Analysis
[1] "労働力調査:完全失業率(%):季節調整値:女: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 3.0
2000 2.9 2.9 3.1 3.2 3.0 2.9 2.9 2.9 3.2 3.0 3.0 3.0
2001 2.8 2.9 2.8 2.8 2.9 3.2 2.9 3.1 3.5 3.2 3.5 3.4
2002 3.6 3.7 3.5 3.8 4.0 3.6 3.2 3.7 3.8 3.7 3.4 3.4
2003 3.3 3.1 3.5 3.3 3.0 3.1 3.8 3.1 2.7 2.9 3.2 3.4
2004 3.5 3.6 3.2 3.2 2.8 2.8 3.1 3.1 3.0 3.0 3.0 2.9
2005 2.7 2.6 2.7 2.7 3.2 3.0 2.6 2.9 3.0 2.8 2.9 2.7
2006 3.2 2.7 2.5 2.4 2.6 3.0 2.5 2.5 2.7 3.2 2.8 2.5
2007 2.5 2.6 2.7 2.6 2.6 2.4 2.9 2.9 2.6 2.4 2.5 2.9
2008 2.6 2.8 3.1 3.3 3.0 2.8 2.6 2.5 2.8 2.9 2.9 3.3
2009 3.5 3.5 3.7 3.8 4.0 3.9 3.7 4.2 4.6 3.9 4.0 4.0
2010 3.7 3.5 3.5 3.8 3.4 3.8 4.0 4.0 3.7 3.5 3.4 3.1
2011 3.4 3.6 3.4 2.9 3.2 3.6 3.8 3.4 2.9 3.3 3.6 3.6
2012 3.8 3.4 3.7 3.3 3.4 3.3 3.4 3.0 2.9 3.3 3.1 3.1
2013 2.8 3.1 2.7 3.2 3.3 2.8 2.8 3.0 3.1 3.5 3.2 3.0
2014 2.9 3.0 3.4 3.4 2.9 3.1 3.3 3.3 3.2 2.6 2.4 2.8
2015 2.7 2.9 2.5 2.6 2.5 2.6 2.7 2.8 3.1 2.6 2.9 2.7
2016 2.9 2.5 2.5 2.4 2.5 2.5 2.2 2.0 2.0 2.2 2.5 2.1
2017 2.3 2.3 2.3 2.5 2.8 2.4 2.0 2.1 2.2 2.4 2.2 2.3
2018 2.1 2.0 2.1 2.0 1.7 1.9 2.3 2.3 2.0 1.9 1.8 2.0
2019 2.2 1.9 2.3 2.2 1.8 2.0 2.1 1.8 1.9 1.8
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.59625 -0.16329 0.03841 0.14622 0.45460
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.814710 0.083432 45.72 < 0.0000000000000002 ***
ID -0.016761 0.003635 -4.61 0.0000467 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2555 on 37 degrees of freedom
Multiple R-squared: 0.3649, Adjusted R-squared: 0.3477
F-statistic: 21.26 on 1 and 37 DF, p-value: 0.00004668
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 = 1.2517, p-value = 0.004335
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.012093, df = 1, p-value = 0.9124
Box-Ljung test
data: lm_residuals
X-squared = 5.7985, df = 1, p-value = 0.01604
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.46528 -0.13165 -0.02987 0.16017 0.48606
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.20515 0.05255 61.00 <0.0000000000000002 ***
ID -0.01682 0.00110 -15.29 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2357 on 80 degrees of freedom
Multiple R-squared: 0.745, Adjusted R-squared: 0.7418
F-statistic: 233.7 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 = 0.14634, p-value = 0.3453
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.1852, p-value = 0.00003315
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 4.6407, df = 1, p-value = 0.03122
Box-Ljung test
data: lm_residuals
X-squared = 12.958, df = 1, p-value = 0.0003186
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-1.0125 -0.2367 0.0104 0.2938 1.1319
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.526125 0.113981 30.936 <0.0000000000000002 ***
ID -0.003413 0.003304 -1.033 0.306
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4322 on 57 degrees of freedom
Multiple R-squared: 0.01838, Adjusted R-squared: 0.001156
F-statistic: 1.067 on 1 and 57 DF, p-value: 0.306
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.11864, p-value = 0.8052
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.45162, p-value = 0.00000000000002175
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 10.418, df = 1, p-value = 0.001248
Box-Ljung test
data: lm_residuals
X-squared = 34.258, df = 1, p-value = 0.000000004826
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.47594 -0.12479 -0.03278 0.13591 0.48359
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.20273 0.05203 61.56 <0.0000000000000002 ***
ID -0.01773 0.00113 -15.69 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.229 on 77 degrees of freedom
Multiple R-squared: 0.7617, Adjusted R-squared: 0.7586
F-statistic: 246.1 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 = 0.13924, p-value = 0.4302
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.1774, p-value = 0.00003735
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 2.4097, df = 1, p-value = 0.1206
Box-Ljung test
data: lm_residuals
X-squared = 13.877, df = 1, p-value = 0.0001952