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# parts a thru e Question Help- X9.2.17-T Find the equalion of the regression ine for the...   parts a thru e Question Help- X9.2.17-T Find the equalion of the regression ine for the given data. Then construct a scater plot of the data and draw the regression line. (The pair of variables have a signficant comelation) Then use the regression equation to predict the value of y fon each of the given xvalues, f meaninghul. The table below shows the heights (n feet) and the number of stories of six notable buildings in a city Height, Sorles, y (b)x-651 feet (d) x724 feet 520 706 404 38 484 36 (a)x501 feet ()x345 fee 508 620 46 45 5 41 Find the regression equation. (Round the slope to three decimal places as needed. Round the y-intercept to two decimal places as needed) Enter your anwer in the edt elds and then click Check Anw Check Anves Clear A perts remaing MacBook Pro T H D
egression line for the given data. Then construct a scatter plot of the data and draw the reg s, if meaningful. The table below shows the heights (in feet) and the number of stories of si 508 484 36 494 520 45 620 766 38 41 46 51 ation. to three decimal places as needed. Round the y-intercept to two decimal places as needed.) answer in the edit fields and then click Check Answer. Clear Al ing MacBook
omplete) on line. (The pair of variables have a significant correlation.) Then use the regressio table buildings in a city. (a) x 501 feet (c)x 345 feet (b) x - 651 feet (d) x - 724 feet Check Ar Clear All
Find the regression equation 9-K- (Round the slope to three decimal places as needed. Round the y-intercept to two de Enter your answer in the edit fields and then click Check Answer 5 arts remaining esc F1 F2 F3 F4 % ! # 1 2 3 N

#### Homework Answers

Answer #1
 X Y XY X^2 Y^2 766 51 39066 586756 2601 620 46 28520 384400 2116 520 45 23400 270400 2025 508 41 20828 258064 1681 494 38 18772 244036 1444 484 36 17424 234256 1296 From the above table and formula we get the value are as:

 n 6 sum(XY) 148010 sum(X) 3392 sum(Y) 257 sum(X^2) 1.97791e+06 sum(Y^2) 11163
 b 0.045 a 17.34

Regression equation = a + bx

ycap = 17.34 + 0.045x

ycap = 17.34 + 0.045x

a)

when x = 501

ycap = 17.34 + 0.045x

Predicted value = 17.34 + 0.05 * 501

= 42

b)

when x = 651

ycap = 17.34 + 0.045x

Predicted value = 17.34 + 0.05 * 651

= 50

c)

when x = 345

ycap = 17.34 + 0.045x

Predicted value = 17.34 + 0.05 * 345

= 35

d)

when x = 724

ycap = 17.34 + 0.045x

Predicted value = 17.34 + 0.05 * 724

= 54

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