1. The correlation coefficient between the number of shipments and delivery time is approximately 0.482. This indicates a moderate positive correlation.

2. The linear regression model is: $\text{Delivery Time} = 0.037 \times \text{Shipments} + 2.93$ Here, the slope is approximately 0.037 and the intercept is approximately 2.93.

3. If the number of shipments is 30, the predicted delivery time is approximately 4.04 days.

Would you like further details or explanations on any part of this analysis?

Here are five related questions for further exploration:

1. How does a correlation coefficient close to 1 or -1 affect predictions?
2. What are the assumptions behind using linear regression in this dataset?
3. How would an outlier impact both the correlation and regression model?
4. Can you explain the concept of residuals in a linear regression model?
5. How would you assess the accuracy of this regression model?

Tip: Always plot the regression line along with the data points to visually assess the model's fit.