A p-value is a statistical measure that helps to determine the significance of your results. In hypothesis testing, a p-value indicates the probability of observing the data, or something more extreme, if the null hypothesis is true. Typically, a p-value threshold (commonly set at 0.05) is used to decide whether to reject the null hypothesis.
In your situation, obtaining a p-value of 1.29 is unusual because p-values range from 0 to 1. A p-value greater than 1 indicates a miscalculation, as valid p-values cannot exceed 1.
Assuming you meant to convey that you obtained a very high p-value (greater than the typical cutoff, like 0.05), this suggests that there is not enough evidence to reject the null hypothesis of no correlation between car weight and miles per gallon (MPG).
Here are some potential reasons why you might find no significant correlation:
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Lack of Variation: If there is not much variation in your data for either car weight or miles per gallon, it may be difficult to detect a relationship.
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Sample Size: A small sample size can lead to less reliable estimates and impact the power of your analysis, making it harder to detect a true correlation when one exists.
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Outliers: Outliers can skew your results. If some cars are significantly heavier or get much different MPG than the others, this can affect your analysis.
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Non-linear Relationship: The relationship between weight and MPG might not be linear. If you are using a linear regression model but the underlying relationship is more complex, it might lead you to conclude there is no correlation when a non-linear model might capture the relationship better.
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Improper Model Specification: If the model used to analyze the relationship is not correctly specified, it may not accurately capture any potential correlation.
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Confounding Variables: Other factors might be influencing both car weight and MPG, obscuring the relationship you're trying to examine.
Make sure to double-check your calculations and ensure you're fitting the appropriate model to understand the relationship between the two variables accurately.