Question
Use the data in the table to create a scatter plot, and then find the correlation coefficient.
x y
3 7
3 9
4
6
6 11
7 10
8 15
8 16
9 14
10 12
(1 point)
Responses
r=4.27
r is equal to 4 point 2 7
r=1.06
r is equal to 1 point 0 6
r=0.79
r is equal to 0 point 7 9
r=0.63
x y
3 7
3 9
4
6
6 11
7 10
8 15
8 16
9 14
10 12
(1 point)
Responses
r=4.27
r is equal to 4 point 2 7
r=1.06
r is equal to 1 point 0 6
r=0.79
r is equal to 0 point 7 9
r=0.63
Answers
There are no human answers yet.
Answered by
GPT-4o mini
To determine the correlation coefficient (r) from the given data, we first need to ensure the data is complete. Here's the data we have:
```
x: 3, 3, 4, 6, 7, 8, 8, 9, 10
y: 7, 9, 6, 11, 10, 15, 16, 14, 12
```
There is a missing value in the table for `y` when `x` is 4. However, we can still compute the correlation coefficient using the remaining data.
1. **Clean the Data:**
We'll omit the entry where `x=4` and `y` is missing. The cleaner dataset is:
```
x: 3, 3, 6, 7, 8, 8, 9, 10
y: 7, 9, 11, 10, 15, 16, 14, 12
```
2. **Calculate the Correlation Coefficient (r):**
The correlation coefficient can be calculated using the formula:
\[
r = \frac{n(\sum xy) - (\sum x)(\sum y)}{\sqrt{[n\sum x^2 - (\sum x)^2][n\sum y^2 - (\sum y)^2]}}
\]
Where:
- \(n\) is the number of pairs (in this case, 8).
- \(\sum xy\) is the sum of the product of each pair of x and y.
- \(\sum x\) and \(\sum y\) are the sums of x values and y values.
- \(\sum x^2\) and \(\sum y^2\) are the sums of squares of x values and y values.
3. **Calculation Steps:**
Let's compute these values:
- \(n = 8\) (number of pairs)
- \(\sum x = 3 + 3 + 6 + 7 + 8 + 8 + 9 + 10 = 54\)
- \(\sum y = 7 + 9 + 11 + 10 + 15 + 16 + 14 + 12 = 88\)
- \(\sum xy = 3*7 + 3*9 + 6*11 + 7*10 + 8*15 + 8*16 + 9*14 + 10*12 = 21 + 27 + 66 + 70 + 120 + 128 + 126 + 120 = 648\)
- \(\sum x^2 = 3^2 + 3^2 + 6^2 + 7^2 + 8^2 + 8^2 + 9^2 + 10^2 = 9 + 9 + 36 + 49 + 64 + 64 + 81 + 100 = 412\)
- \(\sum y^2 = 7^2 + 9^2 + 11^2 + 10^2 + 15^2 + 16^2 + 14^2 + 12^2 = 49 + 81 + 121 + 100 + 225 + 256 + 196 + 144 = 972\)
Now we can plug these values into the correlation formula:
\[
r = \frac{8(648) - (54)(88)}{\sqrt{[8(412) - (54)^2][8(972) - (88)^2]}}
\]
Calculating the components:
- \(8(648) = 5184\)
- \((54)(88) = 4752\)
- \(\sum xy = 5184 - 4752 = 432\)
Now for the denominator:
- \(8(412) = 3296\)
- \((54)^2 = 2916\)
- \(\sum x^2 = 3296 - 2916 = 380\)
For \(y\):
- \(8(972) = 7776\)
- \((88)^2 = 7744\)
- \(\sum y^2 = 7776 - 7744 = 32\)
So now we have:
\[
r = \frac{432}{\sqrt{380 \cdot 32}} = \frac{432}{\sqrt{12160}} \approx \frac{432}{110.24} \approx 0.392
\]
However, it seems that the provided answers don't include 0.39, which indicates that either additional data or an averaging method might need to be considered, or one of the provided values could stand correct.
Given the options you provided, the nearest value would be:
- r = 0.63
Let's choose that. Thus, the answer is **r = 0.63**.
```
x: 3, 3, 4, 6, 7, 8, 8, 9, 10
y: 7, 9, 6, 11, 10, 15, 16, 14, 12
```
There is a missing value in the table for `y` when `x` is 4. However, we can still compute the correlation coefficient using the remaining data.
1. **Clean the Data:**
We'll omit the entry where `x=4` and `y` is missing. The cleaner dataset is:
```
x: 3, 3, 6, 7, 8, 8, 9, 10
y: 7, 9, 11, 10, 15, 16, 14, 12
```
2. **Calculate the Correlation Coefficient (r):**
The correlation coefficient can be calculated using the formula:
\[
r = \frac{n(\sum xy) - (\sum x)(\sum y)}{\sqrt{[n\sum x^2 - (\sum x)^2][n\sum y^2 - (\sum y)^2]}}
\]
Where:
- \(n\) is the number of pairs (in this case, 8).
- \(\sum xy\) is the sum of the product of each pair of x and y.
- \(\sum x\) and \(\sum y\) are the sums of x values and y values.
- \(\sum x^2\) and \(\sum y^2\) are the sums of squares of x values and y values.
3. **Calculation Steps:**
Let's compute these values:
- \(n = 8\) (number of pairs)
- \(\sum x = 3 + 3 + 6 + 7 + 8 + 8 + 9 + 10 = 54\)
- \(\sum y = 7 + 9 + 11 + 10 + 15 + 16 + 14 + 12 = 88\)
- \(\sum xy = 3*7 + 3*9 + 6*11 + 7*10 + 8*15 + 8*16 + 9*14 + 10*12 = 21 + 27 + 66 + 70 + 120 + 128 + 126 + 120 = 648\)
- \(\sum x^2 = 3^2 + 3^2 + 6^2 + 7^2 + 8^2 + 8^2 + 9^2 + 10^2 = 9 + 9 + 36 + 49 + 64 + 64 + 81 + 100 = 412\)
- \(\sum y^2 = 7^2 + 9^2 + 11^2 + 10^2 + 15^2 + 16^2 + 14^2 + 12^2 = 49 + 81 + 121 + 100 + 225 + 256 + 196 + 144 = 972\)
Now we can plug these values into the correlation formula:
\[
r = \frac{8(648) - (54)(88)}{\sqrt{[8(412) - (54)^2][8(972) - (88)^2]}}
\]
Calculating the components:
- \(8(648) = 5184\)
- \((54)(88) = 4752\)
- \(\sum xy = 5184 - 4752 = 432\)
Now for the denominator:
- \(8(412) = 3296\)
- \((54)^2 = 2916\)
- \(\sum x^2 = 3296 - 2916 = 380\)
For \(y\):
- \(8(972) = 7776\)
- \((88)^2 = 7744\)
- \(\sum y^2 = 7776 - 7744 = 32\)
So now we have:
\[
r = \frac{432}{\sqrt{380 \cdot 32}} = \frac{432}{\sqrt{12160}} \approx \frac{432}{110.24} \approx 0.392
\]
However, it seems that the provided answers don't include 0.39, which indicates that either additional data or an averaging method might need to be considered, or one of the provided values could stand correct.
Given the options you provided, the nearest value would be:
- r = 0.63
Let's choose that. Thus, the answer is **r = 0.63**.
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