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 9 recently sold homes in your town. You will note the distance each hame 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 nate the product X-y of the distance from the water and selling price for each hame. (These products are written in the row labeied "Xy"). (a) Cick an "Take Sample" to sen the results for your random sample. Distance from the water, X 3.8 21 1.3 0.2 2.9 21 4.4 13 1.8 (In lam) Taku Samplo Selling price, y (In hundreds of thousands of dollars) 5.7 10.3 17.1 14.8 8.2 7.4 6.1 10.4 9.6 xy 21.66 21.63 22.23 2.96 23.78 15.54 26.84 13.52 17.28 Sand data to calculator Based on the data from your sample, anter the indicated values in the column an the left below. Round decimal values to three decimal places. When you are done, select "Compute". (In the table below, is the sample size and the symbol I xy means the sum of the values Xy.) n: 0 Sample correlation coefficient (): Slope (b): I xy: 0 y-intercept (): Compute (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-O Distance from the water (in kan) (c) Use your regression equation to predict the selling price of a home that is 2.7 km from the water. Round your answer to one decimal place. Predicted price: I hundred thousand dollars Seling price Cin hundreds of thousands of dollars)
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 9 recently sold homes in your town. You will note the distance each hame 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 nate the product X-y of the distance from the water and selling price for each hame. (These products are written in the row labeied "Xy"). (a) Cick an "Take Sample" to sen the results for your random sample. Distance from the water, X 3.8 21 1.3 0.2 2.9 21 4.4 13 1.8 (In lam) Taku Samplo Selling price, y (In hundreds of thousands of dollars) 5.7 10.3 17.1 14.8 8.2 7.4 6.1 10.4 9.6 xy 21.66 21.63 22.23 2.96 23.78 15.54 26.84 13.52 17.28 Sand data to calculator Based on the data from your sample, anter the indicated values in the column an the left below. Round decimal values to three decimal places. When you are done, select "Compute". (In the table below, is the sample size and the symbol I xy means the sum of the values Xy.) n: 0 Sample correlation coefficient (): Slope (b): I xy: 0 y-intercept (): Compute (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-O Distance from the water (in kan) (c) Use your regression equation to predict the selling price of a home that is 2.7 km from the water. Round your answer to one decimal place. Predicted price: I hundred thousand dollars Seling price Cin hundreds of thousands of dollars)
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter10: Statistics
Section10.6: Summarizing Categorical Data
Problem 23PPS
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Question
Transcribed Image Text: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 9 recently sold homes in your town. You will note the
distance each hame 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 oaf the distance from the water and selling price for each hame. (These products are written in the row labeled "Xy")-
(a) Click an "Take Sample" to see the results for your random sample.
Distance from the
water, X
3.8
21
1.3
0.2
29
21
4.4
1.3
1.8
(In lam)
Selling price, y
(In hundreds of thousands
of dollars)
Tako Sample
5.7
10.3
17.1
14.8
8.2
1.4
6.1
10.4
9.6
xy
21.66
21.63
22.23
2.96
23.78
15.54
26.84
13.52
17.28
Sand data to calculator v
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, 1 is the sample size and the symbol I xy means the sum of the valuesXy.)
n: 0
Sample correlation coefficient (7):
I: 0
Slope (b):
I xy: 0
J-intercept (0):
Compute
(b) Write the equation of the least-squares regression line for your data. Then on the scatter plat 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
Distance from the water
(in km)
(c) Use your regression equation to predict the selling price af a home that is 27 km from the water. Round your answer to one decimal place.
Predicted price:
hundred thousand dollars
Selling price
(SJejjop jo spuesnoup jo spaupury ui)
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