A multiple linear regression analysis was carried out using the following data for 60 residential housing sales in a given area in the east coast of the USA in 2019: Y = Sales Price (in thousands of dollars). X1 = Living Area (in square feet) X2 = 1 if the house is on the waterfront = 0 if the house is not on the waterfront X3 = 1 if the house has a garage = 0 if the house does not have a garage The results of a regression analysis used to predict housing sales are as follows: .. U 1. Prediction equation: Y = -28 + 0.088X1 + 280X2 + 35x3 2. The F test is statistically signicant. 3. The three T tests are statistically signicant. 4. The value of SSE = 35,000. 5. The value of SSR = 315,000. 6. The residual plots exhibited a random pattern with no outliers present. A waterfront house without a garage has 3,500 square feet of living area. Obtain a 95% prediction interval fer the sales price of this house. 0 ($546000, $644000) 0 ($511000, $609000) O ($600000, $700000) 0 ($446000, $744000) 0 ($411000, $709000)