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Chad Dobson has heard about the positive outlook for real estate investment in college towns. He is interested in investing in Davis, California, which houses one of the University of California campuses. He has access to monthly Rents (in $) for 27 houses, along with three characteristics of the home: number of bedrooms (Beds). number of bathrooms (Baths), and square footage (Sqft). A portion of the data is shown in the accompanying table. Rent Beds Baths Sqft 2, 950 4 4 1 , 453 2 , 400 4 2 1 , 476 744 2 1 930 Click here for the Excel Data File a-1. Estimate the linear model that uses Rent as the response variable. (Negative values should be indicated by a minus sign. Round your answers to 2 decimal places.) Rent = 65.83 9 + 237.35 0 Beds + 33937 0 Baths + 0.13 0 Sam a-2. Estimate the exponential model that uses log of Rent as the response variable. {Negative values should be indicated by a minus sign. Round your answers to 2 decimal places.) ln(Rant) = 6.29 9 + 0.13 9 Beds + 0.22 9 Baths + 0.00 0 Sqft b. Compute the predicted rent for a 1,500-squarefoot house with three bedrooms and two bathrooms for the linear and the exponential models (ignore the signicance tests). [Do not round intermediate calculations and round your final answers to 2 decimal places.) Llnoar Exponential Rent 1,332.73 9 1,720.93 9 c-1. Find the the proportion of the sample variation in rent that is explained by the estimated regression. Note that for the exponential model, you have to calculate 1:2 for rent and not log of rent. (Do not round intermediate calculations. Round your answers to 4 decimal places.' Linear Exponentlal R2 0.7935 9 0.7407 9 Rent Beds Baths Sqft 2950 4 4 1453 2400 4 2 1476 2375 3 3 1132 2375 3 1132 2350 4 2.5 1589 2000 3 2.5 1459 1950 3 2 1200 1900 3 2 1248 1810 2 2 898 1735 3 2.5 1060 1695 3 2 1100 1405 3 1 1030 1375 2 1 924 1365 2 1 974 1325 2 2 988 1275 2 2 880 1200 1 712 1180 2 1.5 890 1180 2 2 960 1115 2 1 1020 1100 2 1 903 1060 1 1 724 1007 3 2 1260 850 2 1.5 890 810 1 1 570 785 1 1 475 744 2 930