The following table represents a sample of sale prices, in thousands of dollars, and number of
new homes available at that price in 2017
160/180 | 200 | 220 | 240 | 26 |
280
Sale Price, p
(in thousands of dollars)
Number of New Homes Available
fip)
126
103
82
751
40
20

Correct question

Sale Price :160 | 180 | 200 | 220 | 240 | 260 | 280

New home     : 126 | 103 | 82 | 75 | 82 | 40 | 20

A.) state the linear regression function that estimates the number of new homes available at a specific price.

B.) state the correlation Coefficient of the data, and explain what it means in the context of the problem

Y = -0.79X + 249.86

R = -0.9543

Step-by-step explanation:

Sale Price :160 | 180 | 200 | 220 | 240 | 260 | 280

New home     : 126 | 103 | 82 | 75 | 82 | 40 | 20

Calculate the Linear regression equation :

Using the linear regression calculator :

The linear regression equation is :

Y = -0.79X + 249.86

The correlation Coefficient 'R' measures the strength of statistical relationship between the relative movement of two variables. The The value of R is -0.9543 in the question above.

This is a strong negative correlation, which means that high sales price of homes scores correlates with low number of new homes  scores (and vice versa). Homes with high sales price have fewer number of new homes.

95.03 is the answer. hope this !

im pretty sure its

(8,15)

step-by-step explanation: