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To help you with this question, let's go through each part step-by-step. However, note that we don't have the exact data from **Exercise 44**, so I'll describe the approach for each part. If you can share the data or the regression line from Exercise 44, I can provide precise answers. Here’s a breakdown of each part:

### Part (a)
**Write the equation of the regression line.**  
For this, we typically use the form:
\[
\text{Attendance} = a + b \times \text{Runs}
\]
where:
- \( a \) is the intercept (the predicted attendance when Runs is 0),
- \( b \) is the slope (how much the attendance changes for each additional run scored by a team).

You'll need the values of \( a \) and \( b \) from the regression analysis provided in Exercise 44.

### Part (b)
**Estimate the Home Average Attendance for a team with 750 Runs.**  
Once you have the regression equation from part (a), substitute \( \text{Runs} = 750 \) into the equation to estimate the attendance. 

### Part (c)
**Interpret the meaning of the slope of the regression line in this context.**  
The slope \( b \) represents the change in home average attendance for each additional run scored by the team. For example, if \( b = 100 \), it would mean that for each additional run, the average attendance is expected to increase by 100 spectators.

### Part (d)
**In general, what would a negative residual mean in this context?**  
A residual is the difference between the observed attendance and the predicted attendance from the regression line:
\[
\text{Residual} = \text{Observed Attendance} - \text{Predicted Attendance}
\]
A negative residual would mean that the actual attendance is lower than what the model predicted. This could suggest that the model overestimated the attendance for that particular team.

---

If you’d like more specific calculations or interpretations, please share the regression equation or relevant details from Exercise 44.

### Do you want further details or have any questions?

### Related Questions
1. What are some methods to improve the accuracy of a regression model?
2. How can outliers affect the slope and intercept of a regression line?
3. Why is it important to analyze residuals when interpreting a regression model?
4. How do we calculate the coefficient of determination \( R^2 \) in regression analysis?
5. What does a high positive residual indicate about the observed and predicted values?

### Tip:
In regression analysis, always analyze the residual plot to check for patterns. If residuals show a pattern, it may indicate that a non-linear model would fit the data better.

48. Attendance 2016, last inning Refer again to the regression analysis for home average attendance and games won by baseball teams, seen in Exercise 44.

(a) Write the equation of the regression line.
(b) Estimate the Home Average Attendance for a team with 750 Runs.
(c) Interpret the meaning of the slope of the regression line in this context.
(d) In general, what would a negative residual mean in this context?

Learn how to solve a regression analysis problem involving home average attendance and team runs for baseball teams. This includes writing the regression line equation, estimating attendance for a specific number of runs, interpreting the slope, and understanding residuals. Ideal for students in Grades 11-12.

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