5. Analyzing Lines of Fit
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Let's begin by making a table of the residual values.
x | y | y=-0.5x−2 | y-value From Model | Residual |
---|---|---|---|---|
4 | -1 | -0.5(4)−2 | -4 | -1−(-4)=3 |
6 | -3 | -0.5(6)−2 | -5 | -3−(-5)=2 |
8 | -6 | -0.5(8)−2 | -6 | -6−(-6)=0 |
10 | -8 | -0.5(10)−2 | -7 | -8−(-7)=-1 |
12 | -10 | -0.5(12)−2 | -8 | -10−(-8)=-2 |
14 | -10 | -0.5(14)−2 | -9 | -10−(-9)=-1 |
16 | -10 | -0.5(16)−2 | -10 | -10−(-10)=0 |
18 | -9 | -0.5(18)−2 | -11 | -9−(-11)=2 |
20 | -9 | -0.5(20)−2 | -12 | -9−(-12)=3 |
Now we can create a scatter plot using the given x-values and our residuals. Remember, if the model is a good fit for the data, the scatter plot will be evenly distributed above and below the x-axis. Also, there will be no apparent patterns.
This line of fit does not model the data well. It is not evenly distributed above and below the x-axis. We can see that the residual points form a U-shaped pattern, which suggests the data is not linear.