Analysis
[1] "景気ウォッチャー調査:沖縄:季節調整値:景気の現状判断(方向性)DI:内閣府"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2002 48.2 52.0 56.8 55.2 52.2 51.4 46.7 40.9 47.5 47.8 52.8 50.7
2003 49.9 51.2 37.3 37.2 34.5 47.1 58.1 54.2 56.7 56.6 52.6 57.1
2004 53.2 50.1 55.6 59.6 64.1 54.5 56.0 52.8 48.5 48.9 49.3 47.4
2005 51.2 51.2 50.7 51.5 55.6 55.2 52.8 50.9 50.0 50.6 51.3 60.5
2006 53.6 52.0 51.3 50.1 47.1 50.3 49.5 54.6 52.6 54.8 55.4 53.7
2007 55.9 56.1 51.7 45.1 43.4 46.6 48.6 48.0 50.1 46.6 43.6 43.4
2008 39.8 37.3 38.3 39.1 37.5 33.3 30.8 30.8 33.9 31.0 31.7 27.8
2009 25.3 24.5 31.6 33.1 37.5 46.5 41.1 38.9 40.1 39.2 37.2 37.9
2010 44.1 45.7 47.7 50.5 47.9 51.2 51.1 46.8 50.6 44.0 51.4 51.5
2011 45.9 46.3 32.8 32.6 33.2 54.3 54.1 55.5 50.9 54.7 54.0 55.3
2012 50.8 48.5 53.0 56.7 51.7 49.9 50.3 50.1 46.1 46.2 51.9 51.3
2013 54.4 58.9 52.9 53.9 55.3 51.6 52.1 59.3 61.6 56.2 54.2 53.6
2014 56.6 56.5 58.6 46.1 53.8 59.4 60.6 47.2 49.8 52.0 49.6 53.6
2015 51.6 52.5 51.7 52.6 57.1 54.7 54.7 54.2 55.9 58.1 52.2 49.2
2016 53.6 51.5 42.9 47.6 47.1 47.1 53.2 46.5 51.9 46.8 51.7 52.5
2017 52.2 52.1 55.3 52.2 54.0 50.6 50.5 57.8 55.2 56.9 59.3 60.5
2018 56.0 54.7 54.9 55.8 54.2 56.8 53.3 52.6 48.7 51.4 49.9 45.0
2019 51.4 46.8 50.4 55.1 51.0 49.0 46.3 42.6 45.0 39.0
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-15.1950 -1.4602 0.9398 4.6815 6.7926
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 43.46113 1.91504 22.695 < 0.0000000000000002 ***
ID 0.22810 0.08345 2.733 0.00956 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.865 on 37 degrees of freedom
Multiple R-squared: 0.168, Adjusted R-squared: 0.1455
F-statistic: 7.472 on 1 and 37 DF, p-value: 0.009557
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.12821, p-value = 0.9114
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.81605, p-value = 0.00000751
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.20385, df = 1, p-value = 0.6516
Box-Ljung test
data: lm_residuals
X-squared = 14.332, df = 1, p-value = 0.0001532
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-10.7899 -2.0789 0.1188 3.1155 9.1969
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 55.43008 0.91334 60.689 < 0.0000000000000002 ***
ID -0.06878 0.01912 -3.598 0.000554 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.098 on 80 degrees of freedom
Multiple R-squared: 0.1393, Adjusted R-squared: 0.1285
F-statistic: 12.94 on 1 and 80 DF, p-value: 0.000554
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.073171, p-value = 0.9818
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.0289, p-value = 0.0000006773
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 2.2675, df = 1, p-value = 0.1321
Box-Ljung test
data: lm_residuals
X-squared = 16.603, df = 1, p-value = 0.00004607
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-14.1764 -3.0068 0.3293 5.0441 8.7541
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 31.99918 1.55129 20.627 < 0.0000000000000002 ***
ID 0.41048 0.04497 9.128 0.000000000000955 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.882 on 57 degrees of freedom
Multiple R-squared: 0.5938, Adjusted R-squared: 0.5867
F-statistic: 83.32 on 1 and 57 DF, p-value: 0.0000000000009551
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 = 0.68903, p-value = 0.000000001441
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.017378, df = 1, p-value = 0.8951
Box-Ljung test
data: lm_residuals
X-squared = 25.905, df = 1, p-value = 0.0000003586
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-10.7997 -2.0446 0.1213 2.9922 9.1949
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 55.20545 0.94195 58.608 < 0.0000000000000002 ***
ID -0.06843 0.02046 -3.345 0.00127 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.146 on 77 degrees of freedom
Multiple R-squared: 0.1269, Adjusted R-squared: 0.1155
F-statistic: 11.19 on 1 and 77 DF, p-value: 0.001274
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.10127, p-value = 0.8161
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.0007, p-value = 0.0000004716
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 1.7112, df = 1, p-value = 0.1908
Box-Ljung test
data: lm_residuals
X-squared = 16.984, df = 1, p-value = 0.00003769