add in some glossary terms to example notebooks

Author: drbenvincent

docs/source/glossary.rst

@@ -18,7 +18,7 @@ Glossary
    Change score analysis
       A statistical procedure where the outcome variable is the difference between the posttest and protest scores.
 
-   Comparative interrupted time-series design
+   Comparative interrupted time-series
    CITS
       An interrupted time series design with added comparison time series observations.
 
@@ -64,7 +64,7 @@ Glossary
    Sharp regression discontinuity design
       A Regression discontinuity design where allocation to treatment or control is determined by a sharp threshold / step function.
 
-   Synthetic control method
+   Synthetic control
       The synthetic control method is a statistical method used to evaluate the effect of an intervention in comparative case studies. It involves the construction of a weighted combination of groups used as controls, to which the treatment group is compared.
 
    Treatment on the treated effect
@@ -79,5 +79,5 @@ Glossary
 
 
 References
-==========
+----------
 .. footbibliography::

docs/source/notebooks/ancova_pymc.ipynb

@@ -11,7 +11,7 @@
     "This is a preliminary example based on synthetic data. It will hopefully soon be updated with data from a real study.\n",
     ":::\n",
     "\n",
-    "In cases where there is just one pre and one post treatment measurement, it we can analyse data from NEGD experiments using an ANCOVA type approach. The basic model is:\n",
+    "In cases where there is just one pre and one post treatment measurement, it we can analyse data from {term}`NEGD` experiments using an {term}`ANCOVA` type approach. The basic model is:\n",
     "\n",
     "$$\n",
     "post_i = \\beta_0 + (\\beta_1 \\cdot T_i) + (\\beta_2 \\cdot pre_i) + \\epsilon_i\n",

docs/source/notebooks/did_pymc_banks.ipynb

@@ -517,7 +517,7 @@
    "source": [
     "## Analysis 2 - DiD with multiple pre/post observations\n",
     "\n",
-    "Now we'll do a difference in differences analysis of the full dataset. This approach has similarities to CITS (Comparative Interrupted Time Series) with a single control over time. Although slightly abitrary, we distinguish between the two techniques on whether there is enough time series data for CITS to capture the time series patterns."
+    "Now we'll do a difference in differences analysis of the full dataset. This approach has similarities to {term}`CITS` (Comparative Interrupted Time-Series) with a single control over time. Although slightly abitrary, we distinguish between the two techniques on whether there is enough time series data for CITS to capture the time series patterns."
    ]
   },
   {
@@ -725,7 +725,7 @@
   "kernelspec": {
    "display_name": "CausalPy",
    "language": "python",
-   "name": "causalpy"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -737,12 +737,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "46d31859cc45aa26a1223a391e7cf3023d69984b498bed11e66c690302b7e251"
-   }
+   "version": "3.10.8"
   }
  },
  "nbformat": 4,

docs/source/notebooks/geolift1.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# Bayesian geolift with CausalPy\n",
     "\n",
-    "This notebook covers how to use `CausalPy`'s Bayesian synthetic control functionality to assess 'geolift'. Our hypothetical scenario is:\n",
+    "This notebook covers how to use `CausalPy`'s Bayesian {term}`synthetic control` functionality to assess 'geolift'. Our hypothetical scenario is:\n",
     "\n",
     "> We are a data scientist within a company that operates over Europe. We have been given a historical dataset of sales volumes, in units of 1000's. The data is broken down by country and was collected at weekly frequency. We have data for the past 4 years. \n",
     "\n",

docs/source/notebooks/sc_pymc_brexit.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# The effects of Brexit\n",
     "\n",
-    "The aim of this notebook is to estimate the causal impact of Brexit upon the UK's GDP. This will be done using the synthetic control approch. As such, it is similar to the policy brief \"What can we know about the cost of Brexit so far?\" {cite:p}`brexit2022policybrief` from the Center for European Reform. That approach did not use Bayesian estimation methods however.\n",
+    "The aim of this notebook is to estimate the causal impact of Brexit upon the UK's GDP. This will be done using the {term}`synthetic control` approch. As such, it is similar to the policy brief \"What can we know about the cost of Brexit so far?\" {cite:p}`brexit2022policybrief` from the Center for European Reform. That approach did not use Bayesian estimation methods however.\n",
     "\n",
     "I did not use the GDP data from the above report however as it had been scaled in some way that was hard for me to understand how it related to the absolute GDP figures. Instead, GDP data was obtained courtesy of Prof. Dooruj Rambaccussing. Raw data is in units of billions of USD."
    ]