# Find the equation of the regre

Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line.​ (The pair of variables have a significant​ correlation.) Then use the regression equation to predict the value of y for each of the given​ x-values, if meaningful. The number of hours 6 students spent for a test and their scores on that test are shown below.

 Hours spent studying, x Test​ score, y 1 1 3 4 4 6 ​(a) x=2 hours ​(b) x=3.5 hours 40 45 50 47 61 69 ​(c) x=13 hours ​(d) x=4.5 hours

# Find the equation of the regre

Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line.​ (The pair of variables have a significant​ correlation.) Then use the regression equation to predict the value of y for each of the given​ x-values, if meaningful. The table below shows the heights​ (in feet) and the number of stories of six notable buildings in a city.

 Height, x ​Stories, y 766 620 520 508 494 484 ​(a) x=501 feet ​(b) x=648 feet 51 46 45 43 37 35 ​(c) x=310 feet ​(d) x=736 feet

(1)Find the regression equation.

y(^)=____ x+ (___)

(2) Predict the value of y for x=501
(3) Predict the value of y for x=648
(4) Predict the value of y for x=310
(5) Predcit the value of y for x=736

# Find the equation of the regre

Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line.​ (Each pair of variables has a significant​ correlation.) Then use the regression equation to predict the value of y for each of the given​ x-values, if meaningful. The caloric content and the sodium content​ (in milligrams) for 6 beef hot dogs are shown in the table below.

 Calories, x ​Sodium, y 160 170 130 120 70 180 ​(a) x=180 calories ​(b) x=90 calories 430 480 320 380 280 510 ​(c) x=150 calories ​(d) x=200 calories
Find the regression equation.

y=nothingx+​(nothing​)
​(Round to three decimal places as​ needed.)
Choose the correct graph below.

A.

02000560CaloriesSodium (mg)

•
•
•

A scatterplot has a horizontal axis labeled “Calories” from 0 to 200 in increments of 20 and a vertical axis labeled “Sodium (in milligrams)” from 0 to 560 in increments of 40. The following points are plotted: 10 units to the right of and 220 units above the origin; 20 units to the right of and 120 units above the origin; 30 units to the right of and 280 units above the origin; 40 units to the right of and 330 units above the origin; 50 units to the right of and 180 units above the origin; 60 units to the right of and 210 units above the origin. A line falls from left to right and passes through the points (0, 266) and (130, 0). All coordinates are approximate.

B.

02000560CaloriesSodium (mg)

•
•
•

A scatterplot has a horizontal axis labeled “Calories” from 0 to 200 in increments of 20 and a vertical axis labeled “Sodium (in milligrams)” from 0 to 560 in increments of 40. The following points are plotted: 50 units to the right of and 180 units above the origin; 100 units to the right of and 280 units above the origin; 110 units to the right of and 80 units above the origin; 130 units to the right of and 330 units above the origin; 130 units to the right of and 380 units above the origin; 140 units to the right of and 410 units above the origin. A line falls from left to right and passes through the points (0, 466) and (227, 0). All coordinates are approximate.

C.

02000560CaloriesSodium (mg)

•
•
•

A scatterplot has a horizontal axis labeled “Calories” from 0 to 200 in increments of 20 and a vertical axis labeled “Sodium (in milligrams)” from 0 to 560 in increments of 40. The following points are plotted: 70 units to the right of and 280 units above the origin; 120 units to the right of and 380 units above the origin; 130 units to the right of and 320 units above the origin; 160 units to the right of and 430 units above the origin; 170 units to the right of and 480 units above the origin; 180 units to the right of and 510 units above the origin. A line rises from left to right and passes through the points (50, 219) and (100, 321). All coordinates are approximate.

D.

02000560CaloriesSodium (mg)

•
•
•

A scatterplot has a horizontal axis labeled “Calories” from 0 to 200 in increments of 20 and a vertical axis labeled “Sodium (in milligrams)” from 0 to 560 in increments of 40. The following points are plotted: 80 units to the right of and 330 units above the origin; 100 units to the right of and 300 units above the origin; 130 units to the right of and 430 units above the origin; 140 units to the right of and 370 units above the origin; 170 units to the right of and 460 units above the origin; 80 units to the right of and 330 units above the origin. A line rises from left to right and passes through the points (50, 225) and (100, 327). All coordinates are approximate.

​(a) Predict the value of y for

x=180.

A.

423.897

B.

485.457

C.

526.497

D.

not meaningful
​(b) Predict the value of y for

x=90.

A.

300.777

B.

423.897

C.

526.497

D.

not meaningful
​(c) Predict the value of y for

x=150.

A.

300.777

B.

485.457

C.

423.897

D.

not meaningful
​(d) Predict the value of y for

x=200.

A.

526.497

B.

300.777

C.

485.457

D.

not meaningful

# Find the equation of the regre

9.2.2Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line.​ (The pair of variables have a significant​ correlation.) Then use the regression equation to predict the value of y for each of the given​ x-values, if meaningful. The number of hours 6 students spent for a test and their scores on that test are shown below.

 Hours spent studying, x Test​ score, y 0 2 2 3 5 5 ​(a) x=4 hours ​(b) x=2.5 hours 38 45 52 49 64 73 ​(c) x=15 hours ​(d) x=3.5 hours
Find the regression equation.

y=x+

# Find the equation of the regre

Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (The pair of variables have a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The table below shows the heights (in feet) and the number of stories of six notable buildings in a city.

(a) Find the regression equation.

(b) Predict the value of y for x = 639.

(c) Predict the value of y for x = 345.

(d) Predict the value of y for x = 727.

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