One of the biggest factors in determining the value of a home is the square footage. The accompanying data represent the square footage and selling price (in thousands of dollars) for a random sample of homes for sale in a certain region. Complete parts (a) through (h) below.

Square Footage, x

Selling Price ($000s), y

2133

369

3052

355.1

1074

181.5

1968

336.9

3062

614.2

2852

382.3

4292

655.7

2186

373.2

2496

407.4

1718

299.8

1718

260.1

3739

679

a) Which variable is the explanatory variable?

___Square Footage

___Selling Price

(b) Draw a scatter diagram of the data. Choose the correct scatter diagram below.

(c) Determine the linear correlation coefficient between square footage and asking price.

r = ___ (Round to three decimal places as needed.)

(d) Is there a linear relation between square footage and asking price?

___Yes

___No

(e) Find the least-squares regression line treating square footage as the explanatory variable.

ModifyingAbove y with caretyequals=nothingxplus+left parenthesis nothing right parenthesis

(Round to two decimal places as needed.)

(f) Interpret the slope. Select the correct choice below and, if necessary, fill in the answer box to complete your choice.

or a house that is 0 square feet, the predicted selling price is nothing thousand dollars.

(Round to two decimal places as needed.)

B. For every additional thousand dollars in selling price, the square footage increases by nothing square feet, on average.

(Round to two decimal places as needed.)

C. For every additional square foot, the selling price

increases by nothing thousand dollars, on average.

(Round to two decimal places as needed.)

D. For a house that is sold for $0, the predicted square footage is

nothing.

Step-by-step explanation:

Hello!

a)

You have two variables of interest:

Selling price of a house.

Square footage of a house.

If the Square footage determines (or you can say influences in the variation) the selling price of a house, then you can say that it is the explanatory variable, then X: Square footage of a house. adn Y: Selling price of a house. (thousandas of dollars)

b) See attachment.

c) To calculate the linear correlation coeficient you have to use the following formula:

n= 12

Summatories:

X₁: Square footage of a house.

∑X₁= 30290

∑X₁²= 85713686

X₂: Selling price of a house.

∑X₂= 4914.20

∑X₂²= 2285864.34

∑X₁X₂= 15663743

r= 0.907

d)

Looking at the correlation coefficient we can say that there is a strong relationship between the two variables and the scatterplot show that there is a linear relationship between the two variables, although there are probably other types of models that explain their relationship better.

e)

To find the regresion line between the square footage and the selling price you have to calculate the values of the sample intercepts "a" and the sample slope "b"

Using the summatories from item c. the estimated regresion line is:

^Y=  15.98 + 0.16X

f)

In general you can say that the slope of the regression shows the modification on the mean value of Y for every time X increases one unit.

In this example, the slope indicatest the increment of the average selling price of the houses for every aditional square foot.

Correct answer is C.

I hope you have a SUPER day!

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step-by-step explanation:

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geometry: triangles

solvers

lessons

answers archive

call the larger triangle abc

then the smaller triangle cde is similar to abc (all angles are equal, aaa).  

ab is the side that is 36, and we wish to find de, the side corresponding to ab

area_cde = area_abc - area_cde --> area_abc / area_cde = 2

let x=length of de

ratio of areas = (ratio of sides)^2

+2+=+%2836%2fx%29%5e2+

+2+=+1296%2fx%5e2+

++x%5e2+=+1296%2f2+=+648+

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