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find the best fit model to predict the price of a property in Chicago. You are considering the following regression models: Model1: use square feet as the independent variable. Model2: use square feet and beds as the independent variables. Model3: use property type, square feet, and beds as the independent variables. Complete the following statements: 1. The RSquare for Model1 is 2. The RSquare Adj for Model1 is 3. Model1 predicts the price for a property of 3,000sqft to be 4. The RSquare for Model2 is 5. The RSquare Adj for Model2 is 6. Model2 predicts the price for a property of 3,000sqft with 3 bedrooms to be 7. The RSquare for Model3 is 8. The RSquare Adj for Model3 is 9. Model3 predicts the price for a single family residential property of 3.000sqft with 3 bedrooms to be \begin{tabular}{|r|r|c|r|r|r|r|r|} \hline \multicolumn{1}{|l|}{} & PRICE & PROPERTY & SQUARE & LOT & & \multicolumn{1}{|c|}{ YEAR } \\ \hline 1 & TYPE & FEET & SIZE & BEDS & BATHS & BUILT \\ \hline 1 & 330000 & Single Family ... & 1971 & 5187 & 4 & 2.5 & 1929 \\ \hline 2 & 479900 & Single Family ... & 2850 & 5079 & 4 & 3.5 & 1924 \\ \hline 3 & 439000 & Single Family ... & 1400 & 10153 & 3 & 3 & 1922 \\ \hline 4 & 600000 & Single Family ... & 1500 & 2905 & 3 & 2 & 1998 \\ \hline 5 & 315000 & Single Family ... & 1324 & 3720 & 3 & 1.5 & 1951 \\ \hline 6 & 224900 & Single Family ... & 2550 & 4822 & 4 & 3 & 1920 \\ \hline 7 & 239900 & Single Family ... & 2840 & 4438 & 5 & 2 & 1910 \\ \hline 8 & 439000 & Townhouse & 2500 & 3000 & 3 & 3 & 2000 \\ \hline 9 & 579000 & Condo/Co-op & 2650 & 8512 & 3 & 2.5 & 2017 \\ \hline 10 & 59900 & Single Family ... & 1328 & 4338 & 4 & 2 & 1922 \\ \hline 11 & 699900 & Single Family ... & 4428 & 8498 & 4 & 3 & 2007 \\ \hline 12 & 120000 & Multi-Family (5 & 7005 & 3049 & 15 & 6 & 1894 \\ \hline 13 & 295000 & Single Family ... & 1311 & 3689 & 3 & 1.5 & 1946 \\ \hline 14 & 157000 & Single Family ... & 1250 & 4216 & 3 & 1 & 1913 \\ \hline 15 & 489900 & Townhouse & 1850 & 536 & 3 & 2.5 & 1997 \\ \hline 16 & 286900 & Single Family ... & 2150 & 4921 & 5 & 2 & 1966 \\ \hline 17 & 159900 & Single Family ... & 968 & 4164 & 3 & 1.5 & 1944 \\ \hline 18 & 135000 & Single Family ... & 1558 & 5000 & 4 & 2 & 1949 \\ \hline 19 & 415000 & Single Family ... & 2268 & 3720 & 4 & 3 & 2000 \\ \hline \end{tabular} find the best fit model to predict the price of a property in Chicago. You are considering the following regression models: Model1: use square feet as the independent variable. Model2: use square feet and beds as the independent variables. Model3: use property type, square feet, and beds as the independent variables. Complete the following statements: 1. The RSquare for Model1 is 2. The RSquare Adj for Model1 is 3. Model1 predicts the price for a property of 3,000sqft to be 4. The RSquare for Model2 is 5. The RSquare Adj for Model2 is 6. Model2 predicts the price for a property of 3,000sqft with 3 bedrooms to be 7. The RSquare for Model3 is 8. The RSquare Adj for Model3 is 9. Model3 predicts the price for a single family residential property of 3.000sqft with 3 bedrooms to be \begin{tabular}{|r|r|c|r|r|r|r|r|} \hline \multicolumn{1}{|l|}{} & PRICE & PROPERTY & SQUARE & LOT & & \multicolumn{1}{|c|}{ YEAR } \\ \hline 1 & TYPE & FEET & SIZE & BEDS & BATHS & BUILT \\ \hline 1 & 330000 & Single Family ... & 1971 & 5187 & 4 & 2.5 & 1929 \\ \hline 2 & 479900 & Single Family ... & 2850 & 5079 & 4 & 3.5 & 1924 \\ \hline 3 & 439000 & Single Family ... & 1400 & 10153 & 3 & 3 & 1922 \\ \hline 4 & 600000 & Single Family ... & 1500 & 2905 & 3 & 2 & 1998 \\ \hline 5 & 315000 & Single Family ... & 1324 & 3720 & 3 & 1.5 & 1951 \\ \hline 6 & 224900 & Single Family ... & 2550 & 4822 & 4 & 3 & 1920 \\ \hline 7 & 239900 & Single Family ... & 2840 & 4438 & 5 & 2 & 1910 \\ \hline 8 & 439000 & Townhouse & 2500 & 3000 & 3 & 3 & 2000 \\ \hline 9 & 579000 & Condo/Co-op & 2650 & 8512 & 3 & 2.5 & 2017 \\ \hline 10 & 59900 & Single Family ... & 1328 & 4338 & 4 & 2 & 1922 \\ \hline 11 & 699900 & Single Family ... & 4428 & 8498 & 4 & 3 & 2007 \\ \hline 12 & 120000 & Multi-Family (5 & 7005 & 3049 & 15 & 6 & 1894 \\ \hline 13 & 295000 & Single Family ... & 1311 & 3689 & 3 & 1.5 & 1946 \\ \hline 14 & 157000 & Single Family ... & 1250 & 4216 & 3 & 1 & 1913 \\ \hline 15 & 489900 & Townhouse & 1850 & 536 & 3 & 2.5 & 1997 \\ \hline 16 & 286900 & Single Family ... & 2150 & 4921 & 5 & 2 & 1966 \\ \hline 17 & 159900 & Single Family ... & 968 & 4164 & 3 & 1.5 & 1944 \\ \hline 18 & 135000 & Single Family ... & 1558 & 5000 & 4 & 2 & 1949 \\ \hline 19 & 415000 & Single Family ... & 2268 & 3720 & 4 & 3 & 2000 \\ \hline \end{tabular}
