class: center, middle, inverse, title-slide .title[ # <font size="7" color="red">Taipei Real Estate House Prices </font> ] .subtitle[ ## <font size="6" color="forestgreen">EDA and Linear Regression Analysis </font> ] .author[ ### <font size="5" color="royalblue">Andrew Heneghan<br>Julia Randazzo<br>Boning Liu<br>Mikaela Taylor </font> ] .institute[ ### <img 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" height="100"><br><font size="3" color="gold">West Chester University of Pennsylvania</font><br> ] .date[ ### <font color="skyblue" size="4"> 12/15/2022 <font> <br> <font color="white" size="4"> STA 490: Capstone Statistics </font> ] --- class: middle, center # <font color = "red">Agenda</font> <font size = 5>Analysis Subtasks and Research Question</font><br> <font size = 5>Descriptive Statistics</font><br> <font size = 5>Region Variable and Mapping</font><br> <font size = 5>Model Diagnostics</font><br> <font size = 5>Box-Cox Transformation</font><br> <font size = 5>New Candidate Models (Log-Transformed/Interactions)</font><br> <font size = 5>Choosing an Optimal Model</font><br> <font size = 5>Conclusion</font><br> --- class: middle, center ## <center>Subtasks</center> <img src="data:image/png;base64,#https://raw.githubusercontent.com/BrianL0201/BrianL0201.github.io/main/Subtasks.png" width="100%" style="display: block; margin: auto;" /> --- class: middle, center ## Research Question ### How do certain aspects of houses in Taipei affect house price of unit area? <img src = "https://azheneghan.github.io/aheneghan/images/House_Prices.png" width="250" height="220"> --- class: top ## <center>Description of Data</center> .pull-left[ - Data collected of houses in Taipei, Taiwan by real estate investors. - 414 independent houses in or near Taipei were randomly sampled. - Consists of 8 variables, including an ID variable. - House price of unit area acts as the dependent variable. - Transaction date is categorical since it can only be 2012 and 2013. ] .pull-right[ - The independent variables are: + year of the transaction date (X1) + house age (X2) + distance from house to nearest MRT station (X3) + number of nearby convenience stores (X4) + latitude (X5) + longitude (X6). <img src = "https://azheneghan.github.io/aheneghan/images/taipei.png" width="600" height="250"> ] --- class: top ## <center>Descriptive Statistics</center> - We viewed the first six observations of the data. - We then looked at the data structure which described observation count (414), variable count (8), and our types of variables (all numerical except TransactionYear). - We then examined the variables’ means, medians, standard deviations, minimum and maximum values, ranges, and standard errors. - We also made a histogram the distribution of values of the response variable, house price of unit area. <img src="data:image/png;base64,#Taipei-Realestate-Linear-Regression-HTML-Presentation_files/figure-html/unnamed-chunk-3-1.png" style="display: block; margin: auto;" /> --- class: top ## <center>New Region Variable and Mapping</center> We combined the Longitude and Latitude variables into one variable, called Region. This map shows the locations, using latitude and longitude, of all the houses in Taipei that were sampled. This map helped to create the new Region variable. <center> <div id="htmlwidget-c52ade0631d4146cd65e" style="width:576px;height:360px;" class="leaflet html-widget"></div> <script type="application/json" data-for="htmlwidget-c52ade0631d4146cd65e">{"x":{"options":{"crs":{"crsClass":"L.CRS.EPSG3857","code":null,"proj4def":null,"projectedBounds":null,"options":{}}},"calls":[{"method":"addTiles","args":["https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",null,null,{"minZoom":0,"maxZoom":18,"tileSize":256,"subdomains":"abc","errorTileUrl":"","tms":false,"noWrap":false,"zoomOffset":0,"zoomReverse":false,"opacity":1,"zIndex":1,"detectRetina":false,"attribution":"© <a href=\"https://openstreetmap.org\">OpenStreetMap<\/a> contributors, <a 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1<br>"],null,null,null,null,{"interactive":false,"permanent":false,"direction":"auto","opacity":1,"offset":[0,0],"textsize":"10px","textOnly":false,"className":"","sticky":true},null]}],"limits":{"lat":[24.93207,25.01459],"lng":[121.47353,121.56627]}},"evals":[],"jsHooks":[]}</script> </center> --- class: top ## <center>Diagnostics</center> .pull-left[ <img src="data:image/png;base64,#Taipei-Realestate-Linear-Regression-HTML-Presentation_files/figure-html/unnamed-chunk-6-1.png" style="display: block; margin: auto;" /> ] .pull-right[ - All independent variables are linearly correlated with house price of unit area. - All independent variables are not linear correlated with each other, so there likely is no collinearity. - There is one outlier (Obs. 271) in the data. This house observation has a value of 117.5. ] --- class: top ## <center>Diagnostics (cont'd)</center> .pull-left[ <img src="data:image/png;base64,#Taipei-Realestate-Linear-Regression-HTML-Presentation_files/figure-html/unnamed-chunk-7-1.png" style="display: block; margin: auto;" /> ] .pull-right[ - <font size = 4 color = "black">The lines on the Residuals vs Fitted and Scale-Location graphs are horizontal. Points on the Scale-Location graph are evenly spread. The relationship of the residuals is linear and there is homogeneity of variances.</font><br> - <font size = 4 color = "black">Some of the points on the Normal QQ plot are not on the line. Therefore, the residuals are not normally distributed.</font> ] --- class: middle ## <center>Box-Cox Transformation</center> <img src="data:image/png;base64,#Taipei-Realestate-Linear-Regression-HTML-Presentation_files/figure-html/unnamed-chunk-8-1.png" style="display: block; margin: auto;" /> - When this transformation is used, it does not make the analysis easy to understand. - Since 0 and 1 are not within the 95% confidence interval of λ, it indicates that performing the power transformation is not a good idea. - Since the optimal λ is closer to 0.25, we will perform a log transformation on the model. </font> --- class: top ## <center>Log-Transformed Model</center> .pull-left[ - More points now fall on the line for the Normal QQ Plot compared to for the initial model.<br> - The line on the Scale-Location is a little more slanted compared to the initial model, but points are still spread evenly. ] .pull-right[ <img src="data:image/png;base64,#Taipei-Realestate-Linear-Regression-HTML-Presentation_files/figure-html/unnamed-chunk-9-1.png" style="display: block; margin: auto;" /> ] --- class: middle, center ## <center>Interactions Model</center> <font size = 3 color = "black">Since the interaction effects are insignificant, we used the automatic variable selection method to find the final model.<br>The resulting residual plots were similar to the initial model.</font> <img src="data:image/png;base64,#Taipei-Realestate-Linear-Regression-HTML-Presentation_files/figure-html/unnamed-chunk-10-1.png" style="display: block; margin: auto;" /> --- class: middle, center ##<center>Interactions Model Summary</center> <img src="data:image/png;base64,#https://raw.githubusercontent.com/BrianL0201/BrianL0201.github.io/main/Summarized%20Statistics.png" width="100%" style="display: block; margin: auto;" /> --- class: middle, center ## <center>Choosing an Optimal Model</center> Table: Coefficients of correlation of the three candidate models | model| model.transform| model.final| |--------:|---------------:|-----------:| | 0.604729| 0.6890686| 0.7051469| <br><font size = 4 color = "black">The interaction model has the highest R^2 of 69.56%. The log-transformed model has the second biggest R^2 of 68.55%. The initial model has the third highest R^2 of 60.18%. The interpretation of the final model is not as straightforward as the others due to all the insignificant interactions. We believe the log-transformed model is the optimal model to report since it has a high R^2 value, a simple structure, all significant p-values, and is easy to interpret. </font> --- class: middle, center ## <center>Results</center> Table: Summary of the final working model | | Estimate| Std. Error| t value| Pr(>&#124;t&#124;)| |:---------------|------------:|----------:|---------:|------------------:| |(Intercept) | -157.1661789| 47.6825588| -3.296094| 0.0010666| |TransactionYear | 0.0799961| 0.0236917| 3.376540| 0.0008048| |HouseAge | -0.0076916| 0.0009731| -7.904176| 0.0000000| |Distance2MRT | -0.0001552| 0.0000173| -8.947899| 0.0000000| |NumConvenStores | 0.0303354| 0.0047710| 6.358351| 0.0000000| |Region2 | -0.0864295| 0.0545576| -1.584188| 0.1139294| |Region3 | -0.2044123| 0.0268185| -7.622074| 0.0000000| |Region4 | -0.1883889| 0.0513857| -3.666172| 0.0002789| <font size = 4 color = "black">All independent variables, except Region2, are statistically significant (P-value < alpha=0.05) towards the model. TransactionYear and the Number of Convenience Stores variables are positively correlated while all other variables are negatively correlated.</font> --- class: center ## <center>Conclusion</center> - We aimed to see how transaction year, house age, distance to MRT station, number of convenience stores, and region, affect the price of houses in Taipei, Taiwan. - Most variables were significant when predicting the Taipei home prices, except for Region2. - Transaction year and number of convenience stores have positive relationships with house price, while house age, distance to MRT station, and region have negative relationships with house price. - The log-transformed model worked best as the final model for a linear regression analysis of this data. - It would be beneficial to see how new factors, such as home size, upgrades and updates, interest rates, etc., may interact to determine the price of a home in Taipei. --- class: middle, center ## <center>References</center> Sibanda, Hastings. “Taipei Housing Dataset UCI.” Kaggle, 30 Jan. 2022, https://www.kaggle.com/datasets/hastingssibanda/taipei-housing-dataset-uci. --- class: middle, center <img src="data:image/png;base64,#https://raw.githubusercontent.com/BrianL0201/BrianL0201.github.io/main/Team%20Signatures.png" width="75%" style="display: block; margin: auto;" />