Analysis
[1] "景気ウォッチャー調査:九州: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 37.7 41.4 46.3 47.1 47.6 46.0 43.6 43.6 42.9 38.3 40.4 40.1
2003 39.8 39.9 39.3 40.4 41.2 44.0 45.6 47.4 49.6 50.9 50.4 52.1
2004 53.0 53.6 53.6 54.5 53.3 53.4 52.3 50.7 47.6 47.0 47.1 45.5
2005 47.7 49.3 48.0 48.0 50.4 50.0 52.5 51.6 53.0 54.6 57.3 58.9
2006 60.7 56.8 54.9 53.2 51.9 50.9 49.6 50.9 53.2 53.4 52.4 51.1
2007 52.1 49.9 49.9 49.0 47.2 46.8 46.3 45.8 45.6 45.0 41.2 41.0
2008 36.4 39.4 34.7 31.9 31.6 29.6 29.6 29.4 31.0 24.2 28.6 22.1
2009 25.2 28.4 33.8 37.0 39.5 43.4 42.5 43.7 45.0 44.4 38.6 41.8
2010 45.0 45.2 45.8 46.6 45.6 45.8 47.1 41.2 45.7 48.6 48.4 49.3
2011 49.9 48.8 29.5 37.4 43.7 48.2 46.2 50.1 49.3 49.6 49.1 47.1
2012 48.8 49.3 48.8 48.1 43.5 41.9 44.7 44.8 46.0 42.3 46.3 50.7
2013 57.8 55.5 55.6 53.7 53.9 52.3 56.2 52.6 56.3 57.1 60.9 57.1
2014 52.1 37.7 34.3 49.2 53.8 54.3 51.3 53.1 49.9 48.4 48.5 49.7
2015 50.7 51.2 54.0 56.5 52.5 54.0 51.9 49.2 54.5 50.8 50.3 49.6
2016 50.5 48.0 49.3 41.2 47.7 46.2 50.0 51.5 51.0 53.2 52.5 51.3
2017 51.8 53.2 51.5 51.4 49.8 51.0 51.3 51.5 51.9 53.3 54.2 54.4
2018 54.0 53.0 52.1 52.8 52.0 51.1 47.9 52.2 52.3 49.7 51.2 46.5
2019 50.9 49.2 46.9 48.2 46.1 47.9 44.7 39.1 36.2 44.9
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-16.0789 -2.0136 0.8696 2.9364 4.4652
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 44.28178 1.33687 33.124 <0.0000000000000002 ***
ID 0.07206 0.05825 1.237 0.224
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.094 on 37 degrees of freedom
Multiple R-squared: 0.03972, Adjusted R-squared: 0.01377
F-statistic: 1.53 on 1 and 37 DF, p-value: 0.2238
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.20513, p-value = 0.3888
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.1738, p-value = 0.001836
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.012002, df = 1, p-value = 0.9128
Box-Ljung test
data: lm_residuals
X-squared = 6.8211, df = 1, p-value = 0.009009
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-18.3267 -1.4478 0.8391 2.4795 7.9930
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 53.67787 0.93655 57.315 < 0.0000000000000002 ***
ID -0.07008 0.01960 -3.575 0.000597 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.202 on 80 degrees of freedom
Multiple R-squared: 0.1378, Adjusted R-squared: 0.127
F-statistic: 12.78 on 1 and 80 DF, p-value: 0.0005975
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.10976, p-value = 0.7099
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.75331, p-value = 0.00000000004074
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.64162, df = 1, p-value = 0.4231
Box-Ljung test
data: lm_residuals
X-squared = 32.045, df = 1, p-value = 0.00000001506
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-14.9535 -3.6289 0.8152 4.9892 7.0177
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 31.87066 1.45465 21.909 < 0.0000000000000002 ***
ID 0.35951 0.04217 8.526 0.00000000000927 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.516 on 57 degrees of freedom
Multiple R-squared: 0.5605, Adjusted R-squared: 0.5528
F-statistic: 72.69 on 1 and 57 DF, p-value: 0.000000000009274
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 = 0.56174, p-value = 0.000000000007583
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 1.0299, df = 1, p-value = 0.3102
Box-Ljung test
data: lm_residuals
X-squared = 31.917, df = 1, p-value = 0.00000001609
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-17.989 -1.449 0.808 2.545 8.364
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 53.02989 0.96264 55.088 < 0.0000000000000002 ***
ID -0.06176 0.02091 -2.954 0.00416 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.237 on 77 degrees of freedom
Multiple R-squared: 0.1018, Adjusted R-squared: 0.09013
F-statistic: 8.726 on 1 and 77 DF, p-value: 0.004159
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.17722, p-value = 0.1677
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.76349, p-value = 0.0000000001326
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.83042, df = 1, p-value = 0.3622
Box-Ljung test
data: lm_residuals
X-squared = 30.952, df = 1, p-value = 0.00000002644