In: Statistics and Probability
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 serial correlation. Is there a reason for concern? Justify your answer.
x=c(9.8,9.0,10.5,20.6,28.1,28.3,36.4,51.0,51.1,46.9,50.5,58.5,53.4,66.6,70.6,76.4,88.4,98.6,99.2,90.4,91.2,94.9,94.2,104.1,105.3,116.7,113.2,120.5,124.2,130.2,141.3,151.8,151.1,156.4,155.9,160.0)
serialCorrelationTest(x, test = "rank.von.Neumann",
alternative = "two.sided", conf.level = 0.95)
Here from the serial correlation test the p value is 0.04<0.05. Hence we reject the null hypothesis that is rho =0.