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README.html
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<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml">
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<meta charset="utf-8">
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
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<!-- README.md is generated from README.Rmd. Please edit the README.Rmd file -->
<h1 id="lab-2-team-11---report">Lab #2 Team #11 - Report:</h1>
<p>Follow the instructions posted at <a href="https://ds202-at-isu.github.io/labs.html">https://ds202-at-isu.github.io/labs.html</a>
for the lab assignment. The work is meant to be finished during the lab
time, but you have time until Monday evening to polish things.</p>
<p>Include your answers in this document (Rmd file). Make sure that it
knits properly (into the md file). Upload both the Rmd and the md file
to your repository.</p>
<p>All submissions to the github repo will be automatically uploaded for
grading once the due date is passed. Submit a link to your repository on
Canvas (only one submission per team) to signal to the instructors that
you are done with your submission.</p>
<table>
<tbody>
<tr class="odd">
<td>TL;DR Summary - Bhargav Yellepeddi:</td>
</tr>
<tr class="even">
<td>The analysis of Ames property sales since 2017 shows most sales
between $100K–$400K. Total living area follows a normal distribution,
with outliers. A positive correlation exists between living area and
sale price, though it weakens for larger properties. Missing data led to
447 rows being excluded.</td>
</tr>
</tbody>
</table>
<p>Bhargav Yellepeddi - SalePrice and Total Living Area Analysis:</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="co"># Load the Ames dataset</span></span>
<span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a>data <span class="ot"><-</span> classdata<span class="sc">::</span>ames</span></code></pre></div>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a><span class="co"># View first few rows</span></span>
<span id="cb2-2"><a href="#cb2-2" tabindex="-1"></a><span class="fu">head</span>(data)</span></code></pre></div>
<pre><code>## # A tibble: 6 × 16
## `Parcel ID` Address Style Occupancy `Sale Date` `Sale Price` `Multi Sale` YearBuilt Acres `TotalLivingArea (sf)` Bedrooms FinishedBsmtArea (sf…¹ `LotArea(sf)` AC FirePlace Neighborhood
## <chr> <chr> <fct> <fct> <date> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <fct>
## 1 0903202160 1024 RIDGEWOOD AVE, AMES 1 1/2 Story … Single-F… 2022-08-12 181900 <NA> 1940 0.109 1030 2 NA 4740 Yes Yes (28) Res: B…
## 2 0907428215 4503 TWAIN CIR UNIT 105, AMES 1 Story Frame Condomin… 2022-08-04 127100 <NA> 2006 0.027 771 1 NA 1181 Yes No (55) Res: D…
## 3 0909428070 2030 MCCARTHY RD, AMES 1 Story Frame Single-F… 2022-08-15 0 <NA> 1951 0.321 1456 3 1261 14000 Yes No (32) Res: C…
## 4 0923203160 3404 EMERALD DR, AMES 1 Story Frame Townhouse 2022-08-09 245000 <NA> 1997 0.103 1289 4 890 4500 Yes No (31) Res: M…
## 5 0520440010 4507 EVEREST AVE, AMES <NA> <NA> 2022-08-03 449664 <NA> NA 0.287 NA NA NA 12493 No No (19) Res: N…
## 6 0907275030 4512 HEMINGWAY DR, AMES 2 Story Frame Single-F… 2022-08-16 368000 <NA> 1996 0.494 2223 4 NA 21533 Yes Yes (37) Res: C…
## # ℹ abbreviated name: ¹`FinishedBsmtArea (sf)`</code></pre>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a><span class="co"># Inspect the structure of the dataset</span></span>
<span id="cb4-2"><a href="#cb4-2" tabindex="-1"></a><span class="fu">str</span>(data)</span></code></pre></div>
<pre><code>## tibble [6,935 × 16] (S3: tbl_df/tbl/data.frame)
## $ Parcel ID : chr [1:6935] "0903202160" "0907428215" "0909428070" "0923203160" ...
## $ Address : chr [1:6935] "1024 RIDGEWOOD AVE, AMES" "4503 TWAIN CIR UNIT 105, AMES" "2030 MCCARTHY RD, AMES" "3404 EMERALD DR, AMES" ...
## $ Style : Factor w/ 12 levels "1 1/2 Story Brick",..: 2 5 5 5 NA 9 5 5 5 5 ...
## $ Occupancy : Factor w/ 5 levels "Condominium",..: 2 1 2 3 NA 2 2 1 2 2 ...
## $ Sale Date : Date[1:6935], format: "2022-08-12" "2022-08-04" "2022-08-15" "2022-08-09" ...
## $ Sale Price : num [1:6935] 181900 127100 0 245000 449664 ...
## $ Multi Sale : chr [1:6935] NA NA NA NA ...
## $ YearBuilt : num [1:6935] 1940 2006 1951 1997 NA ...
## $ Acres : num [1:6935] 0.109 0.027 0.321 0.103 0.287 0.494 0.172 0.023 0.285 0.172 ...
## $ TotalLivingArea (sf) : num [1:6935] 1030 771 1456 1289 NA ...
## $ Bedrooms : num [1:6935] 2 1 3 4 NA 4 5 1 3 4 ...
## $ FinishedBsmtArea (sf): num [1:6935] NA NA 1261 890 NA ...
## $ LotArea(sf) : num [1:6935] 4740 1181 14000 4500 12493 ...
## $ AC : chr [1:6935] "Yes" "Yes" "Yes" "Yes" ...
## $ FirePlace : chr [1:6935] "Yes" "No" "No" "No" ...
## $ Neighborhood : Factor w/ 42 levels "(0) None","(13) Apts: Campus",..: 15 40 19 18 6 24 14 40 13 23 ...</code></pre>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a><span class="fu">unique</span>(data[,<span class="st">"Sale Price"</span>])</span></code></pre></div>
<pre><code>## # A tibble: 1,327 × 1
## `Sale Price`
## <dbl>
## 1 181900
## 2 127100
## 3 0
## 4 245000
## 5 449664
## 6 368000
## 7 110000
## 8 350000
## 9 242000
## 10 293000
## # ℹ 1,317 more rows</code></pre>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a><span class="co"># Histogram of SalePrice</span></span>
<span id="cb8-2"><a href="#cb8-2" tabindex="-1"></a> <span class="fu">ggplot</span>(data, <span class="fu">aes</span>(<span class="at">x =</span> <span class="st">`</span><span class="at">Sale Price</span><span class="st">`</span>)) <span class="sc">+</span></span>
<span id="cb8-3"><a href="#cb8-3" tabindex="-1"></a> <span class="fu">geom_histogram</span>(<span class="at">binwidth =</span> <span class="dv">20000</span>, <span class="at">fill =</span> <span class="st">'blue'</span>, <span class="at">color =</span> <span class="st">'black'</span>) <span class="sc">+</span> </span>
<span id="cb8-4"><a href="#cb8-4" tabindex="-1"></a> <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">"Distribution of Sale Prices"</span>, <span class="at">x =</span> <span class="st">"Sale Price"</span>, <span class="at">y =</span> <span class="st">"Count"</span>)</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a><span class="co"># Summary statistics of TotalLivingArea</span></span>
<span id="cb9-2"><a href="#cb9-2" tabindex="-1"></a><span class="fu">summary</span>(data<span class="sc">$</span><span class="st">`</span><span class="at">TotalLivingArea (sf)</span><span class="st">`</span>)</span></code></pre></div>
<pre><code>## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0 1095 1460 1507 1792 6007 447</code></pre>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" tabindex="-1"></a><span class="co"># Histogram of TotalLivingArea</span></span>
<span id="cb11-2"><a href="#cb11-2" tabindex="-1"></a><span class="fu">ggplot</span>(data, <span class="fu">aes</span>(<span class="at">x =</span> <span class="st">`</span><span class="at">TotalLivingArea (sf)</span><span class="st">`</span>)) <span class="sc">+</span></span>
<span id="cb11-3"><a href="#cb11-3" tabindex="-1"></a> <span class="fu">geom_histogram</span>(<span class="at">binwidth =</span> <span class="dv">100</span>, <span class="at">fill =</span> <span class="st">'green'</span>, <span class="at">color =</span> <span class="st">'black'</span>) <span class="sc">+</span></span>
<span id="cb11-4"><a href="#cb11-4" tabindex="-1"></a> <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">"Distribution of Ground Living Area"</span>, <span class="at">x =</span> <span class="st">"Ground Living Area (sq ft)"</span>, <span class="at">y =</span> <span class="st">"Count"</span>)</span></code></pre></div>
<pre><code>## Warning: Removed 447 rows containing non-finite outside the scale range (`stat_bin()`).</code></pre>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" tabindex="-1"></a><span class="co"># Scatterplot of SalePrice vs TotalLivingArea</span></span>
<span id="cb13-2"><a href="#cb13-2" tabindex="-1"></a><span class="fu">ggplot</span>(data, <span class="fu">aes</span>(<span class="at">x =</span> <span class="st">`</span><span class="at">TotalLivingArea (sf)</span><span class="st">`</span>, <span class="at">y =</span> <span class="st">`</span><span class="at">Sale Price</span><span class="st">`</span>)) <span class="sc">+</span></span>
<span id="cb13-3"><a href="#cb13-3" tabindex="-1"></a> <span class="fu">geom_point</span>(<span class="at">color =</span> <span class="st">'purple'</span>) <span class="sc">+</span></span>
<span id="cb13-4"><a href="#cb13-4" tabindex="-1"></a> <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">"Sale Price vs Ground Living Area"</span>, <span class="at">x =</span> <span class="st">"Ground Living Area (sq ft)"</span>, <span class="at">y =</span> <span class="st">"Sale Price"</span>)</span></code></pre></div>
<pre><code>## Warning: Removed 447 rows containing missing values or values outside the scale range (`geom_point()`).</code></pre>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<hr />
<hr />
<p>Thien Tam Nguyen - SalePrice and LotArea Analysis:</p>
<p>1.</p>
<p>“Sale Price”: A numeric variable representing the selling price of
each property. This variable ranges from low-value properties to luxury
ones, generally from around $50,000 to over $20 million as seen in the
histogram.</p>
<p>“Lot Area (sf)”: A numeric variable representing the size of the
property lot in square feet. The Lot Area ranges from small plots (less
than 10,000 square feet) to very large areas exceeding 500,000 square
feet.</p>
<p>Both variables are continuous, and they provide insight into the
property values and the physical size of the lots.</p>
<ol start="2" style="list-style-type: decimal">
<li><p>I am taking analysis of sale price and lotArea</p></li>
<li><p>The Sale Price histogram shows that the majority of properties
are sold for prices under $5 million, with most sales clustered around
lower price points.There are a few extremely high sale prices (over $15
million), which could indicate unique or luxury properties.The pattern
shows that the market for properties in Ames is mostly concentrated in
the lower price range, with a small number of high-value
outliers.</p></li>
<li><p>The Sale Price has a wide range, but most properties are
concentrated in the lower value range, with some significant
outliers.The Lot Area is also skewed, with most properties being small
to medium-sized, but a few very large properties exist.There is a
general positive trend between Lot Area and Sale Price, but the
relationship is not perfectly linear, and other factors such as location
or house features may play a crucial role.</p></li>
</ol>
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" tabindex="-1"></a><span class="co"># Load Ames dataset</span></span>
<span id="cb15-2"><a href="#cb15-2" tabindex="-1"></a>data <span class="ot"><-</span> classdata<span class="sc">::</span>ames</span></code></pre></div>
<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" tabindex="-1"></a><span class="co"># View first few rows for relevant columns with corrected names</span></span>
<span id="cb16-2"><a href="#cb16-2" tabindex="-1"></a><span class="fu">colnames</span>(data)</span></code></pre></div>
<pre><code>## [1] "Parcel ID" "Address" "Style" "Occupancy" "Sale Date" "Sale Price" "Multi Sale" "YearBuilt"
## [9] "Acres" "TotalLivingArea (sf)" "Bedrooms" "FinishedBsmtArea (sf)" "LotArea(sf)" "AC" "FirePlace" "Neighborhood"</code></pre>
<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" tabindex="-1"></a><span class="fu">head</span>(data[, <span class="fu">c</span>(<span class="st">"Sale Price"</span>, <span class="st">"LotArea(sf)"</span>)])</span></code></pre></div>
<pre><code>## # A tibble: 6 × 2
## `Sale Price` `LotArea(sf)`
## <dbl> <dbl>
## 1 181900 4740
## 2 127100 1181
## 3 0 14000
## 4 245000 4500
## 5 449664 12493
## 6 368000 21533</code></pre>
<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" tabindex="-1"></a><span class="co"># Inspect structure to understand data types of specific columns</span></span>
<span id="cb20-2"><a href="#cb20-2" tabindex="-1"></a><span class="fu">str</span>(data[, <span class="fu">c</span>(<span class="st">"Sale Price"</span>, <span class="st">"LotArea(sf)"</span>)])</span></code></pre></div>
<pre><code>## tibble [6,935 × 2] (S3: tbl_df/tbl/data.frame)
## $ Sale Price : num [1:6935] 181900 127100 0 245000 449664 ...
## $ LotArea(sf): num [1:6935] 4740 1181 14000 4500 12493 ...</code></pre>
<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" tabindex="-1"></a><span class="co"># Histogram of Lot Area</span></span>
<span id="cb22-2"><a href="#cb22-2" tabindex="-1"></a><span class="fu">ggplot</span>(data, <span class="fu">aes</span>(<span class="at">x =</span> <span class="st">`</span><span class="at">LotArea(sf)</span><span class="st">`</span>)) <span class="sc">+</span></span>
<span id="cb22-3"><a href="#cb22-3" tabindex="-1"></a> <span class="fu">geom_histogram</span>(<span class="at">binwidth =</span> <span class="dv">1000</span>, <span class="at">fill =</span> <span class="st">'orange'</span>, <span class="at">color =</span> <span class="st">'red'</span>) <span class="sc">+</span></span>
<span id="cb22-4"><a href="#cb22-4" tabindex="-1"></a> <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">"Distribution of Lot Area in Ames Properties"</span>, </span>
<span id="cb22-5"><a href="#cb22-5" tabindex="-1"></a> <span class="at">x =</span> <span class="st">"Lot Area (square feet)"</span>, </span>
<span id="cb22-6"><a href="#cb22-6" tabindex="-1"></a> <span class="at">y =</span> <span class="st">"Count"</span>)</span></code></pre></div>
<pre><code>## Warning: Removed 89 rows containing non-finite outside the scale range (`stat_bin()`).</code></pre>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAMAAABNUi8GAAABKVBMVEUAAAAAADoAAGYAOjoAOmYAOpAAZrYzMzM6AAA6ADo6AGY6OgA6Ojo6OmY6OpA6ZmY6ZpA6ZrY6kLY6kNtNTU1NTW5NTY5NbqtNjshmAABmADpmOgBmOmZmZmZmkJBmkNtmtrZmtttmtv9uTU1uTW5uTY5ubo5ubqtuq+SOTU2OTW6OTY6Obk2OyP+QOgCQZgCQZjqQZmaQkDqQkLaQtpCQttuQ27aQ2/+rbk2rbm6rbo6ryKur5OSr5P+2ZgC2Zjq2kDq2tpC2ttu225C229u22/+2/9u2///Ijk3I///bkDrbkGbbtmbbtpDb27bb29vb/7bb/9vb///kq27k///r6+v/AAD/pQD/tmb/yI7/25D/27b/29v/5Kv//7b//8j//9v//+T///+adIxTAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAT20lEQVR4nO2dCXvbxp2HITlm2HZ3k1Z0HMW7bSMnGzkptVeztrMr9Ui9Opo9YjmqxJSSiO//IXYODA4CIEEBBH+23t/zxDyAd/4Y8NUMMLKZKCZEONGmD4CQRUFQIh0EJdJBUCIdBCXSQVAiHQQl0kFQIp07CnoWufztK/P89uDh62zL5O+Pk2f2/evdQRm2uxShBfldFA3cnmfbx6WNWTGTy2ivQXs1hZvBIUn33/t6BWbFfhOfdoJG0d78R555VCuo3aXpB3VpagzmGq4qZnIUVdQqpaZwMzitm+v+CtQK/SZJ7iqoM+P2z8Ot55VbQmoFbZpsZFsm6GT4N6WDaZwV4aTud9FKtq3Sb5KklaDWnx0/KNz+cxRtfWqem2Fl5/ZgcBZt/8mPoJfD6L1XYeQyW8Iu9uWPT6Kw8Q8/i7Z+lbYfNhyZfZNSuY832epbyg7p26F94Yu/cju5S5D4u59FWdu2cKlcCk+GO98NzQZz0B++Tir56xjfv7nuH20f58vZI77e3bE9fh7naLfP1rDY74pmSTktBU0+7tfOFTPjBfseDKOH3ztBH9jPxYxOVYJe2m1+Y3HGTDdUChq2FgQ1PwqhhC3+ehKaCPNx0nbuaNNyGTwZfmC4rd8O/ZXFZO4AA1AQ1JVLj/h694Okx3na7PPek7Tfdc2ScloK6j8iq8PP7XkfhAst642/Bo1+8fr29+bDTgXNrsXMbp9af9wzcyd0Fq4Dsw1VU3xua25UtTu6nUNxOzD999AWNuObPzZPl8rlYKPOp2bmNi1Mhna/0EjWv/yxfOf65cuFYzI9HoQeZ4fgfo6yftc0S8rpTNAHv/xTusU4cRwE9Vr6veYETT6ZIzsWWiC9YM02VAma25oT1ByIb8C3NXEzdrLD//7hX4ZRQdBCuRzs2nYH7Q8wNJL1zx9Lco/kBkFfLhyT77FtMaP9Prl+1zRLyulIUDcXRx++yt+qekH9Z2EdrBB0z268LGy0yTZUC5puzQT1s2aUFr/MFEo2FQQtlMvBzh1v7pGZt7NG0v75Y8lW2cKlQTimRHvT44xObt1Dv2ubJeV0dA1qZqwn/iPekKBhTNspCWrm3A/+7Y//t7tA0AyuFTTtX/FY2gpabJaU09VdvM3//FO0VxLUflxuijtKp9WqKX5O0FWn+OQY7PxaMCYOz+auQQvlcnBR0KyRrH/FY8kELU7xtscZPSdobbOknLbroOmg8Atz2fZ760zujt3dMhiD7S2Dmc1+Ze8WBnG6S/4mac6YpjdJYfHyMrmbz8m+9XUc/+g8MTdEPz4JS5YVgubgoqBZI1n/iseSLZ/lbpLS28JAB0HTftc0S8rp6DdJyXqJdSZd7POCukUX+3m6ie29J4M42yW3zFScc9MNeUHTefgyv4KUuuXBKFkvCnP8TrqEVS9oDi4KWmokrMuXBI1zy0wPCj3Orjpy/a5plpTTStCtD9PfxbsV55+bE339JBp8n78GNR/d37m7gD8Pow+/t1Jku0yyhfqCoOmGSkGzrbYl++HmJvDt/0quOOxOD+wauFtC//qodNUcyuXhb4uCZo2k/fPHUhI0PSb/qwl/3xPocA1R6HdFs6Qc/jZTx6n85S65cxC04yBot0HQjoOg3QZBOw6CdhsEJdJBUCIdBCXSQVAiHQQl0kFQIp27CfrDgizcCATUEEJQIGkIQYGkIQQFkoYQFEgaQlAgaQhBgaQhBAWShhAUSBpCUCBpCEGBpCEEBZKGEBRIGkJQIGkIQYGkIQQFkoYQFEgaQlAgaagHQS8u1twHoHcYQlAgaQhBgaQhBAWShhAUSBpCUCBpCEGBpCEEBZKGEBRIGkJQIGkIQYGkIQQFkoYQFEgaQlAgaQhBgaQhBAWShhAUSBpCUCBpCEGBpCEEBZKGEBRIGkJQIGkIQYGkIQQFkoYQFEgaQlAgaQhBgaShVoI2y8XFGhsn9yOMoECaEIICSUMICiQNISiQNISgQNIQggJJQwgKJA0hKJA0hKBA0hCCAklDCAokDSEokDSEoEDSEIICSUMICiQNISiQNISgQNIQggJJQwgKJA0hKJA0hKBA0hCCAklDCAokDSEokDSEoEDSEIICSUMICiQNISiQNISgQNIQggJJQwgKJA0hKJA0hKBA0hCCAklDCAokDSEokDSEoEDSEIICSUMICiQNISiQNISgQNIQggJJQwgKJA0hKJA0tFTQ85HN2D1+dBrfPBs9foOgQH1BjUbQK+Pkydg+m70wqn6MoEB9QU0EvfnyMJ69PHRPvzqNp5+fIihQT1ATQe2QaaZ2O9FPv3jjfI3j900WDrtp+P/Fk9ZZJKgTcvqZG0XtZO8FtWn288IICnR3qIGgV+ld0ck4G0ERFKgPqIGgJ/vpszHXoED9QssF9bdHdhidfXM6e7HPXTyQlKDJjH4+Gj06jFkHBeoXajDF16fR4VwgKNDdIQQFkoYQFEgaQlAgaQhBgaQhBAWShhAUSBpCUCBpCEGBpCEEBZKGEBRIGkJQIGkIQYGkIQQFkoYQFEgaQlAgaQhBgaQhBAWShhAUSBpCUCBpCEGBpCEEBZKGEBRIGkJQIGkIQYGkIQQFkoYQFEgaQlAgaQhBgaQhBAWShhAUSBpCUCBpCEGBpCEEBZKGEBRIGkJQIGkIQYGkIQQFkoYQFEgaQlAgaQhBgaQhBAWShhAUSBpqJWijGEHX1zi5J2EEBdKEEBRIGkJQIGkIQYGkIQQFkoYQFEgaQlAgaQhBgaQhBAWShhAUSBrqRdDGiqqfLaDeoV4E/QuCAiEo0LsIISiQNISgQNIQggJJQwgKJA0hKJA0hKBA0hCCAklDCAokDSEokDSEoEDSEIICSUMICiQNISiQNISgQNIQggJJQwgKJA0hKJA0hKBA0hCCAklDCAokDSEokDSEoEDSEIICSUMICiQNISiQNISgQNIQggJJQxWCXu/u2YfL7WMEBdo0hKBA0lBJ0LMoZLDMTwQFWjtUP4I2SKPDQVCgFlCFoHM5H41GH53GN89Gj9/E4aG5oBcICtQCqhJ0MnRTfHINejK2f85ejOPzj8MDggL1BFUIenuQv/qcvTy0DzdfncbTz0+TBwQF6gmqELR4DWrm9NFoHE+/eBPffHmYPJj33zepvy7I4gVtsichtSmOoHu5V9PPDu0oevXYmZk8JNua/LwwggK1gSoErVgBPRmXRlAEBeoDqhD0ejfK3yR5QbkGBdoIVDWCFmIn9dk3p7MX+/4ufp+7eCAlQe066KPDmHVQoI1Azab4mjQ5HAQFagPVjqDXnzxf5ieCAq0dqhU0vnz4GkGBNg0tEJQpHmjzUL2gR4ygQJuHKgRNbpK2uAYF2jxUP4I2SJPDQVCgNhCCAklDlYK6f/axg6BAm4eqBD2z9+/Xu8sNbXI4CArUBqoQtNt/1YmgQG0gBAWShioEZYoH0oGqBOUmCUgGqhS0aZocDoICtYHWKeiF9RJBgdpAZUHdPdLtwfLfxCMo0PqhkqCTob/4PFp+E4+gQGuHSoIeDeafICjQ5qB5QbNvbWi/DoqgQK0hBAWShuYFvT0I659nrf/CMoICtYbmBfW/RiqYiqBAm4NKgsZH7q/SX+82WGdacjgICtQaKgvqvx60wT/4QFCg9UMVgjbPksNBUKDWEIICSUPrFvQCQYHaQAgKJA0hKJA0hKBA0hCCAklDCAokDSEokDTUk6ANDVU/W0C9QwgKJA0hKJA0hKBA0hCCAklDCAokDSEokDSEoEDSEIICSUOtBF2Siwv7nxd0He2T+xNGUCBNCEGBpCEEBZKGEBRIGkJQIGkIQYGkIQQFkoYQFEgaQlAgaQhBgaQhBAWShhAUSBpCUCBpCEGBpCEEBZKGEBRIGkJQIGkIQYGkIQQFkoYQFEgaQlAgaQhBgaShNQt6gaBArSAEBZKGEBRIGkJQIGkIQYGkob4EbWao+tkC6h1CUCBpCEGBpCEEBZKGEBRIGkJQIGlojYJeIChQa2i9gl4gKFA7CEGBpCEEBZKGEBRIGkJQIGkIQYGkIQQFkoYQFEgaQlAgaQhBgaQhBAWShpYLOn06Go3j+Hw0Gn10Gt88Gz1+g6BAfUFLBb358jCefnYYn4ztq9mLcXz+MYIC9QUtFfTK6ngynr08dLp+dRpPPz9FUKCeoOVTvB9FzdRuZ/rpF2/cmBrH75sshOKioIv3JWRhFgo6e7HvZnkzil49DoLaLP55YQQFag81EPTm2X7y7GScjaAICtQHtFzQ6dNxeHoy5hoUqF9oqaCJn3Zun31zamd77uKB+oOWCmrXP+3tkXl8dBizDgrUL7R8il+QxYeDoEDtIQQFkoZ6E7SRoepnC6h3CEGBpCEEBZKGEBRIGkJQIGkIQYGkIQQFkoYQFEgaQlAgaQhBgaQhBAWShhAUSBpCUCBpCEGBpCEEBZKGEBRIGkJQIGkIQYGkIQQFkoYQFEgaQlAgaQhBgaQhBAWShhAUSBpCUCBpCEGBpCEEBZKGEBRIGkJQIGkIQYGkIQQFkoYQFEgaQlAgaQhBgaQhBAWShhAUSBpCUCBpCEGBpCEEBZKG+hO0iaHqZwuod6iVoIszL+gaSpD7EkZQIE0IQYGkoR4FbaCo+tkC6h1CUCBpCEGBpCEEBZKGEBRIGkJQIGkIQYGkIQQFkoYQFEgaWp+gFwgK1B5CUCBpCEGBpKFeBV1qqPrZAuodQlAgaQhBgaQhBAWShhAUSBpCUCBpCEGBpCEEBZKGEBRIGupX0GWGqp8toN4hBAWShhAUSBpCUCBpCEGBpKG+BV3sqPrZAuod6lnQJYOo+tkC6h1CUCBpqH9BFxmqfraAeocQFEgaWpugFwgK1AGEoEDS0AYEXWCo+tkC6h1CUCBpCEGBpCEEBZKGEBRIGtqQoDWWqp8toN6hTQha/3dG1M8WUO/QZgStm+fVzxZQ79BmBP1LzSBa1yKr+/cW2pigxUE0eRn75xWNdd5xoLcDWlXQm2ejx286EjQZRt2DH1PnBE2frSgo30T67kArCjp7MY7PP+5M0PlJP0t4ERrLP/yQLgRkw+5c7eUdrzvq2u7UQ3eqBNQMWlHQm69O4+nnp2sRtPqdvKkXRYuTwTfOLVwF5Icf5mTLvUhcy+2UXRHnmeLzRNB5g+uK5E7xorNUh6lr0yW0eHpcUdDpF2/imy8PzbP3TRbtWRKOkJo0EK+poFePg6A2vf2QAd1baEVBsxEUQYH6gFYUtPk1qHrHgd4OaEVBZy/2G97Fq3cc6O2AVhS0+TqoeseB3g5oVUELEekD0DsMISiQNISgQNIQggJJQwgKJA0hKJA0hKBA0hCCAklDCAokDSEokDSEoEDSEIICSUOtBF2UhX/dnkpUWi0ISiXpSghKJelKCEol6UrdC0pIh0FQIh0EJdJBUCIdBCXSaS1o4V97zr1Zua1Fsn+YP1/pfDQafVTaeOc6T0ejcS+VrqoaW0sl//1vfVSqbO3OldoKWvzWu+Kbldta5Krct1DipHTuW8R+g8r0s8Pim2upZH/i6s5et5ViK8d8g+upVNXa3Su1FTR844j5CXH22NfhzeK3kbTOyaP/sI1VVZq9PFyKN8+VVcacyvVXsqk7e11Xmv7jb8Z9nL3QWkeV2goavrPpJPniEXc4yZvF73PqIM72qkrmZFRMym1S16fuK7mBZf2VZi//007x668UWuuoUltBk2+9s5+nORJzSWWyn7xZ/Ea8DuIGm6pKdkLudByw3/TTS6Xp00eHvVQ637fTbA+Vkta6qtTRCOp+NMypXv8IWlnJbe3wSurm2X7cT6W49ux1W8k06gTtp0+mta4qdXQN6o4ivF7TNWgiaFUl90Z3p3j6dBwKrLmSb62HSud+JHv7+tT+Ln4/XEVdpUtKyZvFb8TrIOEatFTJvjH7pqsfBe9nH5XCRdD6K8XJMlM/fbKtdVSpq3VQ8/DosOLN7tdBKyudj/JvtowfbcY9VAqt9VApEfSt6xO/SSLSQVAiHQQl0kFQIh0EJdJBUCIdBF2S24Od4hs/vgrPzqKd+b3n2b3VC55FW8/n3zM1rz8pvXsvgqBLMi/o5CfP0y3/sH28kD0brF7venev9J6refnw9eqtvf1B0CWpF/Ry+9vh3iL0ToNe1v7ce3cajt/+IOiSpILeHkTRIJ4MozCxHz38/sAMkZOf/mu0fWy32vHUbg87nNkxz72xZ0fGaOu3P3nuXLN/JDsWab/7IA6v/WNS834OoQi6JEHQ24OB+y8d4a53d+IzY9FkOPBbrZBugj7zrjnSCzncu94dGGIrFTTsWKAtZXcIr8Ojr1k1tr77QdAlCYJeWuvMH6kml07OPfef32qk+6uVLNnFOTj56XEOzwQNOxboOIHD6/DoG6y6On33g6BLkgrq5utkjrY5SoY+98ZZ5GInYvOwlQkaH9kpO0z3uSk+2XGO9tvC6/Doa5bWE+5FEHRJ6gQ115Q2yZCazM9uFi+MoH7P7eN5QcOOBdrGCZq8Do8ISmqTCmqHxdwU7y80zQzth8Nk7dJpfLlVEDSbrXNTfNixQNu4KT60ljwyxZPazN8kJZq4+xf3kKwBGeGMT1apyTDx6mgnubw0u3h067m9tbo9yHYs0BbyN0n+dXj0NblJIhWxKz3u+tAvM9mLSvvnJFkCPbNrR8lu1jBzybn178lQ50fJ5JrU7vDLZHnp1588DztOCnScrnlmkG91ULwQuD9B0PWltFDfagxkoZ50nflfdbYS9H6u0yPoOjM/6LURlL8sQohgEJRIB0GJdBCUSAdBiXQQlEgHQYl0/h9idmiffh3sVgAAAABJRU5ErkJggg==" /><!-- --></p>
<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1" tabindex="-1"></a><span class="co">#Scatterplot of Sale Price vs Lot Area in Ames Properties</span></span>
<span id="cb24-2"><a href="#cb24-2" tabindex="-1"></a><span class="fu">ggplot</span>(data, <span class="fu">aes</span>(<span class="at">x =</span> <span class="st">`</span><span class="at">LotArea(sf)</span><span class="st">`</span>, <span class="at">y =</span> <span class="st">`</span><span class="at">Sale Price</span><span class="st">`</span>)) <span class="sc">+</span></span>
<span id="cb24-3"><a href="#cb24-3" tabindex="-1"></a> <span class="fu">geom_point</span>(<span class="at">color =</span> <span class="st">'darkgreen'</span>) <span class="sc">+</span></span>
<span id="cb24-4"><a href="#cb24-4" tabindex="-1"></a> <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">"Sale Price vs Lot Area in Ames Properties"</span>, </span>
<span id="cb24-5"><a href="#cb24-5" tabindex="-1"></a> <span class="at">x =</span> <span class="st">"Lot Area (square feet)"</span>, </span>
<span id="cb24-6"><a href="#cb24-6" tabindex="-1"></a> <span class="at">y =</span> <span class="st">"Sale Price ($)"</span>)</span></code></pre></div>
<pre><code>## Warning: Removed 89 rows containing missing values or values outside the scale range (`geom_point()`).</code></pre>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p>The scatter plot of Sale Price vs Lot Area shows a positive
relationship, indicating that larger lot sizes generally lead to higher
sale prices. However, there are many points with large lot areas and
lower-than-expected sale prices, implying that other factors may be
influencing the value.</p>
<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" tabindex="-1"></a><span class="co"># Scatterplot with trend line</span></span>
<span id="cb26-2"><a href="#cb26-2" tabindex="-1"></a><span class="fu">ggplot</span>(data, <span class="fu">aes</span>(<span class="at">x =</span> <span class="st">`</span><span class="at">LotArea(sf)</span><span class="st">`</span>, <span class="at">y =</span> <span class="st">`</span><span class="at">Sale Price</span><span class="st">`</span>)) <span class="sc">+</span></span>
<span id="cb26-3"><a href="#cb26-3" tabindex="-1"></a> <span class="fu">geom_point</span>(<span class="at">color =</span> <span class="st">'darkblue'</span>) <span class="sc">+</span></span>
<span id="cb26-4"><a href="#cb26-4" tabindex="-1"></a> <span class="fu">geom_smooth</span>(<span class="at">method =</span> <span class="st">"lm"</span>, <span class="at">color =</span> <span class="st">'red'</span>, <span class="at">se =</span> <span class="cn">FALSE</span>) <span class="sc">+</span></span>
<span id="cb26-5"><a href="#cb26-5" tabindex="-1"></a> <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">"Sale Price vs Lot Area with Trend Line"</span>, </span>
<span id="cb26-6"><a href="#cb26-6" tabindex="-1"></a> <span class="at">x =</span> <span class="st">"Lot Area (square feet)"</span>, </span>
<span id="cb26-7"><a href="#cb26-7" tabindex="-1"></a> <span class="at">y =</span> <span class="st">"Sale Price ($)"</span>)</span></code></pre></div>
<pre><code>## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 89 rows containing non-finite outside the scale range (`stat_smooth()`).
## Warning: Removed 89 rows containing missing values or values outside the scale range (`geom_point()`).</code></pre>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p>The scatter plot with the <strong>trend line</strong> shows a
downward slope, which could indicate that there isn’t a straightforward,
consistent linear relationship between <strong>Lot Area</strong> and
<strong>Sale Price</strong> for all properties. This suggests
variability and possibly the influence of other characteristics.</p>
<div class="sourceCode" id="cb28"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1" tabindex="-1"></a><span class="co"># Histogram of Sale Price</span></span>
<span id="cb28-2"><a href="#cb28-2" tabindex="-1"></a><span class="fu">ggplot</span>(data, <span class="fu">aes</span>(<span class="at">x =</span> <span class="st">`</span><span class="at">Sale Price</span><span class="st">`</span>)) <span class="sc">+</span></span>
<span id="cb28-3"><a href="#cb28-3" tabindex="-1"></a> <span class="fu">geom_histogram</span>(<span class="at">binwidth =</span> <span class="dv">25000</span>, <span class="at">fill =</span> <span class="st">'skyblue'</span>, <span class="at">color =</span> <span class="st">'black'</span>) <span class="sc">+</span></span>
<span id="cb28-4"><a href="#cb28-4" tabindex="-1"></a> <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">"Distribution of Sale Prices in Ames Properties"</span>, </span>
<span id="cb28-5"><a href="#cb28-5" tabindex="-1"></a> <span class="at">x =</span> <span class="st">"Sale Price ($)"</span>, </span>
<span id="cb28-6"><a href="#cb28-6" tabindex="-1"></a> <span class="at">y =</span> <span class="st">"Count"</span>)</span></code></pre></div>
<h2><img 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" 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