? The following table shows data on the secondary housing market in a district of a city:

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The following table shows data on the secondary housing market in a district of a city: the cost of the apartment (v, in thousand USD), the size of the living space (x1, in m2), and the size of the kitchen (x2, in m?). No y X2 13.0 37.0 6.2 16.4 60.9 10.0 17.0 60.0 8.5 4 15.2 52.1 7.4 5 14.2 40.1 7.0 6 10.5 30.4 6.2 7 20.0 43.0 7.5 8 12.0 32.1 6.4 9 15.6 35.1 7.0 10 12.5 32.0 6.2 11 13.2 33.0 6.0 12 14.6 32.5 5.8 a) Fit a multiple linear regression model to these data. Round your answers to three decimal places (e.g. 98.765). + i x1 + i x2 i Calculate R? for this model. Round your answer to one decimal place (e.g. 98.7). i % b) Construct a normal probability plot of the residuals. Does the assumption of normality appear adequate? 123 The following table shows data on the secondary housing market in a district of a city: the cost of the apartment (v, in thousand USD), the size of the living space (x1, in m2), and the size of the kitchen (x2, in m?). No y X2 13.0 37.0 6.2 16.4 60.9 10.0 17.0 60.0 8.5 4 15.2 52.1 7.4 5 14.2 40.1 7.0 6 10.5 30.4 6.2 7 20.0 43.0 7.5 8 12.0 32.1 6.4 9 15.6 35.1 7.0 10 12.5 32.0 6.2 11 13.2 33.0 6.0 12 14.6 32.5 5.8 a) Fit a multiple linear regression model to these data. Round your answers to three decimal places (e.g. 98.765). + i x1 + i x2 i Calculate R? for this model. Round your answer to one decimal place (e.g. 98.7). i % b) Construct a normal probability plot of the residuals. Does the assumption of normality appear adequate? 123 The following table shows data on the secondary housing market in a district of a city: the cost of the apartment (v, in thousand USD), the size of the living space (x1, in m2), and the size of the kitchen (x2, in m?). No y X2 13.0 37.0 6.2 16.4 60.9 10.0 17.0 60.0 8.5 4 15.2 52.1 7.4 5 14.2 40.1 7.0 6 10.5 30.4 6.2 7 20.0 43.0 7.5 8 12.0 32.1 6.4 9 15.6 35.1 7.0 10 12.5 32.0 6.2 11 13.2 33.0 6.0 12 14.6 32.5 5.8 a) Fit a multiple linear regression model to these data. Round your answers to three decimal places (e.g. 98.765). + i x1 + i x2 i Calculate R? for this model. Round your answer to one decimal place (e.g. 98.7). i % b) Construct a normal probability plot of the residuals. Does the assumption of normality appear adequate? 123 The following table shows data on the secondary housing market in a district of a city: the cost of the apartment (v, in thousand USD), the size of the living space (x1, in m2), and the size of the kitchen (x2, in m?). No y X2 13.0 37.0 6.2 16.4 60.9 10.0 17.0 60.0 8.5 4 15.2 52.1 7.4 5 14.2 40.1 7.0 6 10.5 30.4 6.2 7 20.0 43.0 7.5 8 12.0 32.1 6.4 9 15.6 35.1 7.0 10 12.5 32.0 6.2 11 13.2 33.0 6.0 12 14.6 32.5 5.8 a) Fit a multiple linear regression model to these data. Round your answers to three decimal places (e.g. 98.765). + i x1 + i x2 i Calculate R? for this model. Round your answer to one decimal place (e.g. 98.7). i % b) Construct a normal probability plot of the residuals. Does the assumption of normality appear adequate? 123

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