a-researcher-is-studying-the-relationship-between-the-number-of-hours-employees-

Question

A researcher is studying the relationship between the number of hours employees spend in professional training sessions per month ( $x$ ) and their job performance ratings ( $y$ ), scored out of $100$ . A linear regression model is created to predict job performance based on training hours, and the resulting regression equation is $\hat{y} = 2.8 x + 65$ . The researcher collects data from a sample of $10$ employees. Based on this data:
Residual sum of squares (SSR): $\sum (y - \hat{y})^{2} = 82.4$
Total sum of squares (SST): $\sum (y - \overset{ˉ}{y})^{2} = 240.0$
What is the coefficient of determination $r^{2}$ , and what does it tell us about the model?

Residual sum of squares (SSR): $\sum (y - \hat{y})^{2} = 82.4$

Total sum of squares (SST): $\sum (y - \overset{ˉ}{y})^{2} = 240.0$

What is the coefficient of determination $r^{2}$ , and what does it tell us about the model?

Accepted Answer

$r squared equals 0.657$ ; $65.7 percent sign$ of the variation in job performance is explained by training hours.

Answer

$r squared equals 0.657$ ; $65.7 percent sign$ of the variation in job performance is explained by training hours.

Answer

$r squared equals 0.657$ ; $65.7 percent sign$ of the variation in job performance is explained by training hours.

Answer

$r squared equals 0.657$ ; $65.7 percent sign$ of the variation in training hours is explained by job performance.