5. Analyzing Lines of Fit
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Let's begin by making a table of the residual values. Note that, in our table, x represents the years since 2005 and y represents the attendance at the amusement park (in thousands).
x | y | y=-9.8x+850 | y-value From Model | Residual |
---|---|---|---|---|
0 | 850 | -9.8(0)+850 | 850 | 850−850=0 |
1 | 845 | -9.8(1)+850 | 840.2 | 845−840.2=4.8 |
2 | 828 | -9.8(2)+850 | 830.4 | 828−830.4=-2.4 |
3 | 798 | -9.8(3)+850 | 820.6 | 798−820.6=-22.6 |
4 | 800 | -9.8(4)+850 | 810.8 | 800−810.8=-10.8 |
5 | 792 | -9.8(5)+850 | 801 | 792−801=-9 |
6 | 785 | -9.8(6)+850 | 791.2 | 785−791.2=-6.2 |
7 | 781 | -9.8(7)+850 | 781.4 | 781−781.4=-0.4 |
8 | 775 | -9.8(8)+850 | 771.6 | 775−771.6=3.4 |
9 | 760 | -9.8(9)+850 | 761.8 | 760−761.8=-1.8 |
Now we need to create a scatter plot using the given x-values and our residuals. Recall that 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.
We can see that this line of fit does not model the data well. It is not evenly distributed above and below the x-axis. The residual scatter plot shows that only two points are positive residuals, one point is perfectly explained by the line, and the rest are negative residuals.