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:総務省"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1999 4.9
2000 4.9 5.0 5.1 5.0 4.8 4.9 4.9 4.8 4.9 4.9 5.0 5.0
2001 5.0 4.9 4.9 5.0 5.1 5.1 5.3 5.3 5.5 5.5 5.6 5.7
2002 5.5 5.6 5.6 5.5 5.7 5.7 5.6 5.8 5.7 5.6 5.5 5.6
2003 5.7 5.5 5.7 5.7 5.7 5.6 5.4 5.3 5.4 5.3 5.3 5.1
2004 5.1 5.2 5.0 5.0 4.9 4.9 5.1 5.0 4.8 4.8 4.7 4.7
2005 4.7 4.8 4.7 4.7 4.7 4.5 4.6 4.5 4.4 4.6 4.8 4.6
2006 4.6 4.3 4.3 4.3 4.3 4.4 4.3 4.3 4.3 4.3 4.2 4.2
2007 4.2 4.2 4.2 4.0 3.9 3.8 3.7 3.9 4.1 4.2 4.0 3.9
2008 4.1 4.2 4.0 4.1 4.1 4.1 4.1 4.2 4.2 4.0 4.2 4.6
2009 4.5 4.8 5.1 5.2 5.4 5.4 5.7 5.7 5.6 5.4 5.5 5.4
2010 5.3 5.3 5.4 5.3 5.4 5.4 5.2 5.3 5.4 5.4 5.3 5.1
2011 5.1 4.9 4.9 4.9 4.8 5.0 4.9 4.7 4.4 4.6 4.7 4.8
2012 4.8 4.7 4.7 4.8 4.6 4.5 4.6 4.4 4.5 4.4 4.3 4.4
2013 4.4 4.5 4.3 4.4 4.4 4.1 4.0 4.3 4.1 4.2 4.1 3.9
2014 3.8 3.8 3.8 3.8 3.7 3.9 3.9 3.6 3.7 3.7 3.6 3.5
2015 3.7 3.7 3.6 3.5 3.5 3.5 3.5 3.5 3.5 3.4 3.4 3.5
2016 3.4 3.4 3.4 3.4 3.3 3.3 3.2 3.3 3.1 3.1 3.2 3.2
2017 3.1 3.0 3.0 3.0 3.2 2.9 3.0 2.9 3.0 2.9 2.8 2.8
2018 2.6 2.6 2.7 2.6 2.3 2.6 2.6 2.6 2.5 2.6 2.6 2.5
2019 2.6 2.4 2.7 2.6 2.5 2.4 2.3 2.3 2.6 2.6
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.41404 -0.08322 0.01572 0.08364 0.25084
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.545209 0.041741 132.85 <0.0000000000000002 ***
ID -0.030466 0.001819 -16.75 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1278 on 37 degrees of freedom
Multiple R-squared: 0.8835, Adjusted R-squared: 0.8803
F-statistic: 280.6 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 = 0.20513, p-value = 0.3888
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.98291, p-value = 0.0001414
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.0068017, df = 1, p-value = 0.9343
Box-Ljung test
data: lm_residuals
X-squared = 10.358, df = 1, p-value = 0.001289
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.40196 -0.07278 -0.02247 0.07433 0.31214
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.2852755 0.0277242 154.57 <0.0000000000000002 ***
ID -0.0243587 0.0005803 -41.98 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1244 on 80 degrees of freedom
Multiple R-squared: 0.9566, Adjusted R-squared: 0.956
F-statistic: 1762 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 = 0.18293, p-value = 0.1288
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.1315, p-value = 0.000009666
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.16569, df = 1, p-value = 0.684
Box-Ljung test
data: lm_residuals
X-squared = 12.741, df = 1, p-value = 0.0003577
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.97433 -0.32877 0.01531 0.42292 0.77430
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.003507 0.122804 40.744 <0.0000000000000002 ***
ID -0.004863 0.003560 -1.366 0.177
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4657 on 57 degrees of freedom
Multiple R-squared: 0.0317, Adjusted R-squared: 0.01471
F-statistic: 1.866 on 1 and 57 DF, p-value: 0.1773
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.16949, p-value = 0.3674
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.10545, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 27.165, df = 1, p-value = 0.0000001868
Box-Ljung test
data: lm_residuals
X-squared = 51.112, df = 1, p-value = 0.0000000000008722
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.40655 -0.07033 -0.01806 0.06774 0.29921
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.1863681 0.0274986 152.24 <0.0000000000000002 ***
ID -0.0238681 0.0005972 -39.97 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.121 on 77 degrees of freedom
Multiple R-squared: 0.954, Adjusted R-squared: 0.9534
F-statistic: 1597 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 = 0.12658, p-value = 0.5543
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.1864, p-value = 0.00004523
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.44403, df = 1, p-value = 0.5052
Box-Ljung test
data: lm_residuals
X-squared = 9.6039, df = 1, p-value = 0.001942