Respuesta :
Answer:
[tex]r=\frac{2(23146)-(139)(333)}{\sqrt{[2(9661) -(139)^2][2(55457) -(333)^2]}}=1[/tex]
So then the we have perfect linear association. Because the heights and weights of the men are similar.
Step-by-step explanation:
Let X represent the Height and Y the weigth
We have the follwoing dataset:
X: 70, 69
Y: 169, 164
n=2
The correlation coefficient is a "statistical measure that calculates the strength of the relationship between the relative movements of two variables". It's denoted by r and its always between -1 and 1.
And in order to calculate the correlation coefficient we can use this formula:
[tex]r=\frac{n(\sum xy)-(\sum x)(\sum y)}{\sqrt{[n\sum x^2 -(\sum x)^2][n\sum y^2 -(\sum y)^2]}}[/tex]
For our case we have this:
n=2 [tex] \sum x = 139, \sum y = 333, \sum xy = 23146, \sum x^2 =9661, \sum y^2 =55457[/tex]
And if we replace in the formula we got:
[tex]r=\frac{2(23146)-(139)(333)}{\sqrt{[2(9661) -(139)^2][2(55457) -(333)^2]}}=1[/tex]
So then the we have perfect linear association. Because the heights and weights of the men are similar.
