Data on number of days of work missed and annual salary increase for a company's employees show that in general exployees who missed more days of work during the year received smaller raises than those who missed fewer days. A detailed analysis showed that number of days missed explained 74% of the variation in salary increases. What is the correlation between the number of days missed and salary increase?
One other comment:
Explained variation in this case is the variation in Y values that is explained by X values; unexplained variation is variation in Y values that cannot be explained by X values. Total variation is explained values plus unexplained values.
I hope this will help.
I tried those answers and it didn't work.
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I messed around the numbers and I sqrt .74 and added a negative sign to it and got it right. How do you know if it's a negative correlation?
Here's one way that might help you determine positive and negative correlations. With positive correlations, when one thing goes up, the other thing goes up. With negative correlations, when one thing goes up, the other goes down. With no correlation, there is no relationship between the two things.
To determine the correlation between the number of days missed and salary increase, you can use the coefficient of determination (r-squared value) provided in the question. The coefficient of determination represents the proportion of the variation in the salary increases that can be explained by the number of days missed.
In this case, it is mentioned that the number of days missed explains 74% of the variation in salary increases. So, the correlation between the number of days missed and salary increase is the square root of the coefficient of determination.
To find the correlation, compute the square root of 0.74:
correlation = sqrt(0.74) = 0.86
Therefore, the correlation between the number of days missed and salary increase is 0.86.
I wonder what "explained" means.
If "explained" means the right thing, the correlation is .74