Question

Alice reads a scatterplot that shows data for nine schools. It relates the percentage of students receiving free lunches to the percentage of students wearing a bicycle helmet. The plot shows a strong negative correlation. Alice recalls that correlation does not imply causation. In this example, Alice sees that increasing the percentage of free lunches would not cause children to use their bicycle helmets less.

Identify the confounding variable that is causing Alice's observed association.

a. The number of bike helmets available
b. Parents' income
c. School funding
d. The number of free lunches available

Answer 1

parents income

Explanation:

Answer 2

Parents' income

Explanation:

The confounding variable also called the third or lurking variable is usually an unaccounted for variable or factor which may creep into our research, Hence, causing an association or relationship which does not actually exists between the two correlated variables. In the experiment abive, there is no really any actual relationship between increasing percentage of free lunch and less usage of bicycle helmets.

However, We may attribute the change in bicycle helmets used by the children to a swerve or change in the income of the parents of the students, being able to afford a bicycle helmet will depend in the income of the parent and this mightvhave caused the observed association between the variables.