ch:data_scope>`  the importance of data scope and in {numref}`Chapter %s <ch:theory_datadesign>` the importance of data generation mechanisms, such as one that can be represented by an urn model.  Urn models address one aspect of modeling: they describe chance variation and ensure the data are representative of the target. Good scope and representative data lay the groundwork for extracting useful information from data, which is the other part of modeling.  This information is often referred to as the *signal* in the data. We use models to approximate the signal with the simplest of these being the constant model, where the signal is approximated by a single number, like the mean or median. Other more complex models summarize relationships between features in the data, such as humidity and particulate matter in air quality ({numref}`Chapter %s <ch:pa>`), upward mobility and commute time in communities ({numref}`Chapter %s <ch:linear>`), height and weight of animals ({numref}`Chapter %s <ch:donkey>`). These more complex models are also approximations built from data.\n",
     "When a model fits the data well, it can provide a useful approximation to the world or simply a helpful description of the data. "
    ]
   },