Analysis
[1] "労働力調査(主要項目):労働力人口(万人):季節調整値:女:総務省"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1999 2747
2000 2749 2740 2731 2757 2750 2751 2754 2748 2751 2762 2768 2766
2001 2781 2787 2762 2749 2758 2755 2752 2744 2743 2751 2778 2765
2002 2743 2750 2743 2727 2724 2726 2723 2727 2732 2732 2742 2737
2003 2727 2718 2743 2732 2728 2756 2743 2728 2729 2727 2724 2737
2004 2731 2737 2751 2747 2728 2725 2742 2755 2745 2741 2715 2725
2005 2750 2743 2730 2747 2768 2744 2760 2754 2763 2761 2740 2738
2006 2758 2758 2743 2736 2754 2759 2760 2767 2773 2783 2782 2764
2007 2741 2757 2770 2774 2772 2770 2761 2765 2756 2769 2788 2794
2008 2773 2756 2774 2783 2774 2786 2775 2763 2758 2752 2775 2781
2009 2790 2792 2774 2777 2774 2776 2783 2800 2798 2773 2776 2776
2010 2783 2777 2779 2776 2781 2770 2779 2787 2801 2800 2780 2783
2011 2791 2790 2769 2762 2760 2771 2767 2757 2763 2771 2770 2768
2012 2759 2770 2766 2772 2769 2767 2775 2774 2764 2771 2774 2768
2013 2791 2803 2801 2813 2805 2784 2788 2808 2814 2828 2842 2834
2014 2814 2818 2820 2817 2833 2836 2836 2826 2835 2843 2849 2855
2015 2836 2849 2846 2838 2847 2857 2850 2852 2867 2861 2852 2866
2016 2897 2874 2872 2869 2870 2899 2909 2904 2900 2898 2899 2917
2017 2918 2902 2900 2917 2936 2945 2945 2951 2951 2948 2962 2971
2018 2974 3000 3022 3023 3006 2994 3005 3019 3022 3038 3038 3026
2019 3036 3045 3064 3050 3043 3041 3043 3051 3071 3088
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-15.845 -5.265 -1.023 3.682 23.637
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2782.2915 2.9613 939.549 < 0.0000000000000002 ***
ID -0.4107 0.1290 -3.183 0.00295 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 9.069 on 37 degrees of freedom
Multiple R-squared: 0.215, Adjusted R-squared: 0.1937
F-statistic: 10.13 on 1 and 37 DF, p-value: 0.002951
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 = 0.79953, p-value = 0.000005369
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 1.1044, df = 1, p-value = 0.2933
Box-Ljung test
data: lm_residuals
X-squared = 14.489, df = 1, p-value = 0.000141
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-42.799 -14.967 -0.135 16.222 40.022
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2763.25203 4.68174 590.22 <0.0000000000000002 ***
ID 3.52052 0.09799 35.93 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 21 on 80 degrees of freedom
Multiple R-squared: 0.9416, Adjusted R-squared: 0.9409
F-statistic: 1291 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 = 0.12195, p-value = 0.5785
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.31195, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.0029714, df = 1, p-value = 0.9565
Box-Ljung test
data: lm_residuals
X-squared = 56.8, df = 1, p-value = 0.00000000000004818
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-25.699 -6.913 -1.352 5.913 27.954
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2778.00526 3.18470 872.298 <0.0000000000000002 ***
ID -0.05102 0.09232 -0.553 0.583
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 12.08 on 57 degrees of freedom
Multiple R-squared: 0.00533, Adjusted R-squared: -0.01212
F-statistic: 0.3054 on 1 and 57 DF, p-value: 0.5826
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 = 0.66696, p-value = 0.0000000006354
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.44954, df = 1, p-value = 0.5026
Box-Ljung test
data: lm_residuals
X-squared = 24.231, df = 1, p-value = 0.0000008544
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-42.405 -14.103 0.727 13.722 43.771
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2769.3934 4.6685 593.21 <0.0000000000000002 ***
ID 3.6044 0.1014 35.55 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 20.55 on 77 degrees of freedom
Multiple R-squared: 0.9426, Adjusted R-squared: 0.9418
F-statistic: 1264 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 = 0.33322, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.14476, df = 1, p-value = 0.7036
Box-Ljung test
data: lm_residuals
X-squared = 51.35, df = 1, p-value = 0.0000000000007727