Question

The article "Behavioural Effects of Mobile Telephone Use During Simulated Driving"† reported that for a sample of 20 experimental subjects, the sample correlation coefficient for x = age and y = time since the subject had acquired a driving license (yr) was 0.97. Why do you think the value of r is so close to 1? (The article's authors give an explanation.)

Answer 1

Answer:

Step-by-step explanation:

Sample size N is equal to 20 experimental subjects (small sample size). Sample correlation coefficient r = 0.97

The variables are:

X = Age of subject (in years)

Y = Time since subject acquired a driving license (in years)

The general reason why the value of r for 2 variables will be very close to 1 is that the magnitude of the effect of the phenomenon-of-interest (behavioural effects of mobile telephone use during simulated driving) is very large.

r = 0.97 is a positive figure also. This implies that the phenomenon-of-interest in variable X and in variable Y move in the same direction or increase simultaneously.

For the r of 0.97, the behavioural effect of mobile telephone use during simulated driving as related to the age of the experimental subjects, IS ALMOST THE SAME MAGNITUDE as the behavioural effect of mobile telephone use during simulated driving as related to the time (in years) since subjects acquired a driving license.

This is the reason why the correlation coefficient here is very close to 1.

Answer 2

Answer:

r is very close to 1 because there is a very concrete relationship between the variables x and y.

Step-by-step explanation:

Given that x = age,

y = time since the subject had acquired a driving licence.

We want to know why the correlation of these variables is so close to 1.

Correlation of these variables is the mutual relationship between the two variables.

Drivers licence is only obtainable when one comes of age.

For example: Many countries don't issue drivers license to an individual below the age of 18.

The correlation is very close to 1 because there is a concrete relationship between the variable.