Analysis
[1] "景気ウォッチャー調査:九州: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 34.7 36.0 43.0 44.5 47.6 40.9 39.3 39.9 40.2 36.2 38.6 37.5
2003 37.7 38.3 40.1 37.3 39.3 41.4 44.7 47.1 49.3 51.9 47.4 48.9
2004 50.8 49.0 48.6 52.5 50.4 50.3 51.4 46.3 44.9 45.4 45.8 46.0
2005 49.5 48.3 46.8 47.7 50.3 52.5 51.6 53.1 52.3 52.2 54.6 60.5
2006 55.1 58.1 55.7 50.8 50.2 47.0 46.1 47.4 51.3 51.5 50.8 49.9
2007 49.6 48.1 46.5 46.7 45.0 44.7 43.7 43.4 40.4 43.1 40.7 40.7
2008 36.0 34.4 33.2 31.8 30.0 28.1 27.2 25.1 28.2 20.2 21.9 18.2
2009 20.8 20.9 25.8 28.9 34.6 40.1 37.5 41.1 43.2 41.1 34.9 41.4
2010 43.8 44.7 44.2 47.2 44.4 46.0 46.0 44.5 44.2 48.1 46.9 47.6
2011 48.0 50.1 28.4 30.0 35.6 46.7 48.4 47.7 48.0 50.6 49.6 51.4
2012 44.3 46.8 47.8 49.1 45.2 42.8 41.3 44.5 43.6 42.4 45.2 47.4
2013 50.9 53.7 53.0 52.7 54.8 53.0 52.6 53.3 55.6 55.3 58.0 56.3
2014 56.6 53.5 54.6 37.4 43.7 50.6 49.2 47.7 49.8 47.8 46.4 47.8
2015 45.5 51.1 52.6 52.6 51.1 51.7 51.5 50.2 48.9 50.4 46.3 48.5
2016 47.5 47.3 45.1 31.3 38.7 41.9 47.5 50.1 46.1 50.4 52.3 51.7
2017 51.0 50.0 47.5 49.6 49.1 51.4 48.4 49.7 51.3 50.8 52.9 55.6
2018 49.9 48.9 50.0 50.0 47.8 46.1 45.7 47.1 50.5 48.6 47.7 45.7
2019 43.9 48.0 44.9 44.4 41.8 44.8 42.3 40.0 45.5 35.4
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-16.049 -1.581 1.229 3.228 6.213
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 42.97274 1.63806 26.234 <0.0000000000000002 ***
ID 0.08200 0.07138 1.149 0.258
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.017 on 37 degrees of freedom
Multiple R-squared: 0.03444, Adjusted R-squared: 0.008348
F-statistic: 1.32 on 1 and 37 DF, p-value: 0.258
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.17949, p-value = 0.5622
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.94544, p-value = 0.00007803
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.098468, df = 1, p-value = 0.7537
Box-Ljung test
data: lm_residuals
X-squared = 11.572, df = 1, p-value = 0.0006694
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-17.6326 -1.7066 0.8595 2.4784 8.5490
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 52.69575 0.93422 56.406 < 0.0000000000000002 ***
ID -0.09408 0.01955 -4.811 0.00000695 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.191 on 80 degrees of freedom
Multiple R-squared: 0.2244, Adjusted R-squared: 0.2147
F-statistic: 23.15 on 1 and 80 DF, p-value: 0.000006951
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.12195, p-value = 0.5785
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.81426, p-value = 0.0000000005199
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.06469, df = 1, p-value = 0.7992
Box-Ljung test
data: lm_residuals
X-squared = 26.589, df = 1, p-value = 0.0000002516
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-14.162 -4.870 1.169 5.764 9.182
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 28.10544 1.71234 16.414 < 0.0000000000000002 ***
ID 0.41304 0.04964 8.321 0.0000000000202 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.493 on 57 degrees of freedom
Multiple R-squared: 0.5485, Adjusted R-squared: 0.5406
F-statistic: 69.24 on 1 and 57 DF, p-value: 0.00000000002019
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.16949, p-value = 0.3674
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.46748, p-value = 0.00000000000005572
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 2.8961, df = 1, p-value = 0.08879
Box-Ljung test
data: lm_residuals
X-squared = 36.385, df = 1, p-value = 0.000000001619
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-17.6315 -1.7102 0.8705 2.5150 8.5496
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 52.41168 0.96888 54.09 < 0.0000000000000002 ***
ID -0.09406 0.02104 -4.47 0.0000265 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.265 on 77 degrees of freedom
Multiple R-squared: 0.206, Adjusted R-squared: 0.1957
F-statistic: 19.98 on 1 and 77 DF, p-value: 0.00002654
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.13924, p-value = 0.4302
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.8107, p-value = 0.0000000008805
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.2344, df = 1, p-value = 0.6283
Box-Ljung test
data: lm_residuals
X-squared = 25.895, df = 1, p-value = 0.0000003604