Question
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What is the strength and direction of the correlation between \( x \) and \( y \) ? Small positive linear association Small negative linear association Small positive non-linear association No association, since when one variable changes, it does not influence the other

Ask by Hodges Robbins. in the United States
Feb 03,2025

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Answer

The correlation between \( x \) and \( y \) is a small positive linear association, meaning that as \( x \) increases, \( y \) tends to increase slightly.

Solution

To determine the **strength** and **direction** of the correlation between variables \( x \) and \( y \), we typically rely on visual representations like scatter plots or statistical measures such as the Pearson correlation coefficient. Here's a brief overview of the options you've provided: 1. **Small Positive Linear Association** - **Strength:** Weak - **Direction:** Positive (as \( x \) increases, \( y \) tends to increase slightly) 2. **Small Negative Linear Association** - **Strength:** Weak - **Direction:** Negative (as \( x \) increases, \( y \) tends to decrease slightly) 3. **Small Positive Non-linear Association** - **Strength:** Weak - **Direction:** Positive, but not following a straight line (e.g., a slight curve) 4. **No Association** - **Strength:** None - **Direction:** No consistent trend; changes in one variable do not predict changes in the other **Without specific data or a visual representation**, it's challenging to accurately identify which of these descriptions best fits the correlation between \( x \) and \( y \). If you have a scatter plot or a correlation coefficient value, feel free to share it, and I can help interpret the correlation for you!

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Extra Insights

Both \( x \) and \( y \) can exhibit a small positive linear association if, generally, as \( x \) increases, \( y \) tends to increase too, albeit weakly. This suggests a relationship exists, though it's not particularly strong. Visualization through a scatterplot might reveal this pattern, where the points appear somewhat clustered along an upward-sloping line. On the flip side, for no association, the data points would be scattered randomly without any discernible pattern or trend between \( x \) and \( y \). In a scenario like this, changes in \( x \) wouldn't reliably indicate changes in \( y\), illustrating independence between the two variables.

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