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Create a linear model for the data in the table. x 2 6 9 10 11 13 17 y 7 10 14 17 18 21 27 Question content area bottom Part 1...Question
Create a linear model for the data in the table.
x
3
7
10
11
12
14
18
y
6
10
15
16
18
21
26
Question content area bottom
Part 1
Write a linear model for the data in the table.
yequals
enter your response herexplus(
enter your response here)
(Type an integer or decimal rounded to three decimal places as needed.)
x
3
7
10
11
12
14
18
y
6
10
15
16
18
21
26
Question content area bottom
Part 1
Write a linear model for the data in the table.
yequals
enter your response herexplus(
enter your response here)
(Type an integer or decimal rounded to three decimal places as needed.)
Answers
There are no human answers yet.
Answered by
GPT-4o mini
To create a linear model for the given data, we can use the least squares method to determine the best-fitting line of the form \( y = mx + b \), where \( m \) is the slope and \( b \) is the y-intercept.
Given the data:
\[
\begin{align*}
x: & \quad 3, 7, 10, 11, 12, 14, 18 \\
y: & \quad 6, 10, 15, 16, 18, 21, 26 \\
\end{align*}
\]
### Step 1: Calculate the necessary sums
1. \( n = 7 \) (the number of data points)
2. Sum of \( x \) values:
\( \sum x = 3 + 7 + 10 + 11 + 12 + 14 + 18 = 75 \)
3. Sum of \( y \) values:
\( \sum y = 6 + 10 + 15 + 16 + 18 + 21 + 26 = 112 \)
4. Sum of \( xy \) values:
\( \sum xy = (3)(6) + (7)(10) + (10)(15) + (11)(16) + (12)(18) + (14)(21) + (18)(26) \)
\( = 18 + 70 + 150 + 176 + 216 + 294 + 468 = 1392 \)
5. Sum of \( x^2 \) values:
\( \sum x^2 = (3^2) + (7^2) + (10^2) + (11^2) + (12^2) + (14^2) + (18^2) \)
\( = 9 + 49 + 100 + 121 + 144 + 196 + 324 = 943 \)
### Step 2: Calculate the slope \( m \) and intercept \( b \)
Using the formulas:
\[
m = \frac{n(\sum xy) - (\sum x)(\sum y)}{n(\sum x^2) - (\sum x)^2}
\]
\[
b = \frac{\sum y - m(\sum x)}{n}
\]
**Calculating \( m \)**:
\[
m = \frac{7 \cdot 1392 - 75 \cdot 112}{7 \cdot 943 - 75^2}
\]
\[
m = \frac{9744 - 8400}{6601 - 5625} = \frac{1344}{976} \approx 1.376
\]
**Calculating \( b \)**:
\[
b = \frac{112 - 1.376 \cdot 75}{7}
\]
\[
b = \frac{112 - 103.2}{7} = \frac{8.8}{7} \approx 1.257
\]
### Conclusion
The linear model for the data is:
\[
y = 1.376x + 1.257
\]
So, rounding to three decimal places, the final answer is:
\[
y = 1.376x + 1.257
\]
Given the data:
\[
\begin{align*}
x: & \quad 3, 7, 10, 11, 12, 14, 18 \\
y: & \quad 6, 10, 15, 16, 18, 21, 26 \\
\end{align*}
\]
### Step 1: Calculate the necessary sums
1. \( n = 7 \) (the number of data points)
2. Sum of \( x \) values:
\( \sum x = 3 + 7 + 10 + 11 + 12 + 14 + 18 = 75 \)
3. Sum of \( y \) values:
\( \sum y = 6 + 10 + 15 + 16 + 18 + 21 + 26 = 112 \)
4. Sum of \( xy \) values:
\( \sum xy = (3)(6) + (7)(10) + (10)(15) + (11)(16) + (12)(18) + (14)(21) + (18)(26) \)
\( = 18 + 70 + 150 + 176 + 216 + 294 + 468 = 1392 \)
5. Sum of \( x^2 \) values:
\( \sum x^2 = (3^2) + (7^2) + (10^2) + (11^2) + (12^2) + (14^2) + (18^2) \)
\( = 9 + 49 + 100 + 121 + 144 + 196 + 324 = 943 \)
### Step 2: Calculate the slope \( m \) and intercept \( b \)
Using the formulas:
\[
m = \frac{n(\sum xy) - (\sum x)(\sum y)}{n(\sum x^2) - (\sum x)^2}
\]
\[
b = \frac{\sum y - m(\sum x)}{n}
\]
**Calculating \( m \)**:
\[
m = \frac{7 \cdot 1392 - 75 \cdot 112}{7 \cdot 943 - 75^2}
\]
\[
m = \frac{9744 - 8400}{6601 - 5625} = \frac{1344}{976} \approx 1.376
\]
**Calculating \( b \)**:
\[
b = \frac{112 - 1.376 \cdot 75}{7}
\]
\[
b = \frac{112 - 103.2}{7} = \frac{8.8}{7} \approx 1.257
\]
### Conclusion
The linear model for the data is:
\[
y = 1.376x + 1.257
\]
So, rounding to three decimal places, the final answer is:
\[
y = 1.376x + 1.257
\]
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