+ possibly persuaded the precommits to all pass

Author: jonsedar

examples/generalized_linear_models/GLM-ordinal-features.ipynb

@@ -11,7 +11,7 @@
     "\n",
     ":::{post} Oct 27, 2024\n",
     ":tags: ordinal-features, ordinal-regression, glm, bayesian-workflow, r-datasets\n",
-    ":category: beginner\n",
+    ":category: beginner, reference\n",
     ":author: Jonathan Sedar\n",
     ":::"
    ]
@@ -81,7 +81,7 @@
     "  + As a totally subjective opinion which might be different between observations e.g. \"bad, good, better, way better,\n",
     "    best, actually the best, magnificent\"  - these are difficult to work with and a symptom of poor survey design\n",
     "  + On a partially subjective, standardized scale e.g. \"strongly agree, agree, disagree, strongly disagree\" - this is\n",
-    "    the approach of the familar [Likert scale](https://en.wikipedia.org/wiki/Likert_scale))\n",
+    "    the approach of the familar [Likert scale](https://en.wikipedia.org/wiki/Likert_scale)\n",
     "  + As a summary binning of a metric scale e.g. binning ages into age-groups [<30, 30 - 60, 60+], or medical\n",
     "    self-scoring \"[0-10%, ..., 90-100%]\" - these are typically a misuse of the metric because the data has been\n",
     "    compressed: losing infomation, and reasoning for the binning and the choices of bin-edges are usually not given\n",

examples/generalized_linear_models/GLM-ordinal-features.myst.md

@@ -17,7 +17,7 @@ kernelspec:
 
 :::{post} Oct 27, 2024
 :tags: ordinal-features, ordinal-regression, glm, bayesian-workflow, r-datasets
-:category: beginner
+:category: beginner, reference
 :author: Jonathan Sedar
 :::
 
@@ -72,7 +72,7 @@ which we show in another notebook
   + As a totally subjective opinion which might be different between observations e.g. "bad, good, better, way better,
     best, actually the best, magnificent"  - these are difficult to work with and a symptom of poor survey design
   + On a partially subjective, standardized scale e.g. "strongly agree, agree, disagree, strongly disagree" - this is
-    the approach of the familar [Likert scale](https://en.wikipedia.org/wiki/Likert_scale))
+    the approach of the familar [Likert scale](https://en.wikipedia.org/wiki/Likert_scale)
   + As a summary binning of a metric scale e.g. binning ages into age-groups [<30, 30 - 60, 60+], or medical
     self-scoring "[0-10%, ..., 90-100%]" - these are typically a misuse of the metric because the data has been
     compressed: losing infomation, and reasoning for the binning and the choices of bin-edges are usually not given