class: center, middle, inverse, title-slide .title[ # <font size="7" color="black">Simple Linear Regression Analysis of:<br></font> <font size="7" color="white"><i>Taipei Real Estate Data</i></font> ] .subtitle[ ## <img 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" height="20%," width="20%"> ] .author[ ### <font size="5" color="DarkRed"> <i>Natalie LePera </i></font> ] .institute[ ### <font size="6" color="white">West Chester University of Pennsylvania</font> ] .date[ ### <font color="white" size="4"> Prepared for<br> </font> <font color="DarkRed" size="6"> STA553: Data Visualization (Week 02) </font> <br> <font color="white" size="3"> Slides available at:</font> <font = “darkred” size = 3> <a href="https://rpubs.com/nlepera" class="uri">https://rpubs.com/nlepera</a> </font> <font color="white" size="3">AND</font> <font color="darkred" size="3"> <a href="https://github.com/nlepera/sta553" class="uri">https://github.com/nlepera/sta553</a> </font> ] --- class:inverse4, top <h1 align="center"> Table of Contents</h1> <BR> - Summary of Data - Define that Data - Overall House Price Distribution - Does Year of Sale Impact Price? - Does House Age Impact Price? - Does Distance to Nearest MRT Station Impact Price? - Does # of Convenience Stores Nearby Impact Price? - Does Latitude Impact Price? - Does Longitude Impact Price? - Conclusions --- name: Data-Summary <h1 align = "center">Summary of Data</h1> **Raw Summary** ```{.bg.output} ## tibble [414 × 7] (S3: tbl_df/tbl/data.frame) ## $ TransactionYear: num [1:414] 2012 2012 2013 2013 2012 ... ## $ HouseAge : num [1:414] 32 19.5 13.3 13.3 5 7.1 34.5 20.3 31.7 17.9 ... ## $ Distance2MRT : num [1:414] 84.9 306.6 562 562 390.6 ... ## $ NumConvenStores: num [1:414] 10 9 5 5 5 3 7 6 1 3 ... ## $ Latitude : num [1:414] 25 25 25 25 25 ... ## $ Longitude : num [1:414] 122 122 122 122 122 ... ## $ PriceUnitArea : num [1:414] 37.9 42.2 47.3 54.8 43.1 32.1 40.3 46.7 18.8 22.1 ... ``` <br> **What does this mean?** - 7 Variables - Details on next slide - 414 Observations --- name: Variable-Definition class: inverse2 <h2 align = "center">Define that Data</h2> <table align = "center" width = 90% bgcolor="#DEB887"> <tbody> <tr> <th>Variable Name (Full)</th> <th>Variable Name (Abrv.)</th> <th>Units/Details </th> <th>Purpose </th> </tr> <tr> <td>Transaction Year </td> <td class="td1">TransactionYear</td> <td>Year of Construction (details)</td> <td>To determine if year of home construction impacts home price.</td> </tr> <tr> <td>House Age </td> <td class="td1">HouseAge</td> <td>Years (unit)</td> <td>To determine if home age impacts home price.</td> </tr> <tr> <td>Distance to Nearest MRT Station</td> <td class="td1">Distance2MRT</td> <td>Meters (unit)</td> <td>To determine if distance from nearest MRT station impacts home price.</td> </tr> <tr> <td>Number of Nearby Convenience Stores</td> <td class="td1">NumConvenStores</td> <td>Count of Convenience Stores (details)</td> <td class="td1">To determine if the number of local convenience stores home average price.</td> </tr> <tr> <td>Latitude</td> <td class="td1">Latitude</td> <td>Latitude of Home (details)</td> <td>To determine if latitude of home impacts home price.</td> </tr> <tr> <td>Longitude</td> <td class="td1">Longitude</td> <td>Longitude of Home (details)</td> <td>To determine if longitude of home impacts home price.</td> </tr> <tr> <td>Price per Unit Area</td> <td class="td1">PriceUnitArea</td> <td>Dollars per Unit Area ex: m<sup>2</sup> (unit)</td> <td>Primary observation! To determine home price. </td> </tr> </tbody> --- name: price.distribution class: inverse3 <h1 align="center">Overall Distribution of Price Data</h1> .pull-left[ <!-- --> ] .pull-right[ <!-- --> ] --- name:price.year class:inverse <h1 align="center" color="#70384A">Does Year of Sale Impact Price?</h1> .pull-left[ <!-- --> ] .pull_right[ <br> lm(formula = Price ~ Year, data = year.price) Residuals: <table border = 0> <tr> <th>Min</th> <th>1Q</th> <th>Median</th> <th>3Q</th> <th>Max</th> </tr> <tr> <td>-31.113</td> <td>-10.261</td> <td>0.891</td> <td>8.537</td> <td>78.787</td> <tr/> </table> Coefficients: <table border = 0> <tr> <th></th> <th>Estimate</th> <th>Std.Error</th> <th>t value</th> <th>Pr(>|t|)</th> </tr> <tr> <td>(Intercept)</td> <td>-4809.462 </td> <td>2918.908</td> <td>-1.648</td> <td>0.1002 </td> <tr/> <tr> <td>Year</td> <td>2.408 </td> <td>1.450</td> <td>1.661</td> <td> 0.0975</td> <tr/> </table> <b>Residual standard error:</b> 13.58 on 412 degrees of freedom <p><b>Multiple R-squared:</b> 0.00665, <b>Adjusted R-squared:</b> 0.004238 </p> <p><b>F-statistic:</b> 2.758 on 1 and 412 DF, <b>p-value:</b> 0.09753<p> ] --- name:price.age class:inverse <h1 align="center">Does Age of House Impact Price?</h1> .pull-left[ <!-- --> ] .pull_right[ <br> lm(formula = Price ~ Age, data = age.price) Residuals: <table border = 0> <tr> <th>Min</th> <th>1Q</th> <th>Median</th> <th>3Q</th> <th>Max</th> </tr> <tr> <td>-31.113</td> <td>-10.738</td> <td>1.626 </td> <td>8.199 </td> <td>77.781 </td> <tr/> </table> Coefficients: <table border = 0> <tr> <th></th> <th>Estimate</th> <th>Std.Error</th> <th>t value</th> <th>Pr(>|t|)</th> </tr> <tr> <td>(Intercept)</td> <td>42.43470</td> <td>1.21098</td> <td>35.042</td> <td>< 2e-16 ***</td> <tr/> <tr> <td>Age</td> <td>-0.25149</td> <td>0.05752</td> <td>-4.372</td> <td>1.56e-05 ***</td> <tr/> </table> <b>Residual standard error:</b> 13.32 on 412 degrees of freedom <p><b>Multiple R-squared:</b> 0.04434, <b>Adjusted R-squared:</b> 0.04202 </p> <p><b>F-statistic:</b> 19.11 on 1 and 412 DF, <b>p-value:</b> 1.56e-05<p> ] --- name:price.MRT class:inverse <h1 align="center">Does Distance to MRT Station Impact Price?</h1> .pull-left[ <!-- --> ] .pull_right[ <br> lm(formula = Price ~ MRT, data = mrt.price) Residuals: <table border = 0> <tr> <th>Min</th> <th>1Q</th> <th>Median</th> <th>3Q</th> <th>Max</th> </tr> <tr> <td>-35.396</td> <td>-6.007</td> <td>-1.195</td> <td>4.831</td> <td>73.483</td> <tr/> </table> Coefficients: <table border = 0> <tr> <th></th> <th>Estimate</th> <th>Std.Error</th> <th>t value</th> <th>Pr(>|t|)</th> </tr> <tr> <td>(Intercept)</td> <td>45.8514271</td> <td>0.6526105</td> <td>70.26</td> <td><2e-16 ***</td> <tr/> <tr> <td>MRT</td> <td>-0.0072621</td> <td>0.0003925</td> <td>-18.50</td> <td><2e-16 ***</td> <tr/> </table> <b>Residual standard error:</b> 10.07 on 412 degrees of freedom <p><b>Multiple R-squared:</b> 0.4538, <b>Adjusted R-squared:</b>0.4524</p> <p><b>F-statistic:</b> 342.2 on 1 and 412 DF, <b>p-value:</b>< 2.2e-16<p> ] class: inverse1 center middle --- name:price.convenience class:inverse <h1 align="center">Does # of Convenience Stores Impact Price?</h1> .pull-left[ <!-- --> ] .pull_right[ <br> lm(formula = Price ~ Conv, data = conv.price) Residuals: <table border = 0> <tr> <th>Min</th> <th>1Q</th> <th>Median</th> <th>3Q</th> <th>Max</th> </tr> <tr> <td>-35.407</td> <td>-7.341</td> <td>-1.788</td> <td>5.984</td> <td>87.681</td> <tr/> </table> Coefficients: <table border = 0> <tr> <th></th> <th>Estimate</th> <th>Std.Error</th> <th>t value</th> <th>Pr(>|t|)</th> </tr> <tr> <td>(Intercept)</td> <td>27.1811</td> <td>0.9419</td> <td>28.86</td> <td><2e-16 ***</td> <tr/> <tr> <td>Conv</td> <td>2.6377</td> <td>0.1868</td> <td>14.12</td> <td><2e-16 ***</td> <tr/> </table> <b>Residual standard error:</b> 11.18 on 412 degrees of freedom <p><b>Multiple R-squared:</b> 0.326, <b>Adjusted R-squared:</b> 0.3244 </p> <p><b>F-statistic:</b> 199.3 on 1 and 412 DF, <b>p-value:</b>< 2.2e-16<p> ] class: inverse1 center middle --- name:price.latitude class:inverse <h1 align="center">Does Latitude Impact Price?</h1> .pull-left[ <!-- --> ] .pull_right[ <br> lm(formula = Price ~ Long, data = long.price) Residuals: <table border = 0> <tr> <th>Min</th> <th>1Q</th> <th>Median</th> <th>3Q</th> <th>Max</th> </tr> <tr> <td>-37.969</td> <td>-7.347</td> <td>-1.392</td> <td>5.685</td> <td>76.184</td> <tr/> </table> Coefficients: <table border = 0> <tr> <th></th> <th>Estimate</th> <th>Std.Error</th> <th>t value</th> <th>Pr(>|t|)</th> </tr> <tr> <td>(Intercept)</td> <td>-14917.68</td> <td>1129.66</td> <td>-13.21</td> <td><2e-16 ***</td> <tr/> <tr> <td>Lat</td> <td>598.97</td> <td>45.24</td> <td>13.24</td> <td><2e-16 ***</td> <tr/> </table> <b>Residual standard error:</b> 11.41 on 412 degrees of freedom <p><b>Multiple R-squared:</b> 0.2985, <b>Adjusted R-squared:</b>0.2967</p> <p><b>F-statistic:</b> 175.3 on 1 and 412 DF, <b>p-value:</b>< 2.2e-16<p> ] --- name:price.longitude class:inverse <h1 align="center">Does Longitude Impact Price?</h1> .pull-left[ <!-- --> ] .pull_right[ <br> lm(formula = Price ~ Long, data = long.price) Residuals: <table border = 0> <tr> <th>Min</th> <th>1Q</th> <th>Median</th> <th>3Q</th> <th>Max</th> </tr> <tr> <td>-32.588</td> <td>-5.693</td> <td>-0.417</td> <td>6.157</td> <td>80.866</td> <tr/> </table> Coefficients: <table border = 0> <tr> <th></th> <th>Estimate</th> <th>Std.Error</th> <th>t value</th> <th>Pr(>|t|)</th> </tr> <tr> <td>(Intercept)</td> <td>27.1811</td> <td>0.9419</td> <td>28.86</td> <td><2e-16 ***</td> <tr/> <tr> <td>Long</td> <td>463.93</td> <td>37.22</td> <td>12.46</td> <td><2e-16 ***</td> <tr/> </table> <b>Residual standard error:</b> 11.61 on 412 degrees of freedom <p><b>Multiple R-squared:</b> 0.2738, <b>Adjusted R-squared:</b>0.2721</p> <p><b>F-statistic:</b> 155.4 on 1 and 412 DF, <b>p-value:</b>< 2.2e-16<p> ] --- name:Conclusions class:inverse4 <h1 align="center">Analysis Conclusions</h1> - Multiple linear regressions show statistically significant p values (p<0.05) - Age of Home (p<1.56e-05) - Distance to MRT Station (p<2.2e-16) - Number of Nearby Convenience stores (p<2.2e-16) - Latitude (p<2.2e-16) - Longitude (p<2.2e-16) - F-Statistic acts as secondary check to Variance when p value is significant - Age of Home (19.11 on 1 and 412 DF) - House price seen in Taipei real-estate in 2012-2013 is most closely correlated with <b>Age of House</b>