diff --git a/content/GLM_Single_Subject_Model.ipynb b/content/GLM_Single_Subject_Model.ipynb
index 25df90f..68d951e 100755
--- a/content/GLM_Single_Subject_Model.ipynb
+++ b/content/GLM_Single_Subject_Model.ipynb
@@ -659,7 +659,7 @@
    "outputs": [
     {
      "data": {
-      "image/jpeg": 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",
       "text/html": [
        "\n",
        "        <iframe\n",
@@ -1111,6 +1111,26 @@
     "dm_conv_filt_poly_cov.heatmap(cmap='RdBu_r', vmin=-1,vmax=1)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "```{note}\n",
+    "Multicollineary as discussed above can lead to unstable estimates of the regression coefficients, particularly for the regressors of interest. Multicollinearity between covariates of no interest tends to be less of a problem because we generally are just interested in explaining noise in our model and are rarely interested in interpreting the individual covariate beta coefficients. However, in cases of extreme collinearity, the model may become rank deficient, which can lead to difficulty with even fitting the model. \n",
+    "\n",
+    "A simple fix to this problem is to use the `.clean()` method. This method will remove any columns that are perfectly collinear with other columns in the design matrix.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1617,7 +1637,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.0"
+   "version": "3.12.2"
   },
   "toc": {
    "base_numbering": 1,