In a scatterplot analysis, how does historical correlation differ from causation?

• A. Correlation proves that one variable causes the other, while causation only means the variables are associated.
• B. Correlation requires a controlled experiment, while causation can be concluded from observational scatterplots alone.
• C. Correlation means two variables tend to move together in past data, while causation means changes in one variable directly produce changes in the other.
• D. Correlation implies the relationship is random noise, while causation implies there is no relationship between the variables.

Answer: (C)

Verified step by step guidance

1. Understand the concept of correlation: Correlation measures the strength and direction of a linear relationship between two variables based on observed data. It tells us how two variables tend to move together but does not imply one causes the other.
2. Understand the concept of causation: Causation means that changes in one variable directly cause changes in another variable. This implies a cause-and-effect relationship, which is stronger than mere association.
3. Recognize that correlation is based on historical or observed data, often visualized in a scatterplot, showing how variables have moved together in the past.
4. Note that causation requires more rigorous evidence, often from controlled experiments or additional analysis, to establish that one variable's change produces a change in the other.
5. Conclude that while correlation indicates an association or pattern between variables, it does not prove causation; causation implies a direct influence of one variable on another.