The price for a newly listed condominium asked by real estate agents may depend on many factors: the living area, number of floors, number of bedrooms, number of bathrooms. It's assumed that the factors listed above are the main factors, and multiple regression model to be fitted is, where /vx = Po + Pix, + Byxz + ByX's + BAX4 * = living area in hundreds of square meters, *2 = number of floors, X3 = number of bedrooms, *4 = number of bathrooms To fit multiple regression model the data set of 15 randomly selected condominiums currently on the market is used. The resulting data set in presented in the table below. Observation | List price, y Living area, x, Floors, Bedrooms, |Bathrooms, (in $1000) (in hundreds X2 EX X4 of sq. ft) 1 169.0 6 1 2 1 2 218.5 10 1 2 2 3 216.5 10 1 3 2 4 225.0 11 1 3 2 5 229.9 13 1 3 1.7 6 235.0 13 2 3 2.5 7 239.9 13 1 3 2 8 247.9 17 2 3 2.5 9 260.0 19 2 3 2 10 269.9 18 1 3 2 11 234.9 13 1 4 2 12 255.0 18 1 4 2 13 269.9 17 2 4 3 14 294.5 20 2 4 3 15 309.9 21 4Use R output to answer the following questions a. Do these data provide sufficient evidence to conclude at the 5% significance level that the model is useful in predicting the list price? b. Do these data provide sufficient evidence to conclude at the 5% significance level that the list price and the living area are linearly related? c. Do these data provide sufficient evidence to conclude at the 5% significance level that the list price and the number of the bedrooms are linearly related? Show the code (in blue) and output (in black) for parts a, b, and c. Use p-values in output to answer the questions a, b, and c