You work as a data scientist for a real estate company in a seaside resort town. Your boss has asked you to discover if it's possible to predict how much a home's distance from the water affects its selling price. You are going to collect a random sample of 7 recently sold homes in your town. You will note the distance each home is from the water (denoted by x, in km) and each home's selling price (denoted by y, in hundreds of thousands of dollars). You will also note the product xy of the distance from the water and selling price for each home. (These products are written in the row labeled "xy"). (a)Click on "Take Sample" to see the results for your random sample. Take Sample Distance from the water, x (in km) 4.9 2.1 3.8 0.4 3.2 2.7 1.5 Selling price, y (in hundreds of thousands of dollars) 6.1 9.2 7.9 12.3 11.6 8.8 14.7 xy 29.89 19.32 30.02 4.92 37.12 23.76 22.05 Send data to calculator Based on the data from your sample, enter the indicated values in the column on the left below. Round decimal values to three decimal places. When you are done, select "Compute". (In the table below, n is the sample size and the symbol xy means the sum of the values xy.) n: x: y: sx: sy: xy: Compute Sample correlation coefficient (r): Slope (b1): y-intercept (b0): (b)Write the equation of the least-squares regression line for your data. Then on the scatter plot for your data, graph this regression equation by plotting two points and then drawing the line through them. Round each coordinate to three decimal places. Regression equation: y= Selling price (in hundreds of thousands of dollars) y24681012141618202224x0.511.522.533.544.555.560 Distance from the water (in km) (c)Use your regression equation to predict the selling price of a home that is

1.8

km

from the water. Round your answer to one decimal place.

Predicted price:

hundredthousanddollars