Consider the following times-series where the data is recorded weekly
Data collected over 36 weeks
t |
X |
t |
X |
t |
X |
t |
X |
t |
X |
t |
X |
1 |
9.8 |
7 |
36.4 |
13 |
53.4 |
19 |
99.2 |
25 |
105.3 |
31 |
141.3 |
2 |
9.0 |
8 |
51.0 |
14 |
66.6 |
20 |
90.4 |
26 |
116.7 |
32 |
151.8 |
3 |
10.5 |
9 |
51.1 |
15 |
70.6 |
21 |
91.2 |
27 |
113.2 |
33 |
151.1 |
4 |
20.6 |
10 |
46.9 |
16 |
76.4 |
22 |
94.9 |
28 |
120.5 |
34 |
156.4 |
5 |
28.1 |
11 |
50.5 |
17 |
88.4 |
23 |
94.2 |
29 |
124.2 |
35 |
155.9 |
6 |
28.3 |
12 |
58.5 |
18 |
98.6 |
24 |
104.1 |
30 |
130.2 |
36 |
160.0 |
Assess the level of heteroskedasticity. Is there a reason for concern? Justify your answer.
We have used MINITAB software to plot the data
the residual vs plot is
by the plot we can see that the residuals are not evenly distributed,
in first part , residuals are negative mostly, in second part, residual is positive , and again in third part residuals are negative.
hence we can see that there is reccursive pattern of the residual so that it causes heteroskedasticity.
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