If two variables, x and y, have a very strong linear relationship, then:. a. there is evidence that x causes a change in y. b. there is evidence that y causes a change in x. c. there might not be any causal relationship between x and y. d. none of these alternatives is correct.

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.

the answer to this is a. a^2 + b^2 = c^2