Answer :
Correlations should always be reported with scatter diagrams because the scatter diagram can be used to distinguish between association and causation. Option A
A scatter diagram helps distinguish between association and causation, illustrating how changes in one variable relate to changes in another. It also clearly shows whether the correlation is positive or negative by depicting the direction of the relationship.
Additionally, scatter diagrams allow for the identification of outliers or patterns that might affect the correlation coefficient , providing a fuller understanding of the data.
C - "The scatter diagram is needed to see if the correlation coefficient is being affected".
Correlations are statistical relationships between two variables and are often represented mathematically by a correlation coefficient. However, to fully understand and interpret these relationships, it's important to use scatter diagrams (or scatter plots).
Let's explore why reporting correlations with scatter diagrams is essential:
Visual Representation: A scatter diagram provides a visual representation of the data, allowing us to see the overall pattern of the relationship. This can include how closely the data points cluster together, which is a clear indicator of the strength of the correlation.
Direction of Relationship: The scatter diagram helps to visually determine the direction of the correlation. If the points trend upwards from left to right, it indicates a positive correlation. Conversely, if they trend downwards, it shows a negative correlation. If there is no clear trend, the correlation might be weak or zero.
Checking Outliers: Scatter diagrams can help identify outliers data points that deviate significantly from the other points. Outliers can heavily influence the correlation coefficient, so spotting them allows for a more nuanced analysis.
Detecting Non-linear Relationships: While correlation coefficients measure linear relationships, scatter diagrams can reveal non-linear relationships that the coefficient alone might not show. Seeing how the points are distributed can suggest whether a different kind of analysis might be needed.
Contextual Understanding: Visuals often provide a better context for data than numbers alone. Even if the correlation coefficient is calculated, understanding the context and shape of the data helps in making more informed interpretations and decisions.
