Question
                Use the table to answer the question.
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
0.9
0.9
−1
negative 1
−0.9
            
        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
0.9
0.9
−1
negative 1
−0.9
Answers
                                                    There are no human answers yet.
                                            
                
                    Answered by
            GPT-4o mini
            
    To estimate the correlation coefficient based on the data provided in the table, we first look at the relationship between the two sets of values:
- The x-values (first column) are: 0, 2, 4, 6, 8, 10
- The y-values (second column) are: 3, 6, 9, 12, 15, 18
From this data, we can see that as the x-values increase, the y-values also increase consistently.
If we plot these points on a scatter plot, we will see that they form a straight line, indicating a positive linear relationship.
To estimate the correlation coefficient (r), we note the following:
- A correlation coefficient of 1 implies a perfect positive correlation.
- A correlation coefficient close to 1 indicates a strong positive correlation.
Given the linearity observed in the data, we can estimate the correlation coefficient to be **1**.
Therefore, the correct response is:
**1**
    
- The x-values (first column) are: 0, 2, 4, 6, 8, 10
- The y-values (second column) are: 3, 6, 9, 12, 15, 18
From this data, we can see that as the x-values increase, the y-values also increase consistently.
If we plot these points on a scatter plot, we will see that they form a straight line, indicating a positive linear relationship.
To estimate the correlation coefficient (r), we note the following:
- A correlation coefficient of 1 implies a perfect positive correlation.
- A correlation coefficient close to 1 indicates a strong positive correlation.
Given the linearity observed in the data, we can estimate the correlation coefficient to be **1**.
Therefore, the correct response is:
**1**
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