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:15から24:総務省"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1999 9.2
2000 8.2 9.0 9.2 8.7 8.5 9.2 8.7 8.9 9.1 9.4 10.0 10.2
2001 9.6 8.8 8.9 9.0 9.8 9.6 9.7 9.8 10.8 10.3 9.6 9.6
2002 10.3 10.7 9.9 9.9 9.8 10.1 10.2 10.2 9.4 9.3 9.4 9.6
2003 10.2 10.1 10.8 10.7 10.6 10.5 9.8 9.9 9.4 9.6 9.9 9.7
2004 10.0 9.7 9.7 9.6 9.5 9.3 9.8 9.6 9.3 9.1 9.3 8.9
2005 8.2 8.7 8.6 9.2 8.9 8.0 8.7 8.4 8.0 8.7 8.9 9.0
2006 8.1 7.6 8.2 8.2 8.1 8.8 8.1 7.8 8.1 8.2 6.9 7.1
2007 8.7 8.5 7.6 7.0 7.3 7.3 6.7 7.8 8.2 7.9 7.9 8.0
2008 7.3 7.1 6.4 7.0 7.0 7.0 7.6 7.8 7.9 6.8 7.7 7.5
2009 7.9 8.9 9.4 9.0 8.9 8.7 10.0 9.3 9.6 9.6 9.2 9.8
2010 9.2 9.2 9.7 8.8 10.1 10.9 9.0 8.6 8.9 9.4 9.5 8.6
2011 8.3 8.1 8.5 8.4 8.0 8.0 8.1 8.5 7.6 8.1 8.6 9.1
2012 9.0 9.3 8.7 8.6 8.2 7.5 8.4 8.0 7.4 7.8 6.9 7.2
2013 6.9 6.7 6.7 7.9 7.0 6.3 6.1 7.0 7.1 6.7 6.7 6.0
2014 6.3 5.8 6.6 6.0 6.3 7.0 6.6 5.5 5.6 5.7 6.5 6.3
2015 6.9 6.2 5.2 5.3 5.4 5.5 5.6 5.5 5.5 5.5 5.3 5.1
2016 5.2 5.8 5.9 5.2 5.2 5.1 4.7 5.1 5.0 5.1 4.5 4.7
2017 4.7 4.3 4.6 5.1 5.2 4.6 4.8 4.5 4.8 4.4 4.3 4.5
2018 3.6 4.1 3.9 3.8 3.5 3.9 3.8 3.8 3.4 3.3 3.4 3.3
2019 3.2 3.4 3.6 3.9 3.8 3.8 3.4 3.7 4.8 5.0
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.8256 -0.4339 -0.1616 0.3621 1.6858
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.687314 0.190065 50.968 < 0.0000000000000002 ***
ID -0.052571 0.008282 -6.348 0.000000213 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5821 on 37 degrees of freedom
Multiple R-squared: 0.5213, Adjusted R-squared: 0.5084
F-statistic: 40.29 on 1 and 37 DF, p-value: 0.0000002127
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.15385, p-value = 0.7523
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.1767, p-value = 0.0019
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.0061374, df = 1, p-value = 0.9376
Box-Ljung test
data: lm_residuals
X-squared = 6.867, df = 1, p-value = 0.00878
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.67917 -0.30193 -0.06234 0.24860 1.65472
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.991870 0.101138 69.13 <0.0000000000000002 ***
ID -0.044471 0.002117 -21.01 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4537 on 80 degrees of freedom
Multiple R-squared: 0.8465, Adjusted R-squared: 0.8446
F-statistic: 441.3 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 = 0.13415, p-value = 0.4541
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.997, p-value = 0.0000002708
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 1.5074, df = 1, p-value = 0.2195
Box-Ljung test
data: lm_residuals
X-squared = 14.88, df = 1, p-value = 0.0001146
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-1.9562 -0.7201 0.1315 0.6465 2.4053
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.834600 0.245511 35.984 <0.0000000000000002 ***
ID -0.013074 0.007117 -1.837 0.0714 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9309 on 57 degrees of freedom
Multiple R-squared: 0.0559, Adjusted R-squared: 0.03933
F-statistic: 3.375 on 1 and 57 DF, p-value: 0.07142
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.15254, p-value = 0.5021
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.43304, p-value = 0.000000000000006876
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 4.3027, df = 1, p-value = 0.03805
Box-Ljung test
data: lm_residuals
X-squared = 33.166, df = 1, p-value = 0.000000008463
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.67704 -0.32014 -0.04878 0.25618 1.66535
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.879844 0.104819 65.64 <0.0000000000000002 ***
ID -0.044876 0.002277 -19.71 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4614 on 77 degrees of freedom
Multiple R-squared: 0.8346, Adjusted R-squared: 0.8325
F-statistic: 388.6 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 = 0.18987, p-value = 0.116
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.90567, p-value = 0.00000002589
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 1.0972, df = 1, p-value = 0.2949
Box-Ljung test
data: lm_residuals
X-squared = 15.027, df = 1, p-value = 0.000106