update and simplify introduction

examples/generalized_linear_models/GLM-simpsons-paradox.ipynb

@@ -18,20 +18,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This notebook covers:\n",
-    "- [Simpson's Paradox](https://en.wikipedia.org/wiki/Simpson%27s_paradox) and its resolution through mixed or hierarchical models. This is a situation where there might be a negative relationship between two variables within a group, but when data from multiple groups are combined, that relationship may disappear or even reverse sign. The gif below (from the [Simpson's Paradox](https://en.wikipedia.org/wiki/Simpson%27s_paradox) Wikipedia page) demonstrates this very nicely.\n",
-    "- How to build linear regression models, starting with linear regression, moving up to hierarchical linear regression. Simpon's paradox is a nice motivation for why we might want to do this - but of course we should aim to build models which incorporate as much as our knowledge about the structure of the data (e.g. it's nested nature) as possible.\n",
-    "- Use of `pm.Data` containers to facilitate posterior prediction at different $x$ values with the same model.\n",
-    "- Providing array dimensions (see `coords`) to models to help with shape problems. This involves the use of [xarray](http://xarray.pydata.org/) and is quite helpful in multi-level / hierarchical models.\n",
-    "- Differences between posteriors and posterior predictive distributions.\n",
-    "- How to visualise models in data space and parameter space, using a mixture of [ArviZ](https://arviz-devs.github.io/arviz/) and [matplotlib](https://matplotlib.org/)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![](https://upload.wikimedia.org/wikipedia/commons/f/fb/Simpsons_paradox_-_animation.gif)"
+    "[Simpson's Paradox](https://en.wikipedia.org/wiki/Simpson%27s_paradox) describes a situation where there might be a negative relationship between two variables within a group, but when data from multiple groups are combined, that relationship may disappear or even reverse sign. The gif below (from the [Simpson's Paradox](https://en.wikipedia.org/wiki/Simpson%27s_paradox) Wikipedia page) demonstrates this very nicely.\n",
+    "\n",
+    "![](https://upload.wikimedia.org/wikipedia/commons/f/fb/Simpsons_paradox_-_animation.gif)\n",
+    "\n",
+    "This paradox can be resolved by assuming a causal DAG which includes how the main predictor variable _and_ group membership influence the outcome variable. We demonstrate an example where we _don't_ incorporate group membership (so our causal DAG is wrong, or in other words out model is misspecified). We then show 2 wayes to resolve this by including group membership as causal influence upon the outcome variable. This is shown in an unpooled model (which we could also call a fixed effects model) and a hierarchical model (which we could also call a mixed effects model)."
    ]
   },
   {

examples/generalized_linear_models/GLM-simpsons-paradox.myst.md

@@ -21,18 +21,12 @@ kernelspec:
 
 +++
 
-This notebook covers:
-- [Simpson's Paradox](https://en.wikipedia.org/wiki/Simpson%27s_paradox) and its resolution through mixed or hierarchical models. This is a situation where there might be a negative relationship between two variables within a group, but when data from multiple groups are combined, that relationship may disappear or even reverse sign. The gif below (from the [Simpson's Paradox](https://en.wikipedia.org/wiki/Simpson%27s_paradox) Wikipedia page) demonstrates this very nicely.
-- How to build linear regression models, starting with linear regression, moving up to hierarchical linear regression. Simpon's paradox is a nice motivation for why we might want to do this - but of course we should aim to build models which incorporate as much as our knowledge about the structure of the data (e.g. it's nested nature) as possible.
-- Use of `pm.Data` containers to facilitate posterior prediction at different $x$ values with the same model.
-- Providing array dimensions (see `coords`) to models to help with shape problems. This involves the use of [xarray](http://xarray.pydata.org/) and is quite helpful in multi-level / hierarchical models.
-- Differences between posteriors and posterior predictive distributions.
-- How to visualise models in data space and parameter space, using a mixture of [ArviZ](https://arviz-devs.github.io/arviz/) and [matplotlib](https://matplotlib.org/).
-
-+++
+[Simpson's Paradox](https://en.wikipedia.org/wiki/Simpson%27s_paradox) describes a situation where there might be a negative relationship between two variables within a group, but when data from multiple groups are combined, that relationship may disappear or even reverse sign. The gif below (from the [Simpson's Paradox](https://en.wikipedia.org/wiki/Simpson%27s_paradox) Wikipedia page) demonstrates this very nicely.
 
 ![](https://upload.wikimedia.org/wikipedia/commons/f/fb/Simpsons_paradox_-_animation.gif)
 
+This paradox can be resolved by assuming a causal DAG which includes how the main predictor variable _and_ group membership influence the outcome variable. We demonstrate an example where we _don't_ incorporate group membership (so our causal DAG is wrong, or in other words out model is misspecified). We then show 2 wayes to resolve this by including group membership as causal influence upon the outcome variable. This is shown in an unpooled model (which we could also call a fixed effects model) and a hierarchical model (which we could also call a mixed effects model).
+
 ```{code-cell} ipython3
 import arviz as az
 import graphviz as gr