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diff --git a/cfrr_program_details/code_of_conduct_violations.ipynb b/cfrr_program_details/code_of_conduct_violations.ipynb
index 504c7cd0..3b1048c0 100644
--- a/cfrr_program_details/code_of_conduct_violations.ipynb
+++ b/cfrr_program_details/code_of_conduct_violations.ipynb
@@ -8,7 +8,7 @@
     "# Report Code of Conduct Violations\n",
     "If you have recently experienced an incident violating the CfRR Code of Conduct, please fill out the form below. \n",
     "\n",
-    "<iframe width=\"100%\" height=\"1000px\" src=\"https://forms.office.com/Pages/ResponsePage.aspx?id=d10qkZj77k6vMhM02PBKU84YwThO-BpArj9MrG7uxJxUNFRZWkxBWUJEVzFaWUczNUpJSlMxSFVLTiQlQCN0PWcu&embed=true\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" style=\"border: none;\" allowfullscreen webkitallowfullscreen mozallowfullscreen msallowfullscreen> </iframe>"
+    "<iframe width=\"100%\" height=\"1000px\" title=\"Code of Conduct Violations Form\" src=\"https://forms.office.com/Pages/ResponsePage.aspx?id=d10qkZj77k6vMhM02PBKU84YwThO-BpArj9MrG7uxJxUNFRZWkxBWUJEVzFaWUczNUpJSlMxSFVLTiQlQCN0PWcu&embed=true\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" style=\"border: none;\" allowfullscreen webkitallowfullscreen mozallowfullscreen msallowfullscreen> </iframe>"
    ]
   },
   {
diff --git a/cfrr_program_details/course_feedback.ipynb b/cfrr_program_details/course_feedback.ipynb
index db899c90..b5f886eb 100644
--- a/cfrr_program_details/course_feedback.ipynb
+++ b/cfrr_program_details/course_feedback.ipynb
@@ -8,7 +8,7 @@
     "# Course Feedback\n",
     "If you have recently attended a Coding for Reproducible Research (CfRR) course, please fill out the following form to provide us with insight into how the course is being received by attendees. Having documented information about the quality of the CfRR courses is key to improving our course offer.\n",
     "\n",
-    "<iframe width=\"100%\" height=\"2450px\" src=\"https://forms.office.com/Pages/ResponsePage.aspx?id=d10qkZj77k6vMhM02PBKU1p5IkKpVbtNkGlRxe_xSUNUNERTSEY1SDRTTzBWSVQ3VkpBM0QyREI5SC4u&embed=true\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" style=\"border: none;\" allowfullscreen webkitallowfullscreen mozallowfullscreen msallowfullscreen> </iframe>"
+    "<iframe width=\"100%\" height=\"2450px\" title=\"Course Feedback Form\" src=\"https://forms.office.com/Pages/ResponsePage.aspx?id=d10qkZj77k6vMhM02PBKU1p5IkKpVbtNkGlRxe_xSUNUNERTSEY1SDRTTzBWSVQ3VkpBM0QyREI5SC4u&embed=true\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" style=\"border: none;\" allowfullscreen webkitallowfullscreen mozallowfullscreen msallowfullscreen> </iframe>"
    ]
   },
   {
diff --git a/contributing/community.ipynb b/contributing/community.ipynb
index 5d140942..6a0e0697 100644
--- a/contributing/community.ipynb
+++ b/contributing/community.ipynb
@@ -32,7 +32,7 @@
    "metadata": {},
    "source": [
     "## Sign-up Form \n",
-    "<iframe width=\"100%\" height=\"1000px\" src=\"https://forms.cloud.microsoft/Pages/ResponsePage.aspx?id=d10qkZj77k6vMhM02PBKU-oxqLMQ8w1AowxZDWbXZRhUNFJHMDBEVUVCV1VXNlk1U09OQ0VVNFhOMi4u&embed=true\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" style=\"border: none; max-width:100%; max-height:100vh\" allowfullscreen webkitallowfullscreen mozallowfullscreen msallowfullscreen> </iframe>"
+    "<iframe width=\"100%\" height=\"1000px\" title=\"Research Coding Community (RCC) Signup Form\" src=\"https://forms.cloud.microsoft/Pages/ResponsePage.aspx?id=d10qkZj77k6vMhM02PBKU-oxqLMQ8w1AowxZDWbXZRhUNFJHMDBEVUVCV1VXNlk1U09OQ0VVNFhOMi4u&embed=true\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" style=\"border: none; max-width:100%; max-height:100vh\" allowfullscreen webkitallowfullscreen mozallowfullscreen msallowfullscreen> </iframe>"
    ]
   },
   {
diff --git a/contributing/suggestions_form.ipynb b/contributing/suggestions_form.ipynb
index 5474ba99..fb140b9d 100644
--- a/contributing/suggestions_form.ipynb
+++ b/contributing/suggestions_form.ipynb
@@ -15,7 +15,7 @@
     "\n",
     "The Coding for Reproducible Research is a constantly evolving piece of work, and we are keen to develop the programme in response to community feedback to ensure we meet the needs of those engaging with the workshops. As such, if there is anything you would like to see included as part of the programme, please fill out the form below to let us know. \n",
     "\n",
-    "<iframe width=\"100%\" height=\"1000px\" src=\"https://forms.office.com/Pages/ResponsePage.aspx?id=d10qkZj77k6vMhM02PBKU1p5IkKpVbtNkGlRxe_xSUNUQUtYQTAyRlFCMkhONVRONlMzRlQ4SEU3WS4u&embed=true\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" style=\"border: none; max-width:100%; max-height:200vh\" allowfullscreen webkitallowfullscreen mozallowfullscreen msallowfullscreen> </iframe>"
+    "<iframe width=\"100%\" height=\"1000px\" title=\"Feedback Form\" src=\"https://forms.office.com/Pages/ResponsePage.aspx?id=d10qkZj77k6vMhM02PBKU1p5IkKpVbtNkGlRxe_xSUNUQUtYQTAyRlFCMkhONVRONlMzRlQ4SEU3WS4u&embed=true\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" style=\"border: none; max-width:100%; max-height:200vh\" allowfullscreen webkitallowfullscreen mozallowfullscreen msallowfullscreen> </iframe>"
    ]
   },
   {
@@ -31,6 +31,14 @@
    },
    "outputs": [],
    "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5c7c55c1-6902-47d8-82d0-1a499011b34a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/individual_modules/introduction_to_machine_learning/1_linear_regression.ipynb b/individual_modules/introduction_to_machine_learning/1_linear_regression.ipynb
index c5a01428..997eaa72 100644
--- a/individual_modules/introduction_to_machine_learning/1_linear_regression.ipynb
+++ b/individual_modules/introduction_to_machine_learning/1_linear_regression.ipynb
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -45,12 +45,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -83,13 +83,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-4 {\n",
+       "<style>#sk-container-id-1 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
        "  --sklearn-color-text: black;\n",
        "  --sklearn-color-line: gray;\n",
@@ -119,15 +119,15 @@
        "  }\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 {\n",
+       "#sk-container-id-1 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 pre {\n",
+       "#sk-container-id-1 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 input.sk-hidden--visually {\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -139,7 +139,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -147,7 +147,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-container {\n",
+       "#sk-container-id-1 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -157,7 +157,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -173,14 +173,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-4 div.sk-parallel-item::after {\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-parallel {\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -188,28 +188,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-parallel-item {\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-4 div.sk-serial {\n",
+       "#sk-container-id-1 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -227,14 +227,14 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-4 div.sk-toggleable {\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-4 label.sk-toggleable__label {\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
        "  display: block;\n",
        "  width: 100%;\n",
@@ -244,7 +244,7 @@
        "  text-align: center;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -252,13 +252,13 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-4 div.sk-toggleable__content {\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
        "  max-height: 0;\n",
        "  max-width: 0;\n",
        "  overflow: hidden;\n",
@@ -267,12 +267,12 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -280,79 +280,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
        "  max-height: 200px;\n",
        "  max-width: 100%;\n",
        "  overflow: auto;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-4 div.sk-label label {\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-4 div.sk-label label {\n",
+       "#sk-container-id-1 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
        "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-label-container {\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-4 div.sk-estimator {\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -362,18 +362,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-estimator.fitted {\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-4 div.sk-estimator:hover {\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -460,7 +460,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-4 a.estimator_doc_link {\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -475,31 +475,31 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "LinearRegression()"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -524,7 +524,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -548,14 +548,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Intercept: -4.9985770855532055\n"
+      "Intercept: -4.998577085553205\n"
      ]
     }
    ],
@@ -572,7 +572,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -601,12 +601,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -632,7 +632,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -651,12 +651,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -680,12 +680,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -722,12 +722,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -755,7 +755,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -791,7 +791,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -819,12 +819,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -857,7 +857,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -868,7 +868,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -879,7 +879,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -952,7 +952,7 @@
        "4  Hungary          31007.768407                5.6"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -963,9 +963,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Visualise the data\n",
     "lifesat_df.plot(\n",
@@ -976,9 +987,427 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Select a linear model and train it\n",
     "model = LinearRegression()\n",
@@ -987,9 +1416,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cyprus predicted life satisfaction: 6.302\n"
+     ]
+    }
+   ],
    "source": [
     "# Make a prediction for Cyprus, given GDP is USD 37655.2 in 2020\n",
     "X_new = [[37655.2]]\n",
@@ -1000,9 +1437,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Visualise the data\n",
     "lifesat_df.plot(\n",
@@ -1063,9 +1511,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(100, 2)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "X = np.hstack((X1, X2))\n",
     "X.shape"
@@ -1080,9 +1539,427 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-3 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-3 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-3 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-3 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "model = LinearRegression()\n",
     "model.fit(X, y)"
@@ -1097,9 +1974,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1.74745163, 2.01336064]])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "model.coef_"
    ]
@@ -1113,9 +2001,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1.78628323])"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "model.intercept_"
    ]
@@ -1143,9 +2042,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "22.60370518724289\n"
+     ]
+    }
+   ],
    "source": [
     "# Predict new y value from two independent variables\n",
     "y_pred_new = model.predict(X_new)[0][0]\n",
@@ -1162,7 +2069,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1171,7 +2078,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1216,13 +2123,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {
     "tags": [
      "remove-input"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style='text-align: center;'></div><iframe id='iframe-multiple_regression_hyperplane' title='Multiple Regression Hyperplane' src='../../_static/multiple_regression_hyperplane.html' width='100%' height='750px' style='border:none;'></iframe>\n",
+       "<script>\n",
+       "function adjustIframeHeight_iframe-multiple_regression_hyperplane() {\n",
+       "    const iframe = document.getElementById('iframe-multiple_regression_hyperplane');\n",
+       "    if (iframe) {\n",
+       "        iframe.style.height = iframe.offsetWidth + 'px';\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Adjust height on load and resize\n",
+       "window.onload = adjustIframeHeight_iframe-multiple_regression_hyperplane;\n",
+       "window.onresize = adjustIframeHeight_iframe-multiple_regression_hyperplane;\n",
+       "</script>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from IPython.display import HTML, display\n",
     "\n",
@@ -1234,7 +2166,7 @@
     "\n",
     "# Create a display HTML block with a title\n",
     "html_str = \"<div style='text-align: center;'></div>\"\n",
-    "html_str += f\"<iframe id='{iframe_id}' src='{file_name}' width='100%' height='750px' style='border:none;'></iframe>\"\n",
+    "html_str += f\"<iframe id='{iframe_id}' title='Multiple Regression Hyperplane' src='{file_name}' width='100%' height='750px' style='border:none;'></iframe>\"\n",
     "\n",
     "# Include JavaScript to adjust the iframe height based on its width (if desired)\n",
     "js_code = f\"\"\"\n",
@@ -1258,7 +2190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1308,13 +2240,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {
     "tags": [
      "remove-input"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style='text-align: center;'></div><iframe id='iframe-multiple_regression_hyperplane_2' src='../../_static/multiple_regression_hyperplane_2.html' title='Multiple Regression Hyperplane (with vertical residual lines and our added point' width='100%' height='750px' style='border:none;'></iframe>\n",
+       "<script>\n",
+       "function adjustIframeHeight_iframe-multiple_regression_hyperplane_2() {\n",
+       "    const iframe = document.getElementById('iframe-multiple_regression_hyperplane_2');\n",
+       "    if (iframe) {\n",
+       "        iframe.style.height = iframe.offsetWidth + 'px';\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Adjust height on load and resize\n",
+       "window.onload = adjustIframeHeight_iframe-multiple_regression_hyperplane_2;\n",
+       "window.onresize = adjustIframeHeight_iframe-multiple_regression_hyperplane_2;\n",
+       "</script>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from IPython.display import HTML, display\n",
     "\n",
@@ -1326,7 +2283,7 @@
     "\n",
     "# Create a display HTML block with a title\n",
     "html_str = \"<div style='text-align: center;'></div>\"\n",
-    "html_str += f\"<iframe id='{iframe_id}' src='{file_name}' width='100%' height='750px' style='border:none;'></iframe>\"\n",
+    "html_str += f\"<iframe id='{iframe_id}' src='{file_name}' title='Multiple Regression Hyperplane (with vertical residual lines and our added point' width='100%' height='750px' style='border:none;'></iframe>\"\n",
     "\n",
     "# Include JavaScript to adjust the iframe height based on its width (if desired)\n",
     "js_code = f\"\"\"\n",
diff --git a/individual_modules/parallel_computing/multiprocessing_cpu.ipynb b/individual_modules/parallel_computing/multiprocessing_cpu.ipynb
index 94094762..8d8840e6 100644
--- a/individual_modules/parallel_computing/multiprocessing_cpu.ipynb
+++ b/individual_modules/parallel_computing/multiprocessing_cpu.ipynb
@@ -37,7 +37,7 @@
    "source": [
     "### The multithreading example\n",
     "\n",
-    "[Download complete threaded cpu_bound example file](complete_files/multithreading_cpu_bound_complete.py)"
+    "[Download complete threaded cpu_bound example file](complete_files/multiprocessing_cpu_bound_complete.py)"
    ]
   },
   {
@@ -68,7 +68,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.1"
+   "version": "3.9.19"
   }
  },
  "nbformat": 4,
diff --git a/short_courses/r_environments.ipynb b/short_courses/r_environments.ipynb
index f38df20a..d598933f 100644
--- a/short_courses/r_environments.ipynb
+++ b/short_courses/r_environments.ipynb
@@ -8,7 +8,7 @@
     "\n",
     "```{card}\n",
     "\n",
-    "<div class=\"alert alert-block\" style=\"background-color: #f2f2f2;\">\n",
+    "<div class=\"alert alert-block\" style=\"background-color: #ffffff; border: 1px solid #ccc; padding: 1em; border-radius: 8px;\">\n",
     "\n",
     "**Author:** Amy Heather (GitHub: [amyheather](https://github.com/amyheather), ORCID: [0000-0002-6596-3479](https://orcid.org/0000-0002-6596-3479)).\n",
     "\n",
diff --git a/where_is_my_understanding/quizes/regression_analysis_with_R.json b/where_is_my_understanding/quizes/regression_analysis_with_R.json
index 52be8fa2..bc550842 100644
--- a/where_is_my_understanding/quizes/regression_analysis_with_R.json
+++ b/where_is_my_understanding/quizes/regression_analysis_with_R.json
@@ -1,6 +1,6 @@
 [
     {
-        "question": "Which of these are reasons to do regression analysis? Select all that apply.",
+        "question": "Which of these are reasons to do regression analysis?",
         "type": "many_choice",
         "answers": [
             {
@@ -9,12 +9,7 @@
                 "feedback": "Incorrect."
             },
             {
-                "answer": "Making predictions",
-                "correct": true,
-                "feedback": "Correct."
-            },
-            {
-                "answer": "Hypothesis testing",
+                "answer": "Making predictions and Hypothesis testing",
                 "correct": true,
                 "feedback": "Correct."
             },
@@ -62,7 +57,7 @@
             },
             {
                 "answer": "The mean of the outcome when all predictor variables equal 1.",
-                "correct": true,
+                "correct": false,
                 "feedback": "Correct."
             }
         ]
@@ -197,12 +192,7 @@
         "type": "many_choice",
         "answers": [
             {
-                "answer": "$y ~ 1",
-                "correct": true,
-                "feedback": "Correct."
-            },
-            {
-                "answer": "$y ~ age + sex",
+                "answer": "$y ~ 1, $y ~ education_years + age, $y ~ age + sex",
                 "correct": true,
                 "feedback": "Correct."
             },
@@ -211,11 +201,6 @@
                 "correct": false,
                 "feedback": "Incorrect."
             },
-            {
-                "answer": "$y ~ education_years + age",
-                "correct": true,
-                "feedback": "Correct."
-            },
             {
                 "answer": "$y ~ age + sex + education_years$",
                 "correct": false,
diff --git a/where_is_my_understanding/quizes/using_markdown_in_python.json b/where_is_my_understanding/quizes/using_markdown_in_python.json
index ff38721d..30d63a81 100644
--- a/where_is_my_understanding/quizes/using_markdown_in_python.json
+++ b/where_is_my_understanding/quizes/using_markdown_in_python.json
@@ -207,32 +207,6 @@
             }
         ]
     },
-    {
-        "question": "Which of the following is a valid way to insert an image in Markdown?",
-        "type": "many_choice",
-        "answers": [
-            {
-                "answer": "[image](url)",
-                "correct": false,
-                "feedback": "Incorrect. This is link syntax, not image."
-            },
-            {
-                "answer": "<img src=\"url\">",
-                "correct": false,
-                "feedback": "Incorrect. This is HTML, not Markdown."
-            },
-            {
-                "answer": "![alt text](url)",
-                "correct": true,
-                "feedback": "Correct. This is the right Markdown image syntax."
-            },
-            {
-                "answer": "{image: url}",
-                "correct": false,
-                "feedback": "Incorrect. Not valid Markdown."
-            }
-        ]
-    },
     {
         "question": "What is the recommended way to save and export a Jupyter Notebook as a static, shareable web page?",
         "type": "many_choice",
diff --git a/where_is_my_understanding/using_markdown_in_python.ipynb b/where_is_my_understanding/using_markdown_in_python.ipynb
index 72cf7ce9..ab3c4b7b 100644
--- a/where_is_my_understanding/using_markdown_in_python.ipynb
+++ b/where_is_my_understanding/using_markdown_in_python.ipynb
@@ -17,7 +17,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "87f61215-5388-45ca-9f09-05bff13081bd",
    "metadata": {
     "editable": true,
@@ -32,13 +32,13 @@
     {
      "data": {
       "text/html": [
-       "<div id=\"KwBgXCiVPTed\" data-shufflequestions=\"False\"\n",
+       "<div id=\"yAJbdECaRGYq\" data-shufflequestions=\"False\"\n",
        "               data-shuffleanswers=\"True\"\n",
        "               data-preserveresponses=\"false\"\n",
        "               data-numquestions=\"1000000\"\n",
        "               data-maxwidth=\"600\"\n",
        "               style=\"border-radius: 10px; text-align: left\"> <style>\n",
-       "#KwBgXCiVPTed {\n",
+       "#yAJbdECaRGYq {\n",
        "   --jq-multiple-choice-bg: #6f78ffff;\n",
        "   --jq-mc-button-bg: #fafafa;\n",
        "   --jq-mc-button-border: #e0e0e0e0;\n",
@@ -220,7 +220,7 @@
     {
      "data": {
       "application/javascript": [
-       "var questionsKwBgXCiVPTed=[\n",
+       "var questionsyAJbdECaRGYq=[\n",
        "    {\n",
        "        \"question\": \"What is a markup language primarily used for?\",\n",
        "        \"type\": \"many_choice\",\n",
@@ -430,32 +430,6 @@
        "        ]\n",
        "    },\n",
        "    {\n",
-       "        \"question\": \"Which of the following is a valid way to insert an image in Markdown?\",\n",
-       "        \"type\": \"many_choice\",\n",
-       "        \"answers\": [\n",
-       "            {\n",
-       "                \"answer\": \"[image](url)\",\n",
-       "                \"correct\": false,\n",
-       "                \"feedback\": \"Incorrect. This is link syntax, not image.\"\n",
-       "            },\n",
-       "            {\n",
-       "                \"answer\": \"<img src=\\\"url\\\">\",\n",
-       "                \"correct\": false,\n",
-       "                \"feedback\": \"Incorrect. This is HTML, not Markdown.\"\n",
-       "            },\n",
-       "            {\n",
-       "                \"answer\": \"![alt text](url)\",\n",
-       "                \"correct\": true,\n",
-       "                \"feedback\": \"Correct. This is the right Markdown image syntax.\"\n",
-       "            },\n",
-       "            {\n",
-       "                \"answer\": \"{image: url}\",\n",
-       "                \"correct\": false,\n",
-       "                \"feedback\": \"Incorrect. Not valid Markdown.\"\n",
-       "            }\n",
-       "        ]\n",
-       "    },\n",
-       "    {\n",
        "        \"question\": \"What is the recommended way to save and export a Jupyter Notebook as a static, shareable web page?\",\n",
        "        \"type\": \"many_choice\",\n",
        "        \"answers\": [\n",
@@ -1231,8 +1205,8 @@
        "           Someone more knowledgeable could make this better ;-) */\n",
        "\n",
        "        function try_show() {\n",
-       "          if(document.getElementById(\"KwBgXCiVPTed\")) {\n",
-       "            show_questions(questionsKwBgXCiVPTed,  KwBgXCiVPTed); \n",
+       "          if(document.getElementById(\"yAJbdECaRGYq\")) {\n",
+       "            show_questions(questionsyAJbdECaRGYq,  yAJbdECaRGYq); \n",
        "          } else {\n",
        "             setTimeout(try_show, 200);\n",
        "          }\n",
@@ -1241,8 +1215,8 @@
        "        {\n",
        "        // console.log(element);\n",
        "\n",
-       "        //console.log(\"KwBgXCiVPTed\");\n",
-       "        // console.log(document.getElementById(\"KwBgXCiVPTed\"));\n",
+       "        //console.log(\"yAJbdECaRGYq\");\n",
+       "        // console.log(document.getElementById(\"yAJbdECaRGYq\"));\n",
        "\n",
        "        try_show();\n",
        "        }\n",
