Consider the following data, reflecting the number of fire trucks arriving at a fire to the property damage done (in millions of dollars) #Trucks 1 1 2 3 3 6 6 8 9 DamageDone 5.5 1.3 8.5 10 12 18 18 25 30 Let x represent the number of fire trucks that arrive on scene, and let y represent the damage done by the fire (in millions of $). a). Find the correlation coefficient, r. b). Do we have a strong or weak correlation? Is it positive or negative? c). Find the equation of the regression line model for this data.
Consider the following data, reflecting the number of fire trucks arriving at a fire to the property damage done (in millions of dollars)
| #Trucks | 1 | 1 | 2 | 3 | 3 | 6 | 6 | 8 | 9 |
| DamageDone | 5.5 | 1.3 | 8.5 | 10 | 12 | 18 | 18 | 25 | 30 |
Let x represent the number of fire trucks that arrive on scene, and let y represent the damage done by the fire (in millions of $).
a). Find the
b). Do we have a strong or weak correlation? Is it positive or negative?
c). Find the equation of the regression line model for this data.
d). Using your equation, predict the damage done (in millions) if 5 fire trucks were needed to
extinguish the fire.
e). Using your equation, predict the number of trucks necessary to combat a fire that deals $20
million dollars in damage.
f). Do we have a causation between these two variables? Are the number of fire trucks
responding causing the fires to grow? Explain briefly why this is NOT a causation.
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