To determine the strength of the relationship between the mean temperature and the electricity bill, we would ideally conduct a correlation analysis using methods like calculating the Pearson correlation coefficient. Since I can't perform real-time calculations, I'll help you think through the data based on what you’ve provided.
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Visualization: One way to gauge the relationship is to create a scatter plot. You would plot the mean temperature on the x-axis and the electricity bill on the y-axis. Look for patterns—if the points show a clear trend (increasing or decreasing), that suggests a stronger relationship.
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Correlation: Typically, a correlation coefficient (r) value can range from -1 to 1. Values close to -1 or 1 indicate a strong relationship (negative or positive), while values closer to 0 suggest a weak relationship. A perfect correlation would be 1 or -1.
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Trend Analysis:
- From the data, there is a mix of low and high bills at various temperatures, which doesn't immediately suggest a clear trend.
- It seems there are high electricity bills at colder temperatures and lower bills at temperatures around 47°F.
Given this information, if you were to assess the strength intuitively based on the apparent variability in the electricity bills at different temperatures, it seems there isn't a systematic increase or decrease overall.
With these points considered, it is reasonable to conclude that the relationship between mean temperature and electricity bill appears to be weak. However, for a definitive answer, performing the actual correlation calculation would be necessary.