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Use the table to answer the question.
This table shows the data needed to create a scatter plot
Data 1 Data 2
0 3
2 6
4 9
6 12
8 15
10 18
Use the data table to create a scatter plot. Estimate the correlation coefficient based on the data.
(1 point)
Responses
1
1
−1
negative 1
0.9
0.9
−0.9
negative 0.9
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This table shows the data needed to create a scatter plot
Data 1 Data 2
0 3
2 6
4 9
6 12
8 15
10 18
Use the data table to create a scatter plot. Estimate the correlation coefficient based on the data.
(1 point)
Responses
1
1
−1
negative 1
0.9
0.9
−0.9
negative 0.9
Skip to navigation
Answers
There are no human answers yet.
Answered by
GPT-4o mini
To estimate the correlation coefficient based on the provided data, we can first analyze the relationship between Data 1 and Data 2.
The provided data points are:
- (0, 3)
- (2, 6)
- (4, 9)
- (6, 12)
- (8, 15)
- (10, 18)
If we observe the points, we can see that as Data 1 increases (0, 2, 4, 6, 8, 10), Data 2 also increases (3, 6, 9, 12, 15, 18). This indicates a positive linear relationship.
To determine the strength of the correlation, we can look at the numerical change:
- For each increase of 2 in Data 1, Data 2 increases by 3.
Given this linear relationship, the correlation coefficient (r) is close to 1.
Considering the options provided:
- **1** indicates a perfect positive correlation.
- **0.9** indicates a strong positive correlation.
- **−1** indicates a perfect negative correlation.
- **−0.9** indicates a strong negative correlation.
- **0** indicates no correlation.
Given the data, the correlation is strong and positive but not perfect. Therefore, the best estimate for the correlation coefficient based on this data would be **0.9**.
The provided data points are:
- (0, 3)
- (2, 6)
- (4, 9)
- (6, 12)
- (8, 15)
- (10, 18)
If we observe the points, we can see that as Data 1 increases (0, 2, 4, 6, 8, 10), Data 2 also increases (3, 6, 9, 12, 15, 18). This indicates a positive linear relationship.
To determine the strength of the correlation, we can look at the numerical change:
- For each increase of 2 in Data 1, Data 2 increases by 3.
Given this linear relationship, the correlation coefficient (r) is close to 1.
Considering the options provided:
- **1** indicates a perfect positive correlation.
- **0.9** indicates a strong positive correlation.
- **−1** indicates a perfect negative correlation.
- **−0.9** indicates a strong negative correlation.
- **0** indicates no correlation.
Given the data, the correlation is strong and positive but not perfect. Therefore, the best estimate for the correlation coefficient based on this data would be **0.9**.
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