Apartment occupancy rates in a coastal town are thought to be influenced by the number of...

 

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Apartment occupancy rates in a coastal town are thought to be influenced by the number of tourists staying in the area. Over the past 10 years, the following data has been collected: Year Number of Tourists (1,000s) Occupancy Rate (%) 12 15 3 4 5 6 7 8 9 10 11 18 12 20 15 16 11 19 13 80 77 82 71 90 78 82 72 88 75 a. Calculate the linear correlation coefficient and interpret its value in the context of this problem. b. Calculate the equation of the regression line for predicting the apartment occupancy rate based on the number of tourists. c. If the town expects 22,000 tourists this year, use the regression equation to predict the apartment occupancy rate. d. If there are no tourists at all, explain the predicted ridership. 6. [20 points] The following data give the selling price, square footage, number of bedrooms, and age of houses that have sold in a neighborhood in the past 6 months. Develop a multiple- regression model to predict selling price based on the square footage, number of bedrooms, and age. Use this to predict the selling price of a 10-year-old, 2,000-square-foot house with three bedrooms. SELLING PRICE SQUARE AGE (100s)($) FOOTAGE BEDROOMS (YEARS) 220 1800 3 10 320 2500 4 5 180 1600 2 15 280 2100 3 8 260 1900 3 12 350 2800 4 4 250 1800 3 15 320 2200 4 8 180 1500 2 20 290 2100 3 12 200 1600 2 18 350, 2400 4 6 275 2000 3 12 320 2200 4 6 180 1600 2 18 300 2300 3 10 200 1700 2 20 a. Import the data into a spreadsheet or statistical software and perform a multiple regression analysis to determine the coefficients of the regression model. Show work. b. Use the regression model to predict the selling price of a 10-year-old, 2,000-square-foot house with three bedrooms. Apartment occupancy rates in a coastal town are thought to be influenced by the number of tourists staying in the area. Over the past 10 years, the following data has been collected: Year Number of Tourists (1,000s) Occupancy Rate (%) 12 15 3 4 5 6 7 8 9 10 11 18 12 20 15 16 11 19 13 80 77 82 71 90 78 82 72 88 75 a. Calculate the linear correlation coefficient and interpret its value in the context of this problem. b. Calculate the equation of the regression line for predicting the apartment occupancy rate based on the number of tourists. c. If the town expects 22,000 tourists this year, use the regression equation to predict the apartment occupancy rate. d. If there are no tourists at all, explain the predicted ridership. 6. [20 points] The following data give the selling price, square footage, number of bedrooms, and age of houses that have sold in a neighborhood in the past 6 months. Develop a multiple- regression model to predict selling price based on the square footage, number of bedrooms, and age. Use this to predict the selling price of a 10-year-old, 2,000-square-foot house with three bedrooms. SELLING PRICE SQUARE AGE (100s)($) FOOTAGE BEDROOMS (YEARS) 220 1800 3 10 320 2500 4 5 180 1600 2 15 280 2100 3 8 260 1900 3 12 350 2800 4 4 250 1800 3 15 320 2200 4 8 180 1500 2 20 290 2100 3 12 200 1600 2 18 350, 2400 4 6 275 2000 3 12 320 2200 4 6 180 1600 2 18 300 2300 3 10 200 1700 2 20 a. Import the data into a spreadsheet or statistical software and perform a multiple regression analysis to determine the coefficients of the regression model. Show work. b. Use the regression model to predict the selling price of a 10-year-old, 2,000-square-foot house with three bedrooms.