論文代寫推薦：房屋的因變量與價格的關係

論文代寫推薦：房屋的因變量與價格的關係
很明顯，房子的價格與房子裡的浴室數量，房子的年齡和房子的地塊面積有關。房屋面積與其他變量之間的相關性無統計學意義。然而，地塊面積與浴室數量、價格和住宅面積之間存在顯著的相關關係。這意味著這些變量之間可能存在潛在的多重共線性。然而，多重共線性的最後檢驗是通過VIF(方差膨脹因子)來完成的。工作規則是VIF應小於5或公差(=1/VIF)應大於0.2。表9給出了各變量的VIF和容差，可見模型不存在多重共線性。虛擬變量d98004、d98006、d98040和d98166對應郵政編碼98004、98006、98040和98166。選擇郵政編碼98125作為基本類別是因為它有最多的觀察值。最終開發的模型如表8所示。所建立的模型的因變量是房地產價格的對數。與價格、房屋面積和地段面積相比，與臥室、浴室的數量和房屋的使用年限相對應的變量非常小。
尺度上的差異常常導致異方差。因此，高量值的變量以對數形式使用，而其他變量保持原樣。對數尺度的使用也確保了模型中沒有異常值。為保證最終模型不存在異方差，使用hettest，結果如表9所示。利用最小二乘法對模型進行估計。浴室數量係數在1%顯著水平下顯著。係數是0.26，這意味著每增加一個單位的浴室數量，價格的對數預計會上升0.26。郵政編碼98004對應的係數啞變量值為0.87，在1%顯著水平下顯著。這意味著與基準面積相比，房價的log在郵政編碼98004中要高出0.87倍。同樣，郵政編碼98040的啞變量係數在1%顯著水平下顯著，係數為0.5。這意味著與基本區域相比，郵政編碼98040中的價格日誌要高出50%。

論文代寫推薦：房屋的因變量與價格的關係

It is evident that the price of the houses is correlated with the number of bathrooms in the house, age of the house and the lot area of the house. The correlation between the house area and the other variables is not statistically significant. However, the correlation between lot area and number of bathrooms, price and house area are significant. This implies that potential multicollinearity may exist between these sets of variables. However, the final check of multicollinearity is done via VIF (Variance Inflation Factor). The working rule is that VIF should be less than 5 or the tolerance (=1/VIF) should be more than 0.2. Table 9 shows the VIF and the tolerance of the variables and it is evident that the model is free from multicollinearity.The dummy variables d98004, d98006, d98040 and d98166 correspond to the zip codes 98004, 98006, 98040 and 98166. The zip code 98125 is chosen as the base category because it has the most number of observations. The final model developed is shown in table 8. The dependent variable in the model developed is the log of the price of the properties. The variables corresponding to number of bedrooms, bathrooms and the age of the house are very small in magnitude as compared to the price, house area and lot area.
The difference in scale often leads to heteroskedasticity. Therefore, the variables high in magnitude are used in their logarithmic form while the other variables are left as they are. The use of logarithmic scale also ensures that there are no outliers in the model. To ensure that the final model is free from heteroskedasticity, hettest is used whose result is shown in table 9. The estimation of the model is done by the OLS technique. The coefficient of number of bathrooms is significant at 1% level of significance. The coefficient is 0.26 which implies that for every unit increase in the number of bathrooms in the house, the log of price is expected to go up by 0.26. The coefficient dummy variable corresponding to zip code 98004 has a value of 0.87 and is significant at 1% level of significance. This implies that compared to the base area, the log of price of the houses are 0.87 times higher in the zip code 98004. Similarly, the coefficient of dummy variable for zip code 98040 is significant at 1% level of significance and the coefficient is 0.5. This implies that that compared to the base area the log of prices are 50% higher in the zip code 98040.