Question
In: Math
Given the following data where city MPG is the response variable and weight is the explanatory...
- Given the following data where city MPG is the response variable and weight is the explanatory variable, explain why a regression line would be appropriate to analyze the relationship between these variables:
|
Model |
City MPG |
Weight |
|
Mazda MX-5 Miata |
25 |
2365 |
|
Mercedes/Benz SLK |
22 |
3020 |
|
Mitsubishi Eclipse |
23 |
3235 |
|
Pontiac Firebird |
18 |
3545 |
|
Porsche Boxster |
19 |
2905 |
|
Saturn SC |
27 |
2420 |
- Construct the regression line for this data.
- Interpret the meaning of the y-intercept and the slope within this scenario.
- What would you predict the city MPG to be for a car that weighs 3000 pounds?
- If a car that weighs 3000 pounds actually gets 32 MPG, would this be unusual? Calculate the residual and talk about what that value represents
Solutions
Expert Solution
Using Minitab software, we get the following output :
The regression line is :
Interpretation of y intercept : When weight = 0 pound, the predicted value of city MPG is 39.68 MPG
Interpretation of slope : For a unit increase in weight, the predicted value of City MPG will decrease by 0.00595 MPG.
The predicted value of city MPG to be for a car that weighs 3000 pounds
= 39.68 - (0.00595*3000)
= 21.83
If a car that weighs 3000 pounds actually gets 32 MPG, residual = actual - predicted = 32 - 21.83 = 10.17
Since residual is quite high, so we can say that this would be unusual.
milcah answered 2 years agoRelated Solutions
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6
2
8
7
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yy
7
4
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9
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1
2.8
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1.3
3
6.8
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4
3.0
0.2
5
4.7
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6
5.0
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7
5.0
0.9
8
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7.0
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10
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