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:35から44:総務省"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1999 3.3
2000 3.1 3.5 3.4 3.3 3.6 3.4 3.1 3.1 3.0 3.1 3.3 3.3
2001 3.4 3.4 3.2 3.3 3.2 3.4 3.7 4.1 4.1 4.0 3.9 4.0
2002 3.8 3.8 4.1 3.8 3.9 4.2 4.3 4.0 4.2 4.3 4.2 4.5
2003 4.6 4.1 4.3 4.3 4.3 4.1 4.1 3.9 4.0 3.9 4.1 3.7
2004 3.9 4.2 3.8 4.2 4.2 3.8 3.6 3.9 3.7 3.8 3.8 4.0
2005 3.7 3.9 3.8 3.7 3.7 3.8 4.1 3.8 3.8 3.9 3.9 3.5
2006 3.6 3.5 3.4 3.2 3.3 3.4 3.6 3.6 3.4 3.3 3.4 3.6
2007 3.4 3.2 3.6 3.6 3.1 3.0 3.1 3.3 3.8 3.7 3.2 3.2
2008 3.6 3.5 3.3 3.3 3.4 3.6 3.3 3.4 3.3 3.5 3.6 3.9
2009 4.0 4.3 4.4 4.6 4.9 4.8 5.0 4.7 4.4 4.5 4.9 4.7
2010 4.7 4.5 4.6 4.6 4.7 4.6 4.4 4.6 4.8 4.6 4.6 4.5
2011 4.3 4.4 4.3 4.1 4.2 4.5 4.2 3.9 3.8 4.0 4.1 4.1
2012 3.9 3.9 3.9 4.1 4.0 4.0 4.3 4.0 4.2 4.1 4.1 4.0
2013 4.2 4.2 3.9 3.8 3.9 3.5 3.5 4.0 3.7 3.7 3.4 3.7
2014 3.6 3.5 3.5 3.4 3.3 3.4 3.4 3.2 3.3 3.3 3.2 3.2
2015 3.2 3.1 3.2 3.2 3.1 3.1 3.1 3.1 3.1 3.0 3.0 2.7
2016 3.0 3.0 3.0 3.0 3.1 3.1 2.9 3.1 2.7 2.6 2.7 3.0
2017 2.9 2.7 2.7 2.7 2.6 2.5 2.6 2.5 2.8 2.6 2.5 2.4
2018 2.1 2.4 2.3 2.3 2.2 2.2 2.2 2.0 2.1 2.3 2.4 2.4
2019 2.5 2.2 2.3 2.2 2.3 2.1 2.1 2.1 2.1 1.9
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.41393 -0.10759 0.02026 0.12531 0.32785
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.730364 0.057127 82.804 < 0.0000000000000002 ***
ID -0.021518 0.002489 -8.644 0.000000000208 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.175 on 37 degrees of freedom
Multiple R-squared: 0.6688, Adjusted R-squared: 0.6599
F-statistic: 74.72 on 1 and 37 DF, p-value: 0.0000000002079
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.23077, p-value = 0.2523
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.0649, p-value = 0.0004645
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.76447, df = 1, p-value = 0.3819
Box-Ljung test
data: lm_residuals
X-squared = 8.2611, df = 1, p-value = 0.00405
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.35812 -0.08852 -0.01078 0.07533 0.39384
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.8518519 0.0340465 113.14 <0.0000000000000002 ***
ID -0.0228480 0.0007126 -32.06 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1527 on 80 degrees of freedom
Multiple R-squared: 0.9278, Adjusted R-squared: 0.9269
F-statistic: 1028 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 = 0.10976, p-value = 0.7099
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.1611, p-value = 0.00001932
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.79018, df = 1, p-value = 0.374
Box-Ljung test
data: lm_residuals
X-squared = 12.339, df = 1, p-value = 0.0004435
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.91444 -0.22933 -0.02477 0.37781 0.78252
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.2129164 0.1110112 37.950 <0.0000000000000002 ***
ID 0.0003039 0.0032181 0.094 0.925
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4209 on 57 degrees of freedom
Multiple R-squared: 0.0001565, Adjusted R-squared: -0.01738
F-statistic: 0.008919 on 1 and 57 DF, p-value: 0.9251
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 = 0.20889, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 25.76, df = 1, p-value = 0.0000003866
Box-Ljung test
data: lm_residuals
X-squared = 45.616, df = 1, p-value = 0.00000000001438
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.36291 -0.09095 0.00019 0.07354 0.37310
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.7367089 0.0318966 117.2 <0.0000000000000002 ***
ID -0.0219620 0.0006928 -31.7 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1404 on 77 degrees of freedom
Multiple R-squared: 0.9288, Adjusted R-squared: 0.9279
F-statistic: 1005 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 = 1.373, p-value = 0.001402
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.2497, df = 1, p-value = 0.6173
Box-Ljung test
data: lm_residuals
X-squared = 7.7684, df = 1, p-value = 0.005317