Analysis
[1] "景気ウォッチャー調査:沖縄:季節調整値:景気の先行き判断(方向性)DI:内閣府"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2002 46.2 56.5 49.3 52.0 49.4 47.5 48.4 47.8 45.8 49.5 50.8 51.4
2003 49.4 50.4 32.6 38.0 42.4 50.1 50.9 53.9 55.0 56.3 55.7 53.0
2004 52.0 51.5 58.7 56.9 58.4 52.9 53.6 49.9 50.7 50.0 48.9 47.5
2005 51.8 52.4 51.5 53.5 52.5 53.4 52.3 54.4 57.4 55.1 56.3 59.0
2006 58.7 53.8 53.7 51.9 53.4 53.6 46.9 55.1 54.0 58.8 55.4 54.0
2007 57.1 54.3 54.0 52.4 45.5 53.0 54.1 51.2 55.8 49.2 46.9 48.7
2008 44.1 45.8 41.1 40.8 37.5 30.5 35.9 39.6 39.0 40.1 41.0 24.3
2009 27.9 34.8 38.2 38.6 40.6 39.7 45.3 39.1 48.3 43.6 41.9 36.5
2010 43.8 44.3 47.1 50.2 49.9 52.1 51.3 51.5 47.1 46.9 50.8 49.2
2011 48.5 47.0 27.1 33.3 43.7 48.0 53.1 57.2 56.1 54.6 54.9 51.1
2012 57.3 58.6 56.1 53.1 53.0 52.6 50.3 49.9 52.5 54.0 53.3 58.8
2013 61.1 58.4 58.3 60.2 56.2 50.5 55.9 56.3 57.0 57.5 58.7 53.5
2014 48.4 48.3 39.8 61.0 57.5 58.1 55.9 52.7 55.1 53.2 53.2 50.1
2015 50.4 52.9 55.9 53.4 53.7 51.9 54.2 54.6 52.0 54.0 51.9 55.0
2016 58.3 51.1 48.1 48.7 46.8 46.6 46.0 50.6 48.6 49.5 52.4 53.0
2017 52.9 53.3 53.4 54.8 51.1 49.9 57.6 53.7 61.4 59.9 58.5 55.9
2018 57.0 58.8 56.1 55.2 57.6 57.3 56.2 55.1 55.3 53.0 49.0 53.1
2019 56.4 44.3 53.3 53.9 49.4 47.5 47.0 45.3 42.0 46.9
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-21.7709 -1.6829 0.1008 3.5393 6.7692
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 43.25560 1.86640 23.176 < 0.0000000000000002 ***
ID 0.31196 0.08133 3.836 0.000471 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.716 on 37 degrees of freedom
Multiple R-squared: 0.2845, Adjusted R-squared: 0.2652
F-statistic: 14.71 on 1 and 37 DF, p-value: 0.0004712
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.30769, p-value = 0.04927
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.71587, p-value = 0.0000008423
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.10092, df = 1, p-value = 0.7507
Box-Ljung test
data: lm_residuals
X-squared = 17.093, df = 1, p-value = 0.00003559
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-14.803 -2.700 0.525 3.134 8.900
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 55.35357 0.96375 57.436 <0.0000000000000002 ***
ID -0.05007 0.02017 -2.482 0.0152 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.324 on 80 degrees of freedom
Multiple R-squared: 0.07151, Adjusted R-squared: 0.0599
F-statistic: 6.161 on 1 and 80 DF, p-value: 0.01515
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.15854, p-value = 0.2552
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.9352, p-value = 0.00000003999
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.17861, df = 1, p-value = 0.6726
Box-Ljung test
data: lm_residuals
X-squared = 22.541, df = 1, p-value = 0.000002058
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-21.4692 -1.8411 0.7623 3.3139 7.1028
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 34.67820 1.44442 24.008 < 0.0000000000000002 ***
ID 0.39688 0.04187 9.479 0.000000000000258 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.477 on 57 degrees of freedom
Multiple R-squared: 0.6118, Adjusted R-squared: 0.605
F-statistic: 89.84 on 1 and 57 DF, p-value: 0.0000000000002578
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.13559, p-value = 0.6544
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.87912, p-value = 0.0000005104
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.059449, df = 1, p-value = 0.8074
Box-Ljung test
data: lm_residuals
X-squared = 19.37, df = 1, p-value = 0.00001077
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-14.312 -2.857 0.610 3.100 8.884
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 54.56767 0.98057 55.649 <0.0000000000000002 ***
ID -0.03799 0.02130 -1.784 0.0784 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.316 on 77 degrees of freedom
Multiple R-squared: 0.03968, Adjusted R-squared: 0.02721
F-statistic: 3.182 on 1 and 77 DF, p-value: 0.07839
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.1519, p-value = 0.3233
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.96753, p-value = 0.0000001798
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.27545, df = 1, p-value = 0.5997
Box-Ljung test
data: lm_residuals
X-squared = 20.3, df = 1, p-value = 0.00000662