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 x·y 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. 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 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: ?x: ?x²: ?y: ?y²: ?xy: Sample correlation coefficient (r): Slope (b1): y-intercept (b0):
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\( n \) is the sample size, which is the number of homes we have data for. In this case, \( n = 7 \). \( \bar{x} \) is the mean distance from the water for the homes in our sample. We calculate this by adding up all the \( x \) values and dividing by \( n \). Show more…
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