Respuesta :
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
Answer:
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.
The correct answer to the given table based on the linear regression is:
- Y = -0.79X + 249.86
- R = -0.9543
What is Correlation Coefficient?
This refers to the number between -1 and +1 where the interdependence of two variables are shown.
Hence, based on the given table:
Sale Price :160 | 180 | 200 | 220 | 240 | 260 | 280
New home : 126 | 103 | 82 | 75 | 82 | 40 | 20
To find the Linear regression equation :
Y = -0.79X + 249.86
R= -0.9543
Hence, we can see that the correlation coefficient "R" is used to show the statistical relationship between the two variables.
As a result of the strong negative correlation, then there would be higher prices for homes and lower number of new homes.
Read more about correlation coefficient here:
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