To determine which of the choices represents the best estimated correlation coefficient, we need to consider what correlation coefficients indicate:
- 1 indicates a perfect positive correlation, meaning as one variable increases, the other variable also increases in a perfectly linear relationship.
- -1 indicates a perfect negative correlation, meaning as one variable increases, the other variable decreases in a perfectly linear relationship.
- 0.9 indicates a strong positive correlation, meaning there is a high likelihood that as one variable increases, the other variable also increases.
- -0.9 indicates a strong negative correlation, meaning there is a high likelihood that as one variable increases, the other variable decreases.
Without specific context or data to analyze, it is not possible to definitively choose the best estimated correlation coefficient. However, if we were to make a general estimate without any additional information, a value of 0.9 typically represents a strong positive correlation that is very common in many datasets.
If you have specific data or context in mind that would suggest a positive or negative relationship, that would influence your answer. But based on the standard interpretations of correlation coefficients, the estimate of 0.9 is usually a reasonable choice if there seems to be a positive relationship.
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