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The regression equation that predicts job performance ratings based on the number of training hours is:

\[
\text{Job Performance Rating} = 1.18 \times \text{Training Hours} + 39.95
\]

The Pearson correlation coefficient (r) is approximately 0.982, indicating a strong positive linear relationship between training hours and job performance ratings.

Would you like more details or have any questions? 

Here are 5 related questions to consider:
1. How do we interpret the slope in this regression equation?
2. What does the Pearson correlation coefficient tell us about the data?
3. How can we calculate the predicted job performance rating for 40 hours of training?
4. What is the significance of the intercept in the regression equation?
5. How would we test if the relationship is statistically significant?

**Tip:** The strength of the correlation (r) can be understood by squaring it to obtain the coefficient of determination (R²), which explains the variance in the dependent variable due to the independent variable.

Find the regression equation based on the number of training hours and job performance ratings.

Calculate the regression equation that predicts job performance ratings based on training hours. Understand the strong positive linear relationship using the Pearson correlation coefficient, with an example predicting ratings for 10 employees.

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