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Match each description to the correct relationship between the variables:

An increase in the number of movies produced in a year and growth of the pharmaceutical industry
An increase in the number of defects on a bolt of fabric and a decrease in the number of quality checks
An increase in life expectancy and a decrease in fatal illnesses
a) Correlation - Causation - No relationship
b) Causation - No relationship - Correlation
c) No relationship - Correlation - Causation
d) Causation - Correlation - No relationship

User Blom
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1 Answer

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Final answer:

The correct match is c) No relationship - Correlation - Causation, as there is likely no connection between movies and the pharma industry, a probable correlation between fabric defects and quality checks, and a causal link between life expectancy and fatal illnesses.

Step-by-step explanation:

Match each description to the correct relationship between the variables:

  • An increase in the number of movies produced in a year and the growth of the pharmaceutical industry.
  • An increase in the number of defects on a bolt of fabric and a decrease in the number of quality checks.
  • An increase in life expectancy and a decrease in fatal illnesses.

The correct match for each description based on the relationship between the variables is:

  1. No relationship - There is likely no relationship between the number of movies produced and the growth of the pharmaceutical industry.
  2. Correlation - There seems to be a correlation between the number of defects on fabric and the number of quality checks, as a decrease in checks might lead to an increase in defects.
  3. Causation - An increase in life expectancy can be causally related to a decrease in fatal illnesses.

Thus, the correct option is c) No relationship - Correlation - Causation.

Understanding the difference between correlation and causation is crucial in analyzing relationships between variables. For instance, a positive correlation is observed when both variables increase together, while a negative correlation is when one variable decreases as the other increases. But correlation does not imply causation. Without a controlled experiment, we cannot conclude that one variable causes the change in another.

User Kevin Monk
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