Multiple Choice
A data set is found to have a linear correlation coefficient of . Which of the following graphs most likely represents the relationship between these variables?
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11. Correlation
Correlation Coefficient
Multiple Choice
A marketing researcher analyzed advertising budget vs. monthly sales revenue for small retail stores and found that typically the stores that spent more on advertising saw higher sales revenues. However, the relationship wasn't perfect - some stores advertised more but saw fewer sales due to poor location, customer preferences, or bad timing. Which of the following is the most likely value for the correlation coefficient between advertising budget and sales revenue?
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Verified step by step guidance1
Understand the concept of the correlation coefficient (r): The correlation coefficient measures the strength and direction of the linear relationship between two variables. It ranges from -1 to 1, where values close to 1 indicate a strong positive relationship, values close to -1 indicate a strong negative relationship, and values near 0 indicate little to no linear relationship.
Analyze the context of the problem: The problem states that stores that spent more on advertising generally saw higher sales revenues, but the relationship wasn't perfect due to factors like location, customer preferences, or timing. This suggests a positive but not perfect correlation.
Eliminate unlikely options: A correlation of r = 0.96 would indicate a very strong positive relationship, which is unlikely given the imperfections mentioned. Similarly, r = -0.12 and r = -0.86 indicate negative relationships, which contradict the observation that higher advertising budgets generally lead to higher sales.
Identify the most likely value: The value r = 0.59 represents a moderate positive correlation, which aligns with the description of the relationship being generally positive but not perfect.
Conclude: Based on the analysis, the most likely value for the correlation coefficient is r = 0.59, as it best reflects the described relationship between advertising budget and sales revenue.
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