Question: A multivariate linear regression model has been built to predict the heating load in a residential building based on a set of descriptive features describing

A multivariate linear regression model has been built to predict the heating load in a residential building based on a set of descriptive features describing the characteristics of the building. Heating load is the amount of heat energy required to keep a building at a specified temperature, usually 65 Fahrenheit, during the winter regardless of outside temperature. The descriptive features used are the overall surface area of the building, the height of the building, the area of the building's roof, and the percentage of wall area in the building that is glazed. This kind of model would be useful to architects or engineers when designing a new building.25 The trained model is HEATING LOAD = - 26.030 + 0.0497 x SURFACE AREA + 4.942 x HEIGHT - 0.090 x ROOF AREA + 20.523 x GLAZING AREA Use this model to make predictions for each of the query instances shown in the table below. ID SURFACE AREA HEIGHT ROOF AREA GLAZING AREA 1 784.0 3.5 220.5 0.25 2 710.5 3.0 210.5 0.10 3 563.5 7.0 122.5 0.40

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