Full Answer Section

1. Weak Correlation: Even within the original data range, the correlation is weak. This suggests that salary is not a strong predictor of sick leave hours. Trying to predict anything about the new employee’s salary based on their high sick leave hours and this weak correlation would be unreliable. Many other factors (e.g., illness, family responsibilities, workplace culture) likely play a much larger role.

2. Ecological Fallacy: A correlation calculated on a group (the 200 employees) cannot necessarily be applied to an individual (the new employee). Group trends do not always reflect individual behavior. Even if there were a strong negative correlation, it wouldn’t guarantee that a specific individual with high sick leave would have a low salary.

3. Outlier Potential: 71 hours of sick leave is a very high value. This new employee is likely an outlier. Outliers can heavily influence correlations, and it’s possible that this employee’s situation is unique and not representative of the broader employee population. Including this outlier in the original correlation calculation would likely change the correlation coefficient, and it may not be appropriate to group them with the original data set.

In summary, the correlation of -0.19 is not applicable to the new employee with 71 hours of sick leave. Extrapolating beyond the data range, the weakness of the correlation, the potential for ecological fallacy, and the possibility of the employee being an outlier all make it inappropriate to use this correlation to make any inferences about the new employee’s salary. The manager should consider other factors and not rely on this correlation when analyzing this particular employee’s situation.