Predictions from the least-squares regression line

o REGRESSION AND CORRELATION Predictions from the least-squares regression line V With the aim of predicting the selling price of a house in Newburg Park, Florida, from the distance between the house and the beach, we might examine a regression equation relating the two variables. In the table below, the distance from the beach (x, in miles) and selling price (y, in thousands of dollars) for each of a sample of homes sold in Newburg Park in the A past year are given. The least-squares regression equation relating the two variables is y = 314.39 -7.05x. The line having this equation is plotted below. 33 Distance Selling price, from the l' E beach, 1' (in thousands (in miles) of dollars) 9.0 9.0 13.2 8.7 11.2 15.9 12.6 10.5 2.6 14.7 5.5 5.5 8.4 6.7 7.2 (Send data to calculator V) (Send data to Excel) HE 350 I 300 250 Selling price, y (in thousands of dollars) ISO 0 5 In is 20 Distance from the beach, x (in miles) Based on the sample data and the regression line, complete the following. (a) For these data, house prices that are greater than the mean of the house prices tend to be paired with distances from the beach that are (Choose one) v the mean of the distances from the beach. (b) According to the regression equation, for an increase of one mile in distance from the beach, there is a corresponding (Choose one) V of 7.05 thousand dollars in house price. (c) What was the observed house price (in thousands of dollars) when the distance (in miles) from the beach was 8.4 miles? I] (d) From the regression equation, what is the predicted house price (in thousands of dollars) when the distance (in miles) from the beach is 8.4 miles? (Round your answer to at least one decimal place.) Explanation Check Skip to Main Canton - 2021 McGiaerili Education. All Rights Re