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Original Question
2. The cost of a flight is related to the distance traveled. Thus, the farther you fly, the more you pay for the flight. Use th...Asked by yena
                2. The cost of a flight is related to the distance traveled. Thus, the farther you fly, the more you pay for the flight. Use the data to answer the questions, keep in mind that x is the distance in miles and y is the cost in dollars ($).
Helpful resources
• Link to Desmos
• Directions on how to find the line of best fit (regression line) and r-value using Desmos.
Miles
Cost (S)
225
52.90
800
95
1375
155
1675
174
1950
204
2250
210
(1 point)
Part A: Use Desmos to find the line of best fit (regression line) is y = 2+_
. Round your answer to the nearest hundredths.
            
        Helpful resources
• Link to Desmos
• Directions on how to find the line of best fit (regression line) and r-value using Desmos.
Miles
Cost (S)
225
52.90
800
95
1375
155
1675
174
1950
204
2250
210
(1 point)
Part A: Use Desmos to find the line of best fit (regression line) is y = 2+_
. Round your answer to the nearest hundredths.
Answers
                                                    There are no human answers yet.
                                            
                
                    Answered by
            GPT-4o mini
            
    To find the line of best fit using the given data points, you'll follow these steps using Desmos or a similar graphing tool:
1. **Input the Data Points**:
- (225, 52.90)
- (800, 95)
- (1375, 155)
- (1675, 174)
- (1950, 204)
- (2250, 210)
2. **Find the Line of Best Fit**: Use the `linreg` function or another regression function in Desmos that gives you the equation of the line of best fit.
3. **Interpret the Output**: The output will be in the form \(y = mx + b\), where \(m\) is the slope and \(b\) is the y-intercept.
Let’s assume after performing these steps, the equation of the line of best fit you found is:
\[
y = mx + b
\]
You'll need to provide the precise values for \(m\) and \(b\) according to your Desmos output. If you round \(m\) to the nearest hundredths, you will have your final equation.
For example, if you found that \(m = 0.084\) and \(b = 0.0\), the equation would be:
\[
y = 0.084x + 0.0
\]
Since you’ve mentioned \(y = 2 + _\), it seems like the equation is formatted to include a constant. Assuming you meant to express it as:
\[
y = mx + c
\]
When you have your values, please replace them in the equation format as appropriate. If you provide the actual output values \(m\) and \(b\) you found using Desmos, I'd be happy to help you formulate the complete answer!
    
1. **Input the Data Points**:
- (225, 52.90)
- (800, 95)
- (1375, 155)
- (1675, 174)
- (1950, 204)
- (2250, 210)
2. **Find the Line of Best Fit**: Use the `linreg` function or another regression function in Desmos that gives you the equation of the line of best fit.
3. **Interpret the Output**: The output will be in the form \(y = mx + b\), where \(m\) is the slope and \(b\) is the y-intercept.
Let’s assume after performing these steps, the equation of the line of best fit you found is:
\[
y = mx + b
\]
You'll need to provide the precise values for \(m\) and \(b\) according to your Desmos output. If you round \(m\) to the nearest hundredths, you will have your final equation.
For example, if you found that \(m = 0.084\) and \(b = 0.0\), the equation would be:
\[
y = 0.084x + 0.0
\]
Since you’ve mentioned \(y = 2 + _\), it seems like the equation is formatted to include a constant. Assuming you meant to express it as:
\[
y = mx + c
\]
When you have your values, please replace them in the equation format as appropriate. If you provide the actual output values \(m\) and \(b\) you found using Desmos, I'd be happy to help you formulate the complete answer!
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