Analysis
[1] "労働力調査(主要項目):労働力人口(万人):季節調整値:男:総務省"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1999 4023
2000 4020 4020 4011 4006 4007 4001 3999 4003 4017 4027 4031 4020
2001 4003 4004 4005 4005 3997 3983 3989 3994 3979 3974 3987 3981
2002 3973 3971 3975 3962 3946 3952 3960 3963 3952 3953 3931 3933
2003 3936 3937 3949 3940 3945 3955 3935 3923 3925 3918 3926 3922
2004 3915 3913 3904 3921 3912 3898 3905 3911 3892 3886 3900 3907
2005 3908 3897 3888 3892 3899 3895 3900 3895 3917 3916 3905 3902
2006 3903 3897 3907 3893 3896 3907 3904 3904 3908 3913 3903 3902
2007 3904 3924 3924 3921 3918 3924 3922 3912 3915 3918 3915 3909
2008 3910 3913 3899 3908 3921 3904 3892 3911 3896 3897 3902 3900
2009 3887 3885 3880 3884 3879 3860 3865 3866 3860 3858 3854 3850
2010 3861 3843 3862 3851 3843 3857 3854 3852 3857 3844 3836 3845
2011 3846 3845 3832 3834 3828 3821 3826 3810 3804 3805 3817 3829
2012 3813 3810 3803 3800 3788 3798 3793 3788 3792 3800 3789 3773
2013 3782 3785 3780 3785 3785 3785 3785 3784 3780 3782 3791 3766
2014 3759 3771 3791 3785 3787 3776 3779 3784 3779 3770 3764 3769
2015 3778 3782 3777 3761 3763 3771 3766 3768 3782 3775 3770 3781
2016 3783 3774 3759 3774 3779 3783 3783 3782 3781 3788 3785 3801
2017 3793 3773 3771 3775 3784 3781 3791 3794 3792 3787 3778 3783
2018 3792 3806 3818 3817 3812 3813 3808 3812 3814 3823 3847 3834
2019 3803 3831 3843 3820 3815 3821 3828 3835 3825 3836
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-14.693 -7.159 1.189 6.192 17.903
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3868.283 2.777 1393.1 <0.0000000000000002 ***
ID -2.118 0.121 -17.5 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 8.504 on 37 degrees of freedom
Multiple R-squared: 0.8923, Adjusted R-squared: 0.8893
F-statistic: 306.4 on 1 and 37 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 = 0.15385, p-value = 0.7523
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.2412, p-value = 0.003882
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.39684, df = 1, p-value = 0.5287
Box-Ljung test
data: lm_residuals
X-squared = 4.776, df = 1, p-value = 0.02886
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-29.692 -10.959 -1.609 12.138 37.659
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3763.52575 3.25638 1155.740 < 0.0000000000000002 ***
ID 0.64528 0.06816 9.467 0.0000000000000106 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 14.61 on 80 degrees of freedom
Multiple R-squared: 0.5284, Adjusted R-squared: 0.5225
F-statistic: 89.63 on 1 and 80 DF, p-value: 0.00000000000001064
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 = 0.49185, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.97185, df = 1, p-value = 0.3242
Box-Ljung test
data: lm_residuals
X-squared = 45.759, df = 1, p-value = 0.00000000001337
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-16.5212 -5.7129 0.3337 5.0248 18.1912
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3907.18703 2.19714 1778.31 <0.0000000000000002 ***
ID -2.19041 0.06369 -34.39 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 8.331 on 57 degrees of freedom
Multiple R-squared: 0.954, Adjusted R-squared: 0.9532
F-statistic: 1183 on 1 and 57 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 = 0.18644, p-value = 0.2582
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.2642, p-value = 0.001018
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 1.3631, df = 1, p-value = 0.243
Box-Ljung test
data: lm_residuals
X-squared = 6.964, df = 1, p-value = 0.008317
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-28.82 -10.61 -1.63 12.01 36.85
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3762.6952 3.2736 1149.410 < 0.0000000000000002 ***
ID 0.6978 0.0711 9.815 0.00000000000000326 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 14.41 on 77 degrees of freedom
Multiple R-squared: 0.5558, Adjusted R-squared: 0.55
F-statistic: 96.33 on 1 and 77 DF, p-value: 0.000000000000003264
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 = 0.52171, p-value = 0.0000000000000003647
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.27656, df = 1, p-value = 0.599
Box-Ljung test
data: lm_residuals
X-squared = 41.848, df = 1, p-value = 0.00000000009865