Answer:
Step-by-step explanation:
The relationship between two variables x and y when linear can be measured by either correlation coefficient r, or the slope b1 of the regression line.
While correlation gives the association whether positive or negative whether weak or strong between two variables, b1 represents the rate of change of y with respect to x
r = cov (x,y)/std dev x (std dev y)
But slope is calculated as = cov (x,y)/var (x)
Thus we can say that whenever correlation is positive, slope is positive and vice versa
From correlation slope can be determine if std deviation of X and Y are known
b1 = slope of regression line = [tex]r(\frac{s_y}{s_x} )[/tex]
The relationship between the linear correlation coefficient r and the slope b 1b1 of a regression line is:
Correlation deals with the two sides of an association either weak or strong, +ve or - ve
With this in mind, we can see that the slope b1 has to do with the rate of change of y wrt x
As a result of this, if the correlation is +ve, then we can expect the slop to be +ve and vice versa.
Read more about linear correlation coefficient here:
https://brainly.com/question/17031457
