Minor updates to regression part two slides (#116)

02_activities/assignments/assignment_2.ipynb

@@ -368,7 +368,7 @@
     "\n",
     "### Submission Parameters:\n",
     "* Submission Due Date: `HH:MM AM/PM - DD/MM/YYYY`\n",
-    "* The branch name for your repo should be: `assignment-1`\n",
+    "* The branch name for your repo should be: `assignment-2`\n",
     "* What to submit for this assignment:\n",
     "    * This Jupyter Notebook (assignment_1.ipynb) should be populated and should be the only change in your pull request.\n",
     "* What the pull request link should look like for this assignment: `https://github.com/<your_github_username>/applying_statistical_concepts/pull/<pr_id>`\n",
@@ -382,11 +382,19 @@
     "\n",
     "If you encounter any difficulties or have questions, please don't hesitate to reach out to our team via our Slack at `#cohort-4-help`. Our Technical Facilitators and Learning Support staff are here to help you navigate any challenges.\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08fd209a-a2ff-4c19-a94a-aeed1ea7c889",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.10.4",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -400,7 +408,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.19"
+   "version": "3.12.3"
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   "vscode": {
    "interpreter": {

03_instructional_team/markdown_slides/Regression_II.md

@@ -42,10 +42,6 @@ $$ y = mx + b $$
 - Linear regression addresses these limitations: better prediction range and faster performance with larger datasets.
 - Linear regression is widely used due to its interpretable mathematical equation linking predictors and response variables.
 - Simple linear regression involves one predictor and one response variable and **predicts by creating a straight line of best fit through the training data.**
----
-##### Animation of linear regression
-[![w:750 Watch on YouTube](https://img.youtube.com/vi/SWTeWu1rerk/0.jpg)](https://youtu.be/SWTeWu1rerk)
-https://www.youtube.com/watch?v=SWTeWu1rerk
 
 ---
  ##### Example dataset
@@ -122,9 +118,9 @@ $$
 $$
 
   where:
- $\beta_0$ is the vertical intercept (price when house size is 0).
-  $\beta_1$ is the slope (rate of price increase as house size increases).
-  $\beta_2$ is the slope (rate of price increase as number of bedrooms increases).
+ $\beta_0$ is the vertical intercept (price when house size is 0 and number of bedrooms is 0).
+  $\beta_1$ is the slope (rate of price increase as house size increases when fixing everything else).
+  $\beta_2$ is the slope (rate of price increase as number of bedrooms increases when fixing everything else).
 
 ---
 ![bg right:60% w:700](./images/multivar_linreg.jpg)