Let's analyze each statement:

1. The line of best fit provides a good estimate of a student's test scores based on the attendance of all students in Mr. Taylor's math classes.

   • The line of best fit is based on the data from the second period class only. While it provides an estimate for test scores based on days absent for this specific group, we cannot assume that it applies to all students in Mr. Taylor's math classes without knowing if other classes follow the same trend.
   • Answer: False

2. The y-intercept is at approximately (0, 91).

   • According to the line of best fit described, it passes through (0, 60), so the y-intercept is clearly 60, not 91.
   • Answer: False

3. Point C is an outlier because it is far away from the other data points.

   • If Point C is significantly distant from the general trend or clustering of the other points, then it qualifies as an outlier. However, this depends on the visual context of the graph. Based on the general scenario, if the point is indeed far off, this would be true. If not, it's false. Confirm visually if Point C meets this criterion.
   • Answer: (True/False depending on graph)

---

Would you like to dive deeper into specific calculations or discuss the concepts further? Let me know!

Follow-up Questions:

1. How is the y-intercept determined from a line of best fit?
2. What makes a point qualify as an outlier in a scatter plot?
3. How could we validate whether the line of best fit works for other classes?
4. What is the formula for the line of best fit based on the provided points?
5. How do changes in attendance (x-axis) directly affect test scores (y-axis)?

Tip:

When determining outliers, compare the distance of the point from the line of best fit relative to the overall data spread.