Analysis
[1] "労働力調査:完全失業率(%):季節調整値:男: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 10.8
2000 9.1 10.3 10.7 9.7 9.3 10.4 9.8 10.0 10.2 10.7 10.9 11.2
2001 10.7 9.4 9.5 10.0 11.2 10.4 10.9 10.9 12.1 11.4 10.8 10.8
2002 11.9 12.2 11.0 11.1 10.7 10.9 11.5 11.2 10.4 10.6 11.6 10.8
2003 11.0 10.7 12.4 12.0 11.9 12.0 11.2 11.9 10.7 11.3 11.3 11.7
2004 10.9 11.5 11.0 10.9 10.7 11.3 11.2 10.8 11.3 10.1 10.2 9.7
2005 10.1 10.3 10.0 10.7 10.3 9.2 9.5 9.4 9.3 9.7 9.8 9.9
2006 9.2 9.1 9.5 9.3 9.2 9.1 8.9 9.0 8.8 9.2 7.6 7.9
2007 9.2 9.0 8.2 7.9 7.9 8.3 7.6 7.4 7.4 8.5 8.6 8.4
2008 8.0 7.2 6.8 7.5 7.8 7.7 7.8 8.8 9.1 7.2 7.6 8.0
2009 8.2 8.9 9.4 9.2 9.2 9.4 11.7 10.2 11.3 10.8 10.8 11.1
2010 11.0 11.4 11.4 9.7 11.3 12.7 10.0 9.5 9.2 9.9 10.4 9.8
2011 9.1 9.2 9.6 10.0 9.5 9.1 9.0 9.8 8.6 9.9 10.2 10.4
2012 9.7 10.2 9.8 9.6 9.1 7.5 9.1 9.1 7.9 8.2 8.0 8.0
2013 8.2 7.4 7.5 8.5 7.5 7.2 7.4 7.6 9.0 7.7 7.2 6.6
2014 7.0 7.0 7.7 7.7 7.6 8.4 7.4 6.1 5.7 6.2 6.9 7.0
2015 6.9 6.5 5.9 5.5 5.9 6.1 5.6 6.0 5.8 5.7 5.9 6.1
2016 6.0 6.2 6.3 5.6 5.6 5.3 5.6 5.7 5.8 5.7 5.2 5.5
2017 5.5 4.4 4.3 5.1 5.3 5.0 5.2 4.9 5.1 4.9 4.2 3.9
2018 3.5 4.8 4.7 4.3 4.0 4.1 3.9 4.2 3.9 3.8 4.1 3.7
2019 3.3 3.8 4.1 4.1 4.6 4.1 2.9 3.7 5.1 4.6
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-1.3136 -0.5112 -0.2068 0.5265 2.1884
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.14831 0.24375 45.736 < 0.0000000000000002 ***
ID -0.07075 0.01062 -6.661 0.0000000805 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7465 on 37 degrees of freedom
Multiple R-squared: 0.5453, Adjusted R-squared: 0.533
F-statistic: 44.37 on 1 and 37 DF, p-value: 0.00000008053
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 = 1.4168, p-value = 0.01998
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.16884, df = 1, p-value = 0.6811
Box-Ljung test
data: lm_residuals
X-squared = 3.4435, df = 1, p-value = 0.0635
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-1.09751 -0.32876 -0.00927 0.28264 1.59072
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.916621 0.120257 65.83 <0.0000000000000002 ***
ID -0.054412 0.002517 -21.62 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5395 on 80 degrees of freedom
Multiple R-squared: 0.8538, Adjusted R-squared: 0.852
F-statistic: 467.3 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 = 0.14634, p-value = 0.3453
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.0748, p-value = 0.000002345
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.00017986, df = 1, p-value = 0.9893
Box-Ljung test
data: lm_residuals
X-squared = 15.869, df = 1, p-value = 0.00006787
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-2.3965 -0.8890 -0.1154 0.6729 3.2729
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.647282 0.326395 29.557 <0.0000000000000002 ***
ID -0.008469 0.009462 -0.895 0.375
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.238 on 57 degrees of freedom
Multiple R-squared: 0.01386, Adjusted R-squared: -0.003441
F-statistic: 0.8011 on 1 and 57 DF, p-value: 0.3745
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 = 0.51551, p-value = 0.0000000000007781
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 4.427, df = 1, p-value = 0.03537
Box-Ljung test
data: lm_residuals
X-squared = 31.046, df = 1, p-value = 0.0000000252
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-1.09605 -0.33197 -0.01125 0.27939 1.59835
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.769815 0.123985 62.67 <0.0000000000000002 ***
ID -0.054720 0.002693 -20.32 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5458 on 77 degrees of freedom
Multiple R-squared: 0.8428, Adjusted R-squared: 0.8408
F-statistic: 412.9 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 = 0.088608, p-value = 0.9184
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.0175, p-value = 0.0000007523
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.033344, df = 1, p-value = 0.8551
Box-Ljung test
data: lm_residuals
X-squared = 16.528, df = 1, p-value = 0.00004793