Question
Number of patrons, days
1. 725, 10
2. 675, 20
3. 700, 30
4. 750, 40
5. 650, 50
6. 675, 60
7. 700, 70
8. 725, 80
9. 725, 90
10. 650, 100
A heading to graph the average attendance of patrons every 10 days over a 100 day period of time. Interpret the scatterplot to determine what type of association the scatterplot shows.
The graph shows a linear association.
The graph shows a positive association.
The graph shows a negative association.
The graph is non-linear.
1. 725, 10
2. 675, 20
3. 700, 30
4. 750, 40
5. 650, 50
6. 675, 60
7. 700, 70
8. 725, 80
9. 725, 90
10. 650, 100
A heading to graph the average attendance of patrons every 10 days over a 100 day period of time. Interpret the scatterplot to determine what type of association the scatterplot shows.
The graph shows a linear association.
The graph shows a positive association.
The graph shows a negative association.
The graph is non-linear.
Answers
GPT-4o mini
Based on the data provided, you can graph the number of patrons against the number of days, creating a scatterplot. The x-axis will represent the number of days, while the y-axis will represent the number of patrons.
### Suggested Heading for the Graph:
"Average Attendance of Patrons Over a 100-Day Period"
### Interpretation of the Scatterplot:
To interpret the scatterplot, you'd want to look at the trend of the data points:
1. **Examine the data points**:
- The number of patrons fluctuates over the 100 days, with some increases and some decreases.
- For example, the values are higher around 10, 30, 40, and 70 days, whereas they dip at 50 and 100 days.
2. **Type of Association**:
- **Linear Association**: This typically means the points suggest a straight line trend. However, if there's considerable fluctuation without a clear straight-line trend, it may not be linear.
- **Positive Association**: This means that as the number of days increases, the number of patrons would also increase. This does not appear to be true for your data, as there are fluctuations.
- **Negative Association**: This indicates that as one variable increases, the other decreases, which does not seem to apply to your data as well.
- **Non-Linear**: If the data points vary widely and do not fit a straight line, it indicates non-linearity.
### Conclusion:
Based on the description of the data, it looks like the associations are fluctuating, leading to **non-linear behavior** in the attendance patterns over time.
Ultimately, the most accurate conclusion from your scatterplot would likely be that **the graph is non-linear,** as the attendance does not consistently increase or decrease with the passage of time.
### Suggested Heading for the Graph:
"Average Attendance of Patrons Over a 100-Day Period"
### Interpretation of the Scatterplot:
To interpret the scatterplot, you'd want to look at the trend of the data points:
1. **Examine the data points**:
- The number of patrons fluctuates over the 100 days, with some increases and some decreases.
- For example, the values are higher around 10, 30, 40, and 70 days, whereas they dip at 50 and 100 days.
2. **Type of Association**:
- **Linear Association**: This typically means the points suggest a straight line trend. However, if there's considerable fluctuation without a clear straight-line trend, it may not be linear.
- **Positive Association**: This means that as the number of days increases, the number of patrons would also increase. This does not appear to be true for your data, as there are fluctuations.
- **Negative Association**: This indicates that as one variable increases, the other decreases, which does not seem to apply to your data as well.
- **Non-Linear**: If the data points vary widely and do not fit a straight line, it indicates non-linearity.
### Conclusion:
Based on the description of the data, it looks like the associations are fluctuating, leading to **non-linear behavior** in the attendance patterns over time.
Ultimately, the most accurate conclusion from your scatterplot would likely be that **the graph is non-linear,** as the attendance does not consistently increase or decrease with the passage of time.