Answer :
Answer:
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:
[tex]r= \frac{sumX_1X_2-(\frac{(sumX_1)(sumX_2)}{n} )}{\sqrt{[sumX_1^2-\frac{(sumX_1)^2}{n} ][sumX_2^2-\frac{(sumX_2)^2}{n} ]} }[/tex]
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
[tex]r= \frac{15663743-(\frac{(30290)(4914.20)}{12} )}{\sqrt{[85713686-\frac{(30290)^2}{12} ][2285864.34-\frac{(4914.20)^2}{12} ]} }[/tex]
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!
