Answer:
c. there might not be any causal relationship between x and y.
Step-by-step explanation:
A correlation can be defined as a numerical measure of the relationship between existing between two variables (x and y).
In Mathematics and Statistics, a group of data can either be negatively correlated, positively correlated or not correlated at all.
1. For a negative correlation: a set of values in a data increases, when the other set begins to decrease. Here, the correlation coefficient is less than zero (0).
2. For a positive correlation: a set of values in a data increases, when the other set also increases. Here, the correlation coefficient is greater than zero (0).
3. For no or zero correlation: a set of values in a data has no effect on the other set. Here, the correlation coefficient is equal to zero (0).
If two variables, x and y, have a very strong linear relationship, then there might not be any causal relationship between x and y.
A causal relation exists between two variables (x and y), if the occurrence of the first causes the other; where, the first variable (x) is referred to as the cause while the second variable (y) is the effect.
A strong linear relationship exists between two variables (x and y), if they both increases or decreases at the same time. It usually has a correlation coefficient greater than zero or a slope of 1.
Hence, if two variables, x and y, have a very strong linear relationship, then there might not be any causal relationship between x and y.
