Analysis
[1] "労働力調査:完全失業率(%):季節調整値:男女計:15から64歳:45から54:総務省"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1999 3.1
2000 3.3 3.3 3.3 3.3 3.1 3.2 3.4 3.2 3.4 3.2 3.1 3.3
2001 3.1 3.1 3.2 3.2 3.1 3.5 3.3 3.4 3.6 3.8 4.3 4.0
2002 3.9 4.0 3.9 4.0 4.0 3.7 3.8 4.3 4.2 4.1 3.8 3.9
2003 3.8 3.6 3.7 3.7 3.8 3.8 3.8 3.6 3.7 3.5 3.4 3.3
2004 3.5 3.7 3.6 3.5 3.4 3.5 3.5 3.1 3.1 3.1 3.2 3.3
2005 3.1 3.0 3.0 3.0 3.1 3.1 3.0 3.1 3.0 3.0 3.1 3.0
2006 3.3 3.0 2.8 2.8 2.9 2.9 2.9 2.9 2.8 3.0 3.1 2.9
2007 2.7 2.8 2.8 2.7 2.6 2.6 2.8 2.8 2.8 2.9 2.7 2.8
2008 2.8 3.0 2.9 3.0 3.0 3.0 2.7 2.7 2.9 2.9 3.1 3.4
2009 3.3 3.5 3.8 3.9 4.1 3.7 3.9 4.2 4.3 4.0 4.0 3.9
2010 3.9 3.8 4.0 4.0 3.6 3.9 4.0 4.1 4.1 3.7 3.6 3.7
2011 3.8 3.7 3.4 3.4 3.5 3.9 3.9 3.4 3.2 3.4 3.7 3.5
2012 3.5 3.3 3.6 3.3 3.5 3.6 3.4 3.3 3.1 3.2 3.2 3.2
2013 3.0 3.4 3.2 3.4 3.4 2.9 3.1 3.3 3.2 3.6 3.3 3.0
2014 3.0 2.9 3.1 3.3 2.8 2.9 3.1 3.0 3.1 2.9 2.7 2.9
2015 2.8 2.8 2.7 2.6 2.6 2.8 2.9 2.9 2.9 2.6 2.8 2.9
2016 2.9 2.7 2.6 2.5 2.7 2.6 2.2 2.3 2.3 2.4 2.5 2.3
2017 2.3 2.5 2.4 2.4 2.8 2.4 2.3 2.2 2.3 2.3 2.3 2.3
2018 2.2 2.0 2.1 2.2 1.8 2.0 2.2 2.2 2.0 2.1 1.9 2.0
2019 1.9 1.8 2.3 2.2 1.9 2.0 2.2 1.9 1.9 2.1
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.33943 -0.12080 -0.02038 0.09918 0.31874
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.041296 0.056020 72.140 < 0.0000000000000002 ***
ID -0.020911 0.002441 -8.566 0.000000000261 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1716 on 37 degrees of freedom
Multiple R-squared: 0.6648, Adjusted R-squared: 0.6557
F-statistic: 73.38 on 1 and 37 DF, p-value: 0.0000000002607
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 = 1.472, p-value = 0.03082
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.014394, df = 1, p-value = 0.9045
Box-Ljung test
data: lm_residuals
X-squared = 2.9222, df = 1, p-value = 0.08737
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.36112 -0.10644 0.00652 0.10526 0.46879
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.3075881 0.0372908 88.7 <0.0000000000000002 ***
ID -0.0176380 0.0007805 -22.6 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1673 on 80 degrees of freedom
Multiple R-squared: 0.8646, Adjusted R-squared: 0.8629
F-statistic: 510.6 on 1 and 80 DF, p-value: < 0.00000000000000022
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.097561, p-value = 0.8332
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.5021, p-value = 0.007886
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.41593, df = 1, p-value = 0.519
Box-Ljung test
data: lm_residuals
X-squared = 4.0204, df = 1, p-value = 0.04495
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.8969 -0.2520 -0.0127 0.3297 0.7288
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.602338 0.102734 35.065 <0.0000000000000002 ***
ID -0.001829 0.002978 -0.614 0.541
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3896 on 57 degrees of freedom
Multiple R-squared: 0.006576, Adjusted R-squared: -0.01085
F-statistic: 0.3773 on 1 and 57 DF, p-value: 0.5415
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.084746, p-value = 0.9854
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.31225, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 17.729, df = 1, p-value = 0.00002546
Box-Ljung test
data: lm_residuals
X-squared = 41.522, df = 1, p-value = 0.0000000001165
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.35897 -0.10910 0.00884 0.09991 0.45871
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.2663096 0.0378108 86.39 <0.0000000000000002 ***
ID -0.0178603 0.0008212 -21.75 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1664 on 77 degrees of freedom
Multiple R-squared: 0.86, Adjusted R-squared: 0.8582
F-statistic: 473 on 1 and 77 DF, p-value: < 0.00000000000000022
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.11392, p-value = 0.6878
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.4572, p-value = 0.004858
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.24207, df = 1, p-value = 0.6227
Box-Ljung test
data: lm_residuals
X-squared = 5.2088, df = 1, p-value = 0.02247