diff --git a/docs/gradient_descent/gradient_descent.html b/docs/gradient_descent/gradient_descent.html
index 7796f96c..733f829d 100644
--- a/docs/gradient_descent/gradient_descent.html
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diff --git a/docs/intro_to_modeling/intro_to_modeling_files/figure-html/cell-2-output-1.png b/docs/intro_to_modeling/intro_to_modeling_files/figure-html/cell-2-output-1.png
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Binary files a/docs/intro_to_modeling/intro_to_modeling_files/figure-html/cell-2-output-1.png and b/docs/intro_to_modeling/intro_to_modeling_files/figure-html/cell-2-output-1.png differ
diff --git a/docs/pandas_2/pandas_2.html b/docs/pandas_2/pandas_2.html
index 4532e031..e77f8a65 100644
--- a/docs/pandas_2/pandas_2.html
+++ b/docs/pandas_2/pandas_2.html
@@ -1650,12 +1650,12 @@ <h3 data-number="3.3.4" class="anchored" data-anchor-id="sample"><span class="he
 </thead>
 <tbody>
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-<td data-quarto-table-cell-role="th">23555</td>
+<td data-quarto-table-cell-role="th">366772</td>
 <td>CA</td>
-<td>F</td>
-<td>1946</td>
-<td>Richard</td>
-<td>7</td>
+<td>M</td>
+<td>2008</td>
+<td>Neeraj</td>
+<td>5</td>
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 </tbody>
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-<td>2002</td>
-<td>Devina</td>
-<td>7</td>
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+<td>2004</td>
+<td>Vicki</td>
+<td>8</td>
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-<td data-quarto-table-cell-role="th">291371</td>
-<td>1975</td>
-<td>Sunny</td>
-<td>5</td>
+<td data-quarto-table-cell-role="th">210217</td>
+<td>2015</td>
+<td>Mabel</td>
+<td>63</td>
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-<td>1921</td>
-<td>Verne</td>
-<td>9</td>
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+<td>1950</td>
+<td>Quentin</td>
+<td>5</td>
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-<td>2015</td>
-<td>Noam</td>
-<td>15</td>
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+<td>2012</td>
+<td>Vienna</td>
+<td>55</td>
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-<td>2015</td>
-<td>Alysson</td>
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+<td>1978</td>
+<td>Letisha</td>
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-<td data-quarto-table-cell-role="th">343844</td>
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+<td data-quarto-table-cell-role="th">152423</td>
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-<td>6</td>
+<td>Deven</td>
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-<td>Jaeden</td>
-<td>5</td>
+<td>Chadwick</td>
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diff --git a/docs/pandas_3/pandas_3.html b/docs/pandas_3/pandas_3.html
index 470669d6..dfe09419 100644
--- a/docs/pandas_3/pandas_3.html
+++ b/docs/pandas_3/pandas_3.html
@@ -942,7 +942,7 @@ <h2 data-number="4.2" class="anchored" data-anchor-id="aggregating-data-with-.gr
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 <div class="sourceCode cell-code" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>babynames.groupby(<span class="st">"Year"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="8">
-<pre><code>&lt;pandas.core.groupby.generic.DataFrameGroupBy object at 0x105ce2ac0&gt;</code></pre>
+<pre><code>&lt;pandas.core.groupby.generic.DataFrameGroupBy object at 0x104479df0&gt;</code></pre>
 </div>
 </div>
 <p>What does this strange output mean? Calling <code>.groupby</code> <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html">(documentation)</a> has generated a <code>GroupBy</code> object. You can imagine this as a set of “mini” sub-<code>DataFrame</code>s, where each subframe contains all of the rows from <code>babynames</code> that correspond to a particular year.</p>
@@ -1477,9 +1477,9 @@ <h3 data-number="4.2.2" class="anchored" data-anchor-id="plotting-birth-counts">
 </details>
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index 2b653b42..ea3bd51d 100644
--- a/docs/probability_2/probability_2.html
+++ b/docs/probability_2/probability_2.html
@@ -551,7 +551,7 @@ <h3 data-number="18.3.1" class="anchored" data-anchor-id="prediction-as-estimati
 <p align="center">
 <img src="images/y_hat.png" alt="y_hat" width="600">
 </p>
-<p>When building models it is also important to note that our choice of features will also signficantly impact our estimation. In the plot below, you can see how the different models (green and purple) can lead to different estimates.</p>
+<p>When building models, it is also important to note that our choice of features will also significantly impact our estimation. In the plot below, you can see how the different models (green and purple) can lead to different estimates.</p>
 <p align="center">
 <img src="images/y_hat2.png" alt="y_hat" width="400">
 </p>
@@ -1332,7 +1332,7 @@ <h3 data-number="18.5.5" class="anchored" data-anchor-id="step-4-bias-variance-d
 <span id="cb1-206"><a href="#cb1-206" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;img</span> <span class="er">src</span><span class="ot">=</span><span class="st">"images/y_hat.png"</span> <span class="er">alt</span><span class="ot">=</span><span class="st">'y_hat'</span> <span class="er">width</span><span class="ot">=</span><span class="st">'600'</span><span class="kw">&gt;</span></span>
 <span id="cb1-207"><a href="#cb1-207" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;/p&gt;</span></span>
 <span id="cb1-208"><a href="#cb1-208" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-209"><a href="#cb1-209" aria-hidden="true" tabindex="-1"></a>When building models it is also important to note that our choice of features will also signficantly impact our estimation. In the plot below, you can see how the different models (green and purple) can lead to different estimates.</span>
+<span id="cb1-209"><a href="#cb1-209" aria-hidden="true" tabindex="-1"></a>When building models, it is also important to note that our choice of features will also significantly impact our estimation. In the plot below, you can see how the different models (green and purple) can lead to different estimates.</span>
 <span id="cb1-210"><a href="#cb1-210" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb1-211"><a href="#cb1-211" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;p</span> <span class="er">align</span><span class="ot">=</span><span class="st">"center"</span><span class="kw">&gt;</span></span>
 <span id="cb1-212"><a href="#cb1-212" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;img</span> <span class="er">src</span><span class="ot">=</span><span class="st">"images/y_hat2.png"</span> <span class="er">alt</span><span class="ot">=</span><span class="st">'y_hat'</span> <span class="er">width</span><span class="ot">=</span><span class="st">'400'</span><span class="kw">&gt;</span></span>
diff --git a/docs/regex/regex.html b/docs/regex/regex.html
index aff5ed4c..d51c80a3 100644
--- a/docs/regex/regex.html
+++ b/docs/regex/regex.html
@@ -686,11 +686,11 @@ <h4 data-number="6.2.1.2" class="anchored" data-anchor-id="canonicalization-with
 <span id="cb6-13"><a href="#cb6-13" aria-hidden="true" tabindex="-1"></a>county_and_state[<span class="st">'clean_county_pandas'</span>] <span class="op">=</span> canonicalize_county_series(county_and_state[<span class="st">'County'</span>])</span>
 <span id="cb6-14"><a href="#cb6-14" aria-hidden="true" tabindex="-1"></a>display(county_and_pop), display(county_and_state)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stderr">
-<pre><code>/var/folders/7t/zbwy02ts2m7cn64fvwjqb8xw0000gp/T/ipykernel_45980/2523629438.py:3: FutureWarning:
+<pre><code>/var/folders/7t/zbwy02ts2m7cn64fvwjqb8xw0000gp/T/ipykernel_49929/2523629438.py:3: FutureWarning:
 
 The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.
 
-/var/folders/7t/zbwy02ts2m7cn64fvwjqb8xw0000gp/T/ipykernel_45980/2523629438.py:3: FutureWarning:
+/var/folders/7t/zbwy02ts2m7cn64fvwjqb8xw0000gp/T/ipykernel_49929/2523629438.py:3: FutureWarning:
 
 The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.
 </code></pre>
diff --git a/docs/sampling/sampling.html b/docs/sampling/sampling.html
index 606c5983..11e44a91 100644
--- a/docs/sampling/sampling.html
+++ b/docs/sampling/sampling.html
@@ -704,7 +704,7 @@ <h4 data-number="9.3.3.3" class="anchored" data-anchor-id="simple-random-sample"
 <span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a>random_sample <span class="op">=</span> movie.sample(n, replace <span class="op">=</span> <span class="va">False</span>) <span class="co">## By default, replace = False</span></span>
 <span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a>np.mean(random_sample[<span class="st">"barbie"</span>])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="9">
-<pre><code>0.5304121359169273</code></pre>
+<pre><code>0.5296914823759864</code></pre>
 </div>
 </div>
 <p>This is very close to the actual vote of 0.5302792307692308!</p>
@@ -722,7 +722,7 @@ <h4 data-number="9.3.3.3" class="anchored" data-anchor-id="simple-random-sample"
 <span id="cb15-10"><a href="#cb15-10" aria-hidden="true" tabindex="-1"></a>Markdown(<span class="ss">f"**Actual** = </span><span class="sc">{</span>actual_barbie<span class="sc">:.4f}</span><span class="ss">, **Sample** = </span><span class="sc">{</span>sample_barbie<span class="sc">:.4f}</span><span class="ss">, "</span></span>
 <span id="cb15-11"><a href="#cb15-11" aria-hidden="true" tabindex="-1"></a>         <span class="ss">f"**Err** = </span><span class="sc">{</span><span class="dv">100</span><span class="op">*</span>err<span class="sc">:.2f}</span><span class="ss">%."</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="10">
-<p><strong>Actual</strong> = 0.5303, <strong>Sample</strong> = 0.4975, <strong>Err</strong> = 6.18%.</p>
+<p><strong>Actual</strong> = 0.5303, <strong>Sample</strong> = 0.5337, <strong>Err</strong> = 0.65%.</p>
 </div>
 </div>
 <p>We’ll learn how to choose this number when we (re)learn the Central Limit Theorem later in the semester.</p>
@@ -755,7 +755,7 @@ <h4 data-number="9.3.3.4" class="anchored" data-anchor-id="quantifying-chance-er
 <div class="sourceCode cell-code" id="cb18"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a>poll_result <span class="op">=</span> pd.Series(poll_result)</span>
 <span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a>np.<span class="bu">sum</span>(poll_result <span class="op">&gt;</span> <span class="fl">0.5</span>)<span class="op">/</span><span class="dv">1000</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="13">
-<pre><code>0.964</code></pre>
+<pre><code>0.951</code></pre>
 </div>
 </div>
 <p>You can see the curve looks roughly Gaussian/normal. Using KDE:</p>
diff --git a/docs/sampling/sampling_files/figure-html/cell-13-output-1.png b/docs/sampling/sampling_files/figure-html/cell-13-output-1.png
index 87910cf6..e5948c6b 100644
Binary files a/docs/sampling/sampling_files/figure-html/cell-13-output-1.png and b/docs/sampling/sampling_files/figure-html/cell-13-output-1.png differ
diff --git a/docs/sampling/sampling_files/figure-html/cell-15-output-1.png b/docs/sampling/sampling_files/figure-html/cell-15-output-1.png
index 9caee37b..52c08dfa 100644
Binary files a/docs/sampling/sampling_files/figure-html/cell-15-output-1.png and b/docs/sampling/sampling_files/figure-html/cell-15-output-1.png differ
diff --git a/docs/sql_I/sql_I.html b/docs/sql_I/sql_I.html
index ae1ce40e..2db8bb31 100644
--- a/docs/sql_I/sql_I.html
+++ b/docs/sql_I/sql_I.html
@@ -723,10 +723,10 @@ <h4 data-number="20.4.1.3" class="anchored" data-anchor-id="uniqueness-with-dist
 <td>2011</td>
 </tr>
 <tr class="even">
-<td>2019</td>
+<td>2010</td>
 </tr>
 <tr class="odd">
-<td>2010</td>
+<td>2019</td>
 </tr>
 </tbody>
 </table>
@@ -1208,13 +1208,13 @@ <h2 data-number="20.5" class="anchored" data-anchor-id="aggregating-with-group-b
 <td>30</td>
 </tr>
 <tr class="even">
-<td>dessert</td>
-<td>5</td>
-</tr>
-<tr class="odd">
 <td>appetizer</td>
 <td>12</td>
 </tr>
+<tr class="odd">
+<td>dessert</td>
+<td>5</td>
+</tr>
 </tbody>
 </table>
 
@@ -1253,10 +1253,10 @@ <h2 data-number="20.5" class="anchored" data-anchor-id="aggregating-with-group-b
 </thead>
 <tbody>
 <tr class="odd">
-<td>dessert</td>
-<td>5</td>
-<td>5</td>
-<td>ice cream</td>
+<td>entree</td>
+<td>30</td>
+<td>7</td>
+<td>taco</td>
 </tr>
 <tr class="even">
 <td>appetizer</td>
@@ -1265,10 +1265,10 @@ <h2 data-number="20.5" class="anchored" data-anchor-id="aggregating-with-group-b
 <td>potsticker</td>
 </tr>
 <tr class="odd">
-<td>entree</td>
-<td>30</td>
-<td>7</td>
-<td>taco</td>
+<td>dessert</td>
+<td>5</td>
+<td>5</td>
+<td>ice cream</td>
 </tr>
 </tbody>
 </table>
@@ -1295,16 +1295,16 @@ <h2 data-number="20.5" class="anchored" data-anchor-id="aggregating-with-group-b
 </thead>
 <tbody>
 <tr class="odd">
-<td>2010</td>
+<td>2011</td>
 <td>2</td>
 </tr>
 <tr class="even">
-<td>2019</td>
-<td>1</td>
+<td>2010</td>
+<td>2</td>
 </tr>
 <tr class="odd">
-<td>2011</td>
-<td>2</td>
+<td>2019</td>
+<td>1</td>
 </tr>
 </tbody>
 </table>
diff --git a/docs/visualization_2/visualization_2_files/figure-html/cell-17-output-2.png b/docs/visualization_2/visualization_2_files/figure-html/cell-17-output-2.png
index 5bed1ea9..c4490344 100644
Binary files a/docs/visualization_2/visualization_2_files/figure-html/cell-17-output-2.png and b/docs/visualization_2/visualization_2_files/figure-html/cell-17-output-2.png differ
diff --git a/index.log b/index.log
index db3f2c5f..fb03d54f 100644
--- a/index.log
+++ b/index.log
@@ -1,4 +1,4 @@
-This is XeTeX, Version 3.141592653-2.6-0.999995 (TeX Live 2023) (preloaded format=xelatex 2024.3.3)  2 APR 2024 11:50
+This is XeTeX, Version 3.141592653-2.6-0.999995 (TeX Live 2023) (preloaded format=xelatex 2024.3.3)  2 APR 2024 17:00
 entering extended mode
  restricted \write18 enabled.
  %&-line parsing enabled.
diff --git a/index.pdf b/index.pdf
index 33a407cb..a9b52cd2 100644
Binary files a/index.pdf and b/index.pdf differ
diff --git a/index.tex b/index.tex
index 91d33191..57c466bc 100644
--- a/index.tex
+++ b/index.tex
@@ -220,7 +220,7 @@
 
 \begin{document}
 \maketitle
-\ifdefined\Shaded\renewenvironment{Shaded}{\begin{tcolorbox}[frame hidden, breakable, sharp corners, interior hidden, enhanced, borderline west={3pt}{0pt}{shadecolor}, boxrule=0pt]}{\end{tcolorbox}}\fi
+\ifdefined\Shaded\renewenvironment{Shaded}{\begin{tcolorbox}[breakable, borderline west={3pt}{0pt}{shadecolor}, interior hidden, enhanced, sharp corners, boxrule=0pt, frame hidden]}{\end{tcolorbox}}\fi
 
 \renewcommand*\contentsname{Table of contents}
 {
@@ -262,7 +262,7 @@ \section*{About the Course Notes}\label{about-the-course-notes}}
 \hypertarget{introduction}{%
 \chapter{Introduction}\label{introduction}}
 
-\begin{tcolorbox}[enhanced jigsaw, leftrule=.75mm, toptitle=1mm, colframe=quarto-callout-note-color-frame, opacityback=0, bottomtitle=1mm, colback=white, opacitybacktitle=0.6, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, breakable, arc=.35mm, left=2mm, colbacktitle=quarto-callout-note-color!10!white, titlerule=0mm, rightrule=.15mm, toprule=.15mm, bottomrule=.15mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-note-color-frame, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, toprule=.15mm, colbacktitle=quarto-callout-note-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 \begin{itemize}
 \tightlist
@@ -316,7 +316,7 @@ \chapter{Introduction}\label{introduction}}
 allowing you to take data and produce useful insights on the world's
 most challenging and ambiguous problems.
 
-\begin{tcolorbox}[enhanced jigsaw, leftrule=.75mm, toptitle=1mm, colframe=quarto-callout-note-color-frame, opacityback=0, bottomtitle=1mm, colback=white, opacitybacktitle=0.6, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Course Goals}, breakable, arc=.35mm, left=2mm, colbacktitle=quarto-callout-note-color!10!white, titlerule=0mm, rightrule=.15mm, toprule=.15mm, bottomrule=.15mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-note-color-frame, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Course Goals}, toprule=.15mm, colbacktitle=quarto-callout-note-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 \begin{itemize}
 \tightlist
@@ -334,7 +334,7 @@ \chapter{Introduction}\label{introduction}}
 
 \end{tcolorbox}
 
-\begin{tcolorbox}[enhanced jigsaw, leftrule=.75mm, toptitle=1mm, colframe=quarto-callout-note-color-frame, opacityback=0, bottomtitle=1mm, colback=white, opacitybacktitle=0.6, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Some Topics We'll Cover}, breakable, arc=.35mm, left=2mm, colbacktitle=quarto-callout-note-color!10!white, titlerule=0mm, rightrule=.15mm, toprule=.15mm, bottomrule=.15mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-note-color-frame, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Some Topics We'll Cover}, toprule=.15mm, colbacktitle=quarto-callout-note-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 \begin{itemize}
 \tightlist
@@ -364,7 +364,7 @@ \chapter{Introduction}\label{introduction}}
 
 \end{tcolorbox}
 
-\begin{tcolorbox}[enhanced jigsaw, leftrule=.75mm, toptitle=1mm, colframe=quarto-callout-note-color-frame, opacityback=0, bottomtitle=1mm, colback=white, opacitybacktitle=0.6, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Prerequisites}, breakable, arc=.35mm, left=2mm, colbacktitle=quarto-callout-note-color!10!white, titlerule=0mm, rightrule=.15mm, toprule=.15mm, bottomrule=.15mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-note-color-frame, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Prerequisites}, toprule=.15mm, colbacktitle=quarto-callout-note-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 To ensure that you can get the most out of the course content, please
 make sure that you are familiar with:
@@ -602,7 +602,7 @@ \section{Conclusion}\label{conclusion}}
 \hypertarget{pandas-i}{%
 \chapter{Pandas I}\label{pandas-i}}
 
-\begin{tcolorbox}[enhanced jigsaw, leftrule=.75mm, toptitle=1mm, colframe=quarto-callout-note-color-frame, opacityback=0, bottomtitle=1mm, colback=white, opacitybacktitle=0.6, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, breakable, arc=.35mm, left=2mm, colbacktitle=quarto-callout-note-color!10!white, titlerule=0mm, rightrule=.15mm, toprule=.15mm, bottomrule=.15mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-note-color-frame, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, toprule=.15mm, colbacktitle=quarto-callout-note-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 \begin{itemize}
 \tightlist
@@ -3276,7 +3276,7 @@ \section{Parting Note}\label{parting-note}}
 \hypertarget{pandas-ii}{%
 \chapter{Pandas II}\label{pandas-ii}}
 
-\begin{tcolorbox}[enhanced jigsaw, leftrule=.75mm, toptitle=1mm, colframe=quarto-callout-note-color-frame, opacityback=0, bottomtitle=1mm, colback=white, opacitybacktitle=0.6, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, breakable, arc=.35mm, left=2mm, colbacktitle=quarto-callout-note-color!10!white, titlerule=0mm, rightrule=.15mm, toprule=.15mm, bottomrule=.15mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-note-color-frame, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, toprule=.15mm, colbacktitle=quarto-callout-note-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 \begin{itemize}
 \tightlist
@@ -4137,9 +4137,9 @@ \subsection{\texorpdfstring{\texttt{.sample()}}{.sample()}}\label{sample}}
 
 \begin{tabular}{lllrlr}
 \toprule
-{} & State & Sex &  Year &  Name &  Count \\
+{} & State & Sex &  Year &    Name &  Count \\
 \midrule
-245530 &    CA &   M &  1925 &  Jack &    751 \\
+365418 &    CA &   M &  2008 &  Rayyan &     13 \\
 \bottomrule
 \end{tabular}
 
@@ -4161,13 +4161,13 @@ \subsection{\texorpdfstring{\texttt{.sample()}}{.sample()}}\label{sample}}
 
 \begin{tabular}{lrlr}
 \toprule
-{} &  Year &      Name &  Count \\
+{} &  Year &    Name &  Count \\
 \midrule
-112364 &  1989 &   Kathyrn &      5 \\
-148806 &  1999 &   Kareena &      5 \\
-288313 &  1973 &  Cheyenne &      6 \\
-37353  &  1957 &    Debora &    220 \\
-347600 &  2002 &     Lucas &    585 \\
+91420  &  1983 &  Kristy &    189 \\
+109951 &  1989 &   Trina &     34 \\
+72806  &  1975 &  Lashon &      9 \\
+45134  &  1961 &  Amparo &      8 \\
+172731 &  2006 &   Heidi &    212 \\
 \bottomrule
 \end{tabular}
 
@@ -4186,12 +4186,12 @@ \subsection{\texorpdfstring{\texttt{.sample()}}{.sample()}}\label{sample}}
 
 \begin{tabular}{lrlr}
 \toprule
-{} &  Year &     Name &  Count \\
+{} &  Year &   Name &  Count \\
 \midrule
-343695 &  2000 &   Shemar &     12 \\
-150979 &  2000 &  Fatimah &     11 \\
-151325 &  2000 &   Kamila &      9 \\
-342659 &  2000 &    Miles &    205 \\
+342878 &  2000 &  Keven &     63 \\
+344758 &  2000 &  Ivann &      5 \\
+149350 &  2000 &    Mya &    149 \\
+344627 &  2000 &  Amogh &      5 \\
 \bottomrule
 \end{tabular}
 
@@ -4334,7 +4334,7 @@ \section{Parting Note}\label{parting-note-1}}
 \hypertarget{pandas-iii}{%
 \chapter{Pandas III}\label{pandas-iii}}
 
-\begin{tcolorbox}[enhanced jigsaw, leftrule=.75mm, toptitle=1mm, colframe=quarto-callout-note-color-frame, opacityback=0, bottomtitle=1mm, colback=white, opacitybacktitle=0.6, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, breakable, arc=.35mm, left=2mm, colbacktitle=quarto-callout-note-color!10!white, titlerule=0mm, rightrule=.15mm, toprule=.15mm, bottomrule=.15mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-note-color-frame, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, toprule=.15mm, colbacktitle=quarto-callout-note-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 \begin{itemize}
 \tightlist
@@ -4637,7 +4637,7 @@ \section{\texorpdfstring{Aggregating Data with
 \end{Shaded}
 
 \begin{verbatim}
-<pandas.core.groupby.generic.DataFrameGroupBy object at 0x119726cd0>
+<pandas.core.groupby.generic.DataFrameGroupBy object at 0x107c33460>
 \end{verbatim}
 
 What does this strange output mean? Calling \texttt{.groupby}
@@ -20061,7 +20061,7 @@ \chapter{Data Cleaning and EDA}\label{data-cleaning-and-eda}}
 \end{Highlighting}
 \end{Shaded}
 
-\begin{tcolorbox}[enhanced jigsaw, leftrule=.75mm, toptitle=1mm, colframe=quarto-callout-note-color-frame, opacityback=0, bottomtitle=1mm, colback=white, opacitybacktitle=0.6, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, breakable, arc=.35mm, left=2mm, colbacktitle=quarto-callout-note-color!10!white, titlerule=0mm, rightrule=.15mm, toprule=.15mm, bottomrule=.15mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-note-color-frame, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, toprule=.15mm, colbacktitle=quarto-callout-note-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 \begin{itemize}
 \tightlist
@@ -22623,7 +22623,7 @@ \subsection{EDA and Data Wrangling}\label{eda-and-data-wrangling}}
 \hypertarget{regular-expressions}{%
 \chapter{Regular Expressions}\label{regular-expressions}}
 
-\begin{tcolorbox}[enhanced jigsaw, leftrule=.75mm, toptitle=1mm, colframe=quarto-callout-note-color-frame, opacityback=0, bottomtitle=1mm, colback=white, opacitybacktitle=0.6, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, breakable, arc=.35mm, left=2mm, colbacktitle=quarto-callout-note-color!10!white, titlerule=0mm, rightrule=.15mm, toprule=.15mm, bottomrule=.15mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-note-color-frame, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, toprule=.15mm, colbacktitle=quarto-callout-note-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 \begin{itemize}
 \tightlist
@@ -22960,11 +22960,11 @@ \subsubsection{Canonicalization with Pandas Series
 \end{Shaded}
 
 \begin{verbatim}
-/var/folders/7t/zbwy02ts2m7cn64fvwjqb8xw0000gp/T/ipykernel_46002/2523629438.py:3: FutureWarning:
+/var/folders/7t/zbwy02ts2m7cn64fvwjqb8xw0000gp/T/ipykernel_49951/2523629438.py:3: FutureWarning:
 
 The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.
 
-/var/folders/7t/zbwy02ts2m7cn64fvwjqb8xw0000gp/T/ipykernel_46002/2523629438.py:3: FutureWarning:
+/var/folders/7t/zbwy02ts2m7cn64fvwjqb8xw0000gp/T/ipykernel_49951/2523629438.py:3: FutureWarning:
 
 The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.
 
@@ -23911,7 +23911,7 @@ \section{Limitations of Regular
 \hypertarget{visualization-i}{%
 \chapter{Visualization I}\label{visualization-i}}
 
-\begin{tcolorbox}[enhanced jigsaw, leftrule=.75mm, toptitle=1mm, colframe=quarto-callout-note-color-frame, opacityback=0, bottomtitle=1mm, colback=white, opacitybacktitle=0.6, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, breakable, arc=.35mm, left=2mm, colbacktitle=quarto-callout-note-color!10!white, titlerule=0mm, rightrule=.15mm, toprule=.15mm, bottomrule=.15mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-note-color-frame, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, toprule=.15mm, colbacktitle=quarto-callout-note-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 \begin{itemize}
 \tightlist
@@ -24749,7 +24749,7 @@ \subsubsection{Evaluating Histograms}\label{evaluating-histograms}}
 \hypertarget{visualization-ii}{%
 \chapter{Visualization II}\label{visualization-ii}}
 
-\begin{tcolorbox}[enhanced jigsaw, leftrule=.75mm, toptitle=1mm, colframe=quarto-callout-note-color-frame, opacityback=0, bottomtitle=1mm, colback=white, opacitybacktitle=0.6, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, breakable, arc=.35mm, left=2mm, colbacktitle=quarto-callout-note-color!10!white, titlerule=0mm, rightrule=.15mm, toprule=.15mm, bottomrule=.15mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-note-color-frame, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, toprule=.15mm, colbacktitle=quarto-callout-note-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 \begin{itemize}
 \tightlist
@@ -26295,7 +26295,7 @@ \subsection{Harnessing Context}\label{harnessing-context}}
 \hypertarget{sampling}{%
 \chapter{Sampling}\label{sampling}}
 
-\begin{tcolorbox}[enhanced jigsaw, leftrule=.75mm, toptitle=1mm, colframe=quarto-callout-note-color-frame, opacityback=0, bottomtitle=1mm, colback=white, opacitybacktitle=0.6, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, breakable, arc=.35mm, left=2mm, colbacktitle=quarto-callout-note-color!10!white, titlerule=0mm, rightrule=.15mm, toprule=.15mm, bottomrule=.15mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-note-color-frame, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, toprule=.15mm, colbacktitle=quarto-callout-note-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 \begin{itemize}
 \tightlist
@@ -26904,7 +26904,7 @@ \subsubsection{Simple Random Sample}\label{simple-random-sample}}
 \end{Shaded}
 
 \begin{verbatim}
-0.5295885318701377
+0.5307710714643458
 \end{verbatim}
 
 This is very close to the actual vote of 0.5302792307692308!
@@ -26928,8 +26928,8 @@ \subsubsection{Simple Random Sample}\label{simple-random-sample}}
 \end{Highlighting}
 \end{Shaded}
 
-\textbf{Actual} = 0.5303, \textbf{Sample} = 0.5088, \textbf{Err} =
-4.06\%.
+\textbf{Actual} = 0.5303, \textbf{Sample} = 0.5425, \textbf{Err} =
+2.30\%.
 
 We'll learn how to choose this number when we (re)learn the Central
 Limit Theorem later in the semester.
@@ -26979,7 +26979,7 @@ \subsubsection{Quantifying Chance
 \end{Shaded}
 
 \begin{verbatim}
-0.952
+0.958
 \end{verbatim}
 
 You can see the curve looks roughly Gaussian/normal. Using KDE:
@@ -27012,7 +27012,7 @@ \section{Summary}\label{summary-1}}
 \hypertarget{introduction-to-modeling}{%
 \chapter{Introduction to Modeling}\label{introduction-to-modeling}}
 
-\begin{tcolorbox}[enhanced jigsaw, leftrule=.75mm, toptitle=1mm, colframe=quarto-callout-note-color-frame, opacityback=0, bottomtitle=1mm, colback=white, opacitybacktitle=0.6, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, breakable, arc=.35mm, left=2mm, colbacktitle=quarto-callout-note-color!10!white, titlerule=0mm, rightrule=.15mm, toprule=.15mm, bottomrule=.15mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-note-color-frame, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, toprule=.15mm, colbacktitle=quarto-callout-note-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 \begin{itemize}
 \tightlist
@@ -27375,7 +27375,7 @@ \subsection{Derivation}\label{derivation}}
   \(\hat{a} = \text{average of }y - \text{slope}\cdot\text{average of }x\)
 \end{itemize}
 
-\begin{tcolorbox}[enhanced jigsaw, leftrule=.75mm, breakable, arc=.35mm, opacityback=0, left=2mm, colback=white, rightrule=.15mm, bottomrule=.15mm, toprule=.15mm]
+\begin{tcolorbox}[enhanced jigsaw, left=2mm, colback=white, opacityback=0, arc=.35mm, breakable, rightrule=.15mm, leftrule=.75mm, bottomrule=.15mm, toprule=.15mm]
 
 Proof:
 
@@ -27727,7 +27727,7 @@ \section{Fitting the Model}\label{fitting-the-model}}
 \chapter{Constant Model, Loss, and
 Transformations}\label{constant-model-loss-and-transformations}}
 
-\begin{tcolorbox}[enhanced jigsaw, leftrule=.75mm, toptitle=1mm, colframe=quarto-callout-note-color-frame, opacityback=0, bottomtitle=1mm, colback=white, opacitybacktitle=0.6, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, breakable, arc=.35mm, left=2mm, colbacktitle=quarto-callout-note-color!10!white, titlerule=0mm, rightrule=.15mm, toprule=.15mm, bottomrule=.15mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-note-color-frame, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, toprule=.15mm, colbacktitle=quarto-callout-note-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 \begin{itemize}
 \tightlist
@@ -29065,7 +29065,7 @@ \section{Bonus: Calculating Constant Model MSE Using an Algebraic
 \hypertarget{ordinary-least-squares}{%
 \chapter{Ordinary Least Squares}\label{ordinary-least-squares}}
 
-\begin{tcolorbox}[enhanced jigsaw, leftrule=.75mm, toptitle=1mm, colframe=quarto-callout-note-color-frame, opacityback=0, bottomtitle=1mm, colback=white, opacitybacktitle=0.6, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, breakable, arc=.35mm, left=2mm, colbacktitle=quarto-callout-note-color!10!white, titlerule=0mm, rightrule=.15mm, toprule=.15mm, bottomrule=.15mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-note-color-frame, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, toprule=.15mm, colbacktitle=quarto-callout-note-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 \begin{itemize}
 \tightlist
@@ -29239,7 +29239,7 @@ \subsection{Multiple Linear
 \hypertarget{linear-algebra-approach}{%
 \subsection{Linear Algebra Approach}\label{linear-algebra-approach}}
 
-\begin{tcolorbox}[enhanced jigsaw, leftrule=.75mm, toptitle=1mm, colframe=quarto-callout-tip-color-frame, opacityback=0, bottomtitle=1mm, colback=white, opacitybacktitle=0.6, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Linear Algebra Review: Vector Dot Product}, breakable, arc=.35mm, left=2mm, colbacktitle=quarto-callout-tip-color!10!white, titlerule=0mm, rightrule=.15mm, toprule=.15mm, bottomrule=.15mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-tip-color-frame, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Linear Algebra Review: Vector Dot Product}, toprule=.15mm, colbacktitle=quarto-callout-tip-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 The \textbf{dot product (or inner product)} is a vector operation that:
 
@@ -29337,7 +29337,7 @@ \subsection{Linear Algebra Approach}\label{linear-algebra-approach}}
 \(\mathbb{Y}\) is also a vector with \(n\) elements
 (\(\mathbb{Y} \in \mathbb{R}^{n}\)).
 
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+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-tip-color-frame, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Linear Algebra Review: Linearity}, toprule=.15mm, colbacktitle=quarto-callout-tip-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 An expression is \textbf{linear in \(\theta\)} (a set of parameters) if
 it is a linear combination of the elements of the set. Checking if an
@@ -29382,7 +29382,7 @@ \subsection{Mean Squared Error}\label{mean-squared-error}}
 indication of how ``far away'' the predictions are from the true values,
 on average.
 
-\begin{tcolorbox}[enhanced jigsaw, leftrule=.75mm, toptitle=1mm, colframe=quarto-callout-tip-color-frame, opacityback=0, bottomtitle=1mm, colback=white, opacitybacktitle=0.6, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Linear Algebra: L2 Norm}, breakable, arc=.35mm, left=2mm, colbacktitle=quarto-callout-tip-color!10!white, titlerule=0mm, rightrule=.15mm, toprule=.15mm, bottomrule=.15mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-tip-color-frame, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Linear Algebra: L2 Norm}, toprule=.15mm, colbacktitle=quarto-callout-tip-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 When working with vectors, this idea of ``distance'' or the vector's
 \textbf{size/length} is represented by the \textbf{norm}. More
@@ -29530,7 +29530,7 @@ \subsection{A Note on Terminology for Multiple Linear
 \hypertarget{geometric-derivation}{%
 \section{Geometric Derivation}\label{geometric-derivation}}
 
-\begin{tcolorbox}[enhanced jigsaw, leftrule=.75mm, toptitle=1mm, colframe=quarto-callout-tip-color-frame, opacityback=0, bottomtitle=1mm, colback=white, opacitybacktitle=0.6, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Linear Algebra: Span}, breakable, arc=.35mm, left=2mm, colbacktitle=quarto-callout-tip-color!10!white, titlerule=0mm, rightrule=.15mm, toprule=.15mm, bottomrule=.15mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-tip-color-frame, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Linear Algebra: Span}, toprule=.15mm, colbacktitle=quarto-callout-tip-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 Recall that the \textbf{span} or \textbf{column space} of a matrix
 \(\mathbb{X}\) (denoted \(span(\mathbb{X})\)) is the set of all possible
@@ -29542,7 +29542,7 @@ \section{Geometric Derivation}\label{geometric-derivation}}
 
 \end{tcolorbox}
 
-\begin{tcolorbox}[enhanced jigsaw, leftrule=.75mm, toptitle=1mm, colframe=quarto-callout-tip-color-frame, opacityback=0, bottomtitle=1mm, colback=white, opacitybacktitle=0.6, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Linear Algebra: Matrix-Vector Multiplication}, breakable, arc=.35mm, left=2mm, colbacktitle=quarto-callout-tip-color!10!white, titlerule=0mm, rightrule=.15mm, toprule=.15mm, bottomrule=.15mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-tip-color-frame, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Linear Algebra: Matrix-Vector Multiplication}, toprule=.15mm, colbacktitle=quarto-callout-tip-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 There are 2 ways we can think about matrix-vector multiplication
 
@@ -29631,7 +29631,7 @@ \section{Geometric Derivation}\label{geometric-derivation}}
 visualize this as the vector created by dropping a perpendicular line
 from \(\mathbb{Y}\) onto the span of \(\mathbb{X}\).
 
-\begin{tcolorbox}[enhanced jigsaw, leftrule=.75mm, toptitle=1mm, colframe=quarto-callout-tip-color-frame, opacityback=0, bottomtitle=1mm, colback=white, opacitybacktitle=0.6, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Linear Algebra: Orthogonality}, breakable, arc=.35mm, left=2mm, colbacktitle=quarto-callout-tip-color!10!white, titlerule=0mm, rightrule=.15mm, toprule=.15mm, bottomrule=.15mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-tip-color-frame, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Linear Algebra: Orthogonality}, toprule=.15mm, colbacktitle=quarto-callout-tip-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 Recall that two vectors \(\vec{a}\) and \(\vec{b}\) are orthogonal if
 their dot product is zero: \(\vec{a}^{T}\vec{b} = 0\).
@@ -29754,7 +29754,7 @@ \section{OLS Properties}\label{ols-properties}}
 
 \[\mathbb{X}^Te = 0 \]
 
-\begin{tcolorbox}[enhanced jigsaw, leftrule=.75mm, breakable, arc=.35mm, opacityback=0, left=2mm, colback=white, rightrule=.15mm, bottomrule=.15mm, toprule=.15mm]
+\begin{tcolorbox}[enhanced jigsaw, left=2mm, colback=white, opacityback=0, arc=.35mm, breakable, rightrule=.15mm, leftrule=.75mm, bottomrule=.15mm, toprule=.15mm]
 
 Proof:
 
@@ -29792,7 +29792,7 @@ \section{OLS Properties}\label{ols-properties}}
 
 \[\sum_i^n e_i = 0\]
 
-\begin{tcolorbox}[enhanced jigsaw, leftrule=.75mm, breakable, arc=.35mm, opacityback=0, left=2mm, colback=white, rightrule=.15mm, bottomrule=.15mm, toprule=.15mm]
+\begin{tcolorbox}[enhanced jigsaw, left=2mm, colback=white, opacityback=0, arc=.35mm, breakable, rightrule=.15mm, leftrule=.75mm, bottomrule=.15mm, toprule=.15mm]
 
 Proof:
 
@@ -29858,7 +29858,7 @@ \section{Bonus: Uniqueness of the
 The Least Squares estimate \(\hat{\theta}\) is \textbf{unique} if and
 only if \(\mathbb{X}\) is \textbf{full column rank}.
 
-\begin{tcolorbox}[enhanced jigsaw, leftrule=.75mm, breakable, arc=.35mm, opacityback=0, left=2mm, colback=white, rightrule=.15mm, bottomrule=.15mm, toprule=.15mm]
+\begin{tcolorbox}[enhanced jigsaw, left=2mm, colback=white, opacityback=0, arc=.35mm, breakable, rightrule=.15mm, leftrule=.75mm, bottomrule=.15mm, toprule=.15mm]
 
 Proof:
 
@@ -29948,7 +29948,7 @@ \section{Bonus: Uniqueness of the
 \chapter{sklearn and Gradient
 Descent}\label{sklearn-and-gradient-descent}}
 
-\begin{tcolorbox}[enhanced jigsaw, leftrule=.75mm, toptitle=1mm, colframe=quarto-callout-note-color-frame, opacityback=0, bottomtitle=1mm, colback=white, opacitybacktitle=0.6, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, breakable, arc=.35mm, left=2mm, colbacktitle=quarto-callout-note-color!10!white, titlerule=0mm, rightrule=.15mm, toprule=.15mm, bottomrule=.15mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-note-color-frame, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, toprule=.15mm, colbacktitle=quarto-callout-note-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 \begin{itemize}
 \tightlist
@@ -31134,7 +31134,7 @@ \subsection{Batch Gradient Descent and Stochastic Gradient
 \hypertarget{feature-engineering}{%
 \chapter{Feature Engineering}\label{feature-engineering}}
 
-\begin{tcolorbox}[enhanced jigsaw, leftrule=.75mm, toptitle=1mm, colframe=quarto-callout-note-color-frame, opacityback=0, bottomtitle=1mm, colback=white, opacitybacktitle=0.6, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, breakable, arc=.35mm, left=2mm, colbacktitle=quarto-callout-note-color!10!white, titlerule=0mm, rightrule=.15mm, toprule=.15mm, bottomrule=.15mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-note-color-frame, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, toprule=.15mm, colbacktitle=quarto-callout-note-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 \begin{itemize}
 \tightlist
@@ -31731,7 +31731,7 @@ \section{Complexity and Overfitting}\label{complexity-and-overfitting}}
 we can improve model performance by designing increasingly complex
 models.
 
-\begin{tcolorbox}[enhanced jigsaw, leftrule=.75mm, toptitle=1mm, colframe=quarto-callout-tip-color-frame, opacityback=0, bottomtitle=1mm, colback=white, opacitybacktitle=0.6, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Math Fact: Polynomial Degrees}, breakable, arc=.35mm, left=2mm, colbacktitle=quarto-callout-tip-color!10!white, titlerule=0mm, rightrule=.15mm, toprule=.15mm, bottomrule=.15mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-tip-color-frame, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Math Fact: Polynomial Degrees}, toprule=.15mm, colbacktitle=quarto-callout-tip-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 Given \(N\) overlapping data points, we can always find a polynomial of
 degree \(N-1\) that goes through all those points.
@@ -31827,7 +31827,7 @@ \chapter{Case Study in Human Contexts and
 understand the material. The course notes will have the same broader
 structure but are by no means comprehensive.
 
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 \begin{itemize}
 \tightlist
@@ -32052,7 +32052,7 @@ \section{The Response: Cook County Open Data
 \subsection{1. Question/Problem
 Formulation}\label{questionproblem-formulation}}
 
-\begin{tcolorbox}[enhanced jigsaw, leftrule=.75mm, toptitle=1mm, colframe=quarto-callout-note-color-frame, opacityback=0, bottomtitle=1mm, colback=white, opacitybacktitle=0.6, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Driving Questions}, breakable, arc=.35mm, left=2mm, colbacktitle=quarto-callout-note-color!10!white, titlerule=0mm, rightrule=.15mm, toprule=.15mm, bottomrule=.15mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-note-color-frame, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Driving Questions}, toprule=.15mm, colbacktitle=quarto-callout-note-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 \begin{itemize}
 \tightlist
@@ -32092,7 +32092,7 @@ \subsection{1. Question/Problem
 comfortable. We can determine some metrics of success and frame a social
 problem as a data science problem.
 
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+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-tip-color-frame, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Definitions: Fairness and Transparency}, toprule=.15mm, colbacktitle=quarto-callout-tip-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 The definitions, as given by the Cook County Assessor's Office, are
 given below:
@@ -32185,7 +32185,7 @@ \subsection{1. Question/Problem
 \subsection{2. Data Acquisition and
 Cleaning}\label{data-acquisition-and-cleaning}}
 
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+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-note-color-frame, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Driving Questions}, toprule=.15mm, colbacktitle=quarto-callout-note-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 \begin{itemize}
 \tightlist
@@ -32262,7 +32262,7 @@ \subsection{2. Data Acquisition and
 \subsection{3. Exploratory Data
 Analysis}\label{exploratory-data-analysis}}
 
-\begin{tcolorbox}[enhanced jigsaw, leftrule=.75mm, toptitle=1mm, colframe=quarto-callout-note-color-frame, opacityback=0, bottomtitle=1mm, colback=white, opacitybacktitle=0.6, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Driving Questions}, breakable, arc=.35mm, left=2mm, colbacktitle=quarto-callout-note-color!10!white, titlerule=0mm, rightrule=.15mm, toprule=.15mm, bottomrule=.15mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-note-color-frame, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Driving Questions}, toprule=.15mm, colbacktitle=quarto-callout-note-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 \begin{itemize}
 \tightlist
@@ -32308,7 +32308,7 @@ \subsection{3. Exploratory Data
 \subsection{4. Prediction and
 Inference}\label{prediction-and-inference}}
 
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+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-note-color-frame, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Driving Questions}, toprule=.15mm, colbacktitle=quarto-callout-note-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 \begin{itemize}
 \tightlist
@@ -32370,7 +32370,7 @@ \subsection{4. Prediction and
 \hypertarget{results-and-conclusions}{%
 \subsection{5. Results and Conclusions}\label{results-and-conclusions}}
 
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+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-note-color-frame, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Driving Questions}, toprule=.15mm, colbacktitle=quarto-callout-note-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 \begin{itemize}
 \tightlist
@@ -32516,7 +32516,7 @@ \section{Key Takeaways}\label{key-takeaways}}
 \chapter{Cross Validation and
 Regularization}\label{cross-validation-and-regularization}}
 
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+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-note-color-frame, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, toprule=.15mm, colbacktitle=quarto-callout-note-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 \begin{itemize}
 \tightlist
@@ -33314,7 +33314,7 @@ \section{Regression Summary}\label{regression-summary}}
 \hypertarget{random-variables}{%
 \chapter{Random Variables}\label{random-variables}}
 
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+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-note-color-frame, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, toprule=.15mm, colbacktitle=quarto-callout-note-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 \begin{itemize}
 \tightlist
@@ -33360,7 +33360,7 @@ \chapter{Random Variables}\label{random-variables}}
 implications for modeling, but if you want to go a step further, take
 Data 140, CS 70, and/or EECS 126.
 
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+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-tip-color-frame, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Data 8 Recap}, toprule=.15mm, colbacktitle=quarto-callout-tip-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 Recall the following concepts from Data 8:
 
@@ -33719,7 +33719,7 @@ \subsection{Variance}\label{variance}}
 calculations, as \(\mathbb{E}[X^2] = \text{Var}(X)\) if \(X\) is
 centered and \(E(X)=0\).
 
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+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-tip-color-frame, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Proof}, toprule=.15mm, colbacktitle=quarto-callout-tip-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 \[\begin{align}
    \text{Var}(X) &= \mathbb{E}[(X-\mathbb{E}[X])^2] \\
@@ -33747,7 +33747,7 @@ \subsection{Example: Die}\label{example-die}}
       0, \text{otherwise} 
    \end{cases}\]
 
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+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-caution-color-frame, title=\textcolor{quarto-callout-caution-color}{\faFire}\hspace{0.5em}{What's the expectation, \(\mathbb{E}[X]?\)}, toprule=.15mm, colbacktitle=quarto-callout-caution-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 \[ \begin{align} 
          \mathbb{E}[X] &= 1\big(\frac{1}{6}\big) + 2\big(\frac{1}{6}\big) + 3\big(\frac{1}{6}\big) + 4\big(\frac{1}{6}\big) + 5\big(\frac{1}{6}\big) + 6\big(\frac{1}{6}\big) \\
@@ -33757,7 +33757,7 @@ \subsection{Example: Die}\label{example-die}}
 
 \end{tcolorbox}
 
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+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-caution-color-frame, title=\textcolor{quarto-callout-caution-color}{\faFire}\hspace{0.5em}{What's the variance, \(\text{Var}(X)?\)}, toprule=.15mm, colbacktitle=quarto-callout-caution-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 Using Approach 1 (definition): \[\begin{align} 
       \text{Var}(X) &= \big(\frac{1}{6}\big)((1 - \frac{7}{2})^2 + (2 - \frac{7}{2})^2 + (3 - \frac{7}{2})^2 + (4 - \frac{7}{2})^2 + (5 - \frac{7}{2})^2 + (6 - \frac{7}{2})^2) \\
@@ -33820,7 +33820,7 @@ \subsection{Properties of Expectation}\label{properties-of-expectation}}
 
 \[\mathbb{E}[aX+b] = aE[\mathbb{X}] + b\]
 
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+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-tip-color-frame, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Proof}, toprule=.15mm, colbacktitle=quarto-callout-tip-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 \[\begin{align}
         \mathbb{E}[aX+b] &= \sum_{x} (ax + b) P(X=x) \\
@@ -33841,7 +33841,7 @@ \subsection{Properties of Expectation}\label{properties-of-expectation}}
 
 \[\mathbb{E}[X+Y] = \mathbb{E}[X] + \mathbb{E}[Y]\]
 
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+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-tip-color-frame, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Proof}, toprule=.15mm, colbacktitle=quarto-callout-tip-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 \[\begin{align}
     \mathbb{E}[X+Y] &= \sum_{s} (X+Y)(s) P(s) \\
@@ -33892,7 +33892,7 @@ \subsection{Properties of Variance}\label{properties-of-variance}}
   \(X\) by \(b\) units.
 \end{itemize}
 
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+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-tip-color-frame, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Proof}, toprule=.15mm, colbacktitle=quarto-callout-tip-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 We know that \[\mathbb{E}[aX+b] = aE[\mathbb{X}] + b\]
 
@@ -33931,7 +33931,7 @@ \subsection{Properties of Variance}\label{properties-of-variance}}
   \[\text{Var}(X + Y) = \text{Var}(X) + \text{Var}(Y) \qquad \text{if } X, Y \text{ independent}\]
 \end{enumerate}
 
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+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-tip-color-frame, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Proof}, toprule=.15mm, colbacktitle=quarto-callout-tip-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 The variance of a sum is affected by the dependence between the two
 random variables that are being added. Let's expand the definition of
@@ -34103,7 +34103,7 @@ \subsection{Summary}\label{summary-2}}
 \chapter{Estimators, Bias, and
 Variance}\label{estimators-bias-and-variance}}
 
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+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-note-color-frame, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, toprule=.15mm, colbacktitle=quarto-callout-note-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 \begin{itemize}
 \tightlist
@@ -34255,7 +34255,7 @@ \subsection{Example}\label{example}}
 
 C. \(Y_C = 20 * X_1\)
 
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 Let \(X_1, X_2, ... X_{20}\) be 20 i.i.d Bernoulli(0.5) random
 variables. Since the \(X_i\)'s are independent,
@@ -34416,7 +34416,7 @@ \subsection{Using the Sample Mean to Estimate the Population
 \textbf{unbiased estimator} of the population mean and will explore this
 idea more in the next lecture.
 
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+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-tip-color-frame, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Data 8 Recap: Square Root Law}, toprule=.15mm, colbacktitle=quarto-callout-tip-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 The square root law
 (\href{https://inferentialthinking.com/chapters/14/5/Variability_of_the_Sample_Mean.html\#the-square-root-law}{Data
@@ -34525,8 +34525,8 @@ \subsection{Prediction as Estimation}\label{prediction-as-estimation}}
 
 \[\text{Prediction: }\hat{Y}(x)\]
 
-When building models it is also important to note that our choice of
-features will also signficantly impact our estimation. In the plot
+When building models, it is also important to note that our choice of
+features will also significantly impact our estimation. In the plot
 below, you can see how the different models (green and purple) can lead
 to different estimates.
 
@@ -34539,7 +34539,7 @@ \subsubsection{Estimating a Linear
 modeled by \[Y = g(x) + \epsilon\]
 \[ f_{\theta}(x) = Y = \theta_0 + \sum_{j=1}^p \theta_j x_j + \epsilon\]
 
-\begin{tcolorbox}[enhanced jigsaw, leftrule=.75mm, toptitle=1mm, colframe=quarto-callout-warning-color-frame, opacityback=0, bottomtitle=1mm, colback=white, opacitybacktitle=0.6, title=\textcolor{quarto-callout-warning-color}{\faExclamationTriangle}\hspace{0.5em}{Which expressions are random?}, breakable, arc=.35mm, left=2mm, colbacktitle=quarto-callout-warning-color!10!white, titlerule=0mm, rightrule=.15mm, toprule=.15mm, bottomrule=.15mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-warning-color-frame, title=\textcolor{quarto-callout-warning-color}{\faExclamationTriangle}\hspace{0.5em}{Which expressions are random?}, toprule=.15mm, colbacktitle=quarto-callout-warning-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 In our two equations above, the true relationship
 \(g(x) = \theta_0 + \sum_{j=1}^p \theta_j x_j\) is not random, but
@@ -34554,7 +34554,7 @@ \subsubsection{Estimating a Linear
 use it to train a model and obtain an estimate of \(\hat{\theta}\)
 \[\hat{Y}(x) = f_{\hat{\theta}}(x) = \hat{\theta_0} + \sum_{j=1}^p \hat{\theta_j} x_j\]
 
-\begin{tcolorbox}[enhanced jigsaw, leftrule=.75mm, toptitle=1mm, colframe=quarto-callout-warning-color-frame, opacityback=0, bottomtitle=1mm, colback=white, opacitybacktitle=0.6, title=\textcolor{quarto-callout-warning-color}{\faExclamationTriangle}\hspace{0.5em}{Which expressions are random?}, breakable, arc=.35mm, left=2mm, colbacktitle=quarto-callout-warning-color!10!white, titlerule=0mm, rightrule=.15mm, toprule=.15mm, bottomrule=.15mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-warning-color-frame, title=\textcolor{quarto-callout-warning-color}{\faExclamationTriangle}\hspace{0.5em}{Which expressions are random?}, toprule=.15mm, colbacktitle=quarto-callout-warning-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 In our estimating equation above, our sample \(\Bbb{X}\), \(\Bbb{Y}\)
 are random (often due to human error). Hence, the estimates we calculate
@@ -34745,7 +34745,7 @@ \subsubsection{Model Bias}\label{model-bias}}
 \(g(x)\); if it's negative, our model tends to underestimate \(g(x)\).
 And if it's 0, we can say that our model is \textbf{unbiased}.
 
-\begin{tcolorbox}[enhanced jigsaw, leftrule=.75mm, toptitle=1mm, colframe=quarto-callout-tip-color-frame, opacityback=0, bottomtitle=1mm, colback=white, opacitybacktitle=0.6, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Unbiased Estimators}, breakable, arc=.35mm, left=2mm, colbacktitle=quarto-callout-tip-color!10!white, titlerule=0mm, rightrule=.15mm, toprule=.15mm, bottomrule=.15mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-tip-color-frame, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Unbiased Estimators}, toprule=.15mm, colbacktitle=quarto-callout-tip-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 An \textbf{unbiased model} has a \(\text{model bias } = 0\). In other
 words, our model predicts \(g(x)\) on average.
@@ -34842,7 +34842,7 @@ \section{{[}Bonus{]} Proof of Bias-Variance
 Decomposition in the Bias-Variance Tradeoff section above, and this
 content is out of scope.
 
-\begin{tcolorbox}[enhanced jigsaw, leftrule=.75mm, breakable, arc=.35mm, opacityback=0, left=2mm, colback=white, rightrule=.15mm, bottomrule=.15mm, toprule=.15mm]
+\begin{tcolorbox}[enhanced jigsaw, left=2mm, colback=white, opacityback=0, arc=.35mm, breakable, rightrule=.15mm, leftrule=.75mm, bottomrule=.15mm, toprule=.15mm]
 
 \textbf{Click to show}\vspace{2mm}
 
@@ -34991,7 +34991,7 @@ \subsection{Step 4: Bias-Variance
 \chapter{Causal Inference and
 Confounding}\label{causal-inference-and-confounding}}
 
-\begin{tcolorbox}[enhanced jigsaw, leftrule=.75mm, toptitle=1mm, colframe=quarto-callout-note-color-frame, opacityback=0, bottomtitle=1mm, colback=white, opacitybacktitle=0.6, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, breakable, arc=.35mm, left=2mm, colbacktitle=quarto-callout-note-color!10!white, titlerule=0mm, rightrule=.15mm, toprule=.15mm, bottomrule=.15mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-note-color-frame, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, toprule=.15mm, colbacktitle=quarto-callout-note-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 \begin{itemize}
 \tightlist
@@ -35120,7 +35120,7 @@ \section{Review: Bootstrap
   accurately model the variability of our estimates.
 \end{itemize}
 
-\begin{tcolorbox}[enhanced jigsaw, leftrule=.75mm, toptitle=1mm, colframe=quarto-callout-warning-color-frame, opacityback=0, bottomtitle=1mm, colback=white, opacitybacktitle=0.6, title=\textcolor{quarto-callout-warning-color}{\faExclamationTriangle}\hspace{0.5em}{Why must we resample \emph{with replacement}?}, breakable, arc=.35mm, left=2mm, colbacktitle=quarto-callout-warning-color!10!white, titlerule=0mm, rightrule=.15mm, toprule=.15mm, bottomrule=.15mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-warning-color-frame, title=\textcolor{quarto-callout-warning-color}{\faExclamationTriangle}\hspace{0.5em}{Why must we resample \emph{with replacement}?}, toprule=.15mm, colbacktitle=quarto-callout-warning-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 Given an original sample of size \(n\), we want a resample that has the
 same size \(n\) as the original. Sampling \emph{without} replacement
@@ -35784,7 +35784,7 @@ \subsection{How to perform causal
 \hypertarget{sql-i}{%
 \chapter{SQL I}\label{sql-i}}
 
-\begin{tcolorbox}[enhanced jigsaw, leftrule=.75mm, toptitle=1mm, colframe=quarto-callout-note-color-frame, opacityback=0, bottomtitle=1mm, colback=white, opacitybacktitle=0.6, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, breakable, arc=.35mm, left=2mm, colbacktitle=quarto-callout-note-color!10!white, titlerule=0mm, rightrule=.15mm, toprule=.15mm, bottomrule=.15mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, left=2mm, titlerule=0mm, coltitle=black, opacityback=0, arc=.35mm, breakable, colframe=quarto-callout-note-color-frame, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, toprule=.15mm, colbacktitle=quarto-callout-note-color!10!white, bottomtitle=1mm, rightrule=.15mm, leftrule=.75mm, toptitle=1mm, bottomrule=.15mm, colback=white, opacitybacktitle=0.6]
 
 \begin{itemize}
 \tightlist
@@ -36300,9 +36300,9 @@ \subsubsection{\texorpdfstring{Uniqueness with
 \endhead
 \bottomrule\noalign{}
 \endlastfoot
+2011 \\
 2010 \\
 2019 \\
-2011 \\
 \end{longtable}
 
 \hypertarget{applying-where-conditions}{%
@@ -36694,8 +36694,8 @@ \section{\texorpdfstring{Aggregating with
 \endhead
 \bottomrule\noalign{}
 \endlastfoot
-dessert & 5 \\
 entree & 30 \\
+dessert & 5 \\
 appetizer & 12 \\
 \end{longtable}
 
@@ -36762,8 +36762,8 @@ \section{\texorpdfstring{Aggregating with
 \bottomrule\noalign{}
 \endlastfoot
 entree & 30 & 7 & taco \\
-appetizer & 12 & 4 & potsticker \\
 dessert & 5 & 5 & ice cream \\
+appetizer & 12 & 4 & potsticker \\
 \end{longtable}
 
 To count the number of rows associated with each group, we use the
@@ -36820,9 +36820,9 @@ \section{\texorpdfstring{Aggregating with
 \endhead
 \bottomrule\noalign{}
 \endlastfoot
-2010 & 2 \\
 2011 & 1 \\
 2019 & 1 \\
+2010 & 2 \\
 \end{longtable}
 
 With this definition of \texttt{GROUP\ BY} in hand, let's update our SQL
diff --git a/probability_2/probability_2.qmd b/probability_2/probability_2.qmd
index 498d25a7..5cc77169 100644
--- a/probability_2/probability_2.qmd
+++ b/probability_2/probability_2.qmd
@@ -206,7 +206,7 @@ $$\text{Prediction: }\hat{Y}(x)$$
 <img src="images/y_hat.png" alt='y_hat' width='600'>
 </p>
 
-When building models it is also important to note that our choice of features will also signficantly impact our estimation. In the plot below, you can see how the different models (green and purple) can lead to different estimates.
+When building models, it is also important to note that our choice of features will also significantly impact our estimation. In the plot below, you can see how the different models (green and purple) can lead to different estimates.
 
 <p align="center">
 <img src="images/y_hat2.png" alt='y_hat' width='400'>
@@ -499,4 +499,3 @@ $$
 E\left[(Y(x)-\hat{Y}(x))^2\right] = \sigma^2 + \left(E\left[\hat{Y}(x)\right] - g(x)\right)^2 + E\left[\left(\hat{Y}(x)-E\left[\hat{Y}(x)\right]\right)^2\right]
 $$
 :::
-