Analysis
[1] "労働力調査:完全失業率(%):季節調整値:男:15から64歳:45から54:総務省"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1999 3.2
2000 3.5 3.6 3.4 3.4 3.1 3.3 3.6 3.3 3.5 3.4 3.3 3.5
2001 3.4 3.1 3.5 3.5 3.3 3.7 3.6 3.6 3.6 4.2 4.8 4.4
2002 4.1 4.3 4.1 4.1 4.0 3.8 4.2 4.7 4.7 4.4 4.2 4.3
2003 4.2 4.0 3.8 4.1 4.4 4.2 3.8 4.1 4.4 3.9 3.5 3.3
2004 3.5 3.8 3.9 3.8 3.8 4.0 3.8 3.0 3.2 3.1 3.3 3.5
2005 3.3 3.2 3.3 3.1 3.1 3.2 3.1 3.3 2.9 3.3 3.3 3.0
2006 3.4 3.3 3.1 3.0 3.1 3.0 3.2 3.2 2.9 2.9 3.3 3.1
2007 2.9 2.9 2.9 2.8 2.7 2.7 2.7 2.8 3.0 3.1 2.8 2.8
2008 3.0 3.1 2.8 2.8 3.0 3.0 2.9 2.9 3.0 2.9 3.2 3.6
2009 3.1 3.5 3.8 4.0 4.2 3.6 4.0 4.2 4.0 4.2 4.0 3.9
2010 4.0 4.1 4.4 4.3 3.7 4.0 4.0 4.2 4.2 3.9 3.9 4.0
2011 4.1 3.6 3.6 3.9 3.7 4.0 3.8 3.4 3.5 3.6 3.5 3.4
2012 3.4 3.3 3.5 3.3 3.6 3.9 3.5 3.5 3.3 3.0 3.3 3.2
2013 3.2 3.7 3.6 3.5 3.4 3.0 3.3 3.5 3.3 3.7 3.3 2.9
2014 3.0 2.7 2.8 3.2 2.8 2.7 3.0 2.8 3.0 3.0 2.9 3.0
2015 3.0 2.8 2.9 2.5 2.7 3.0 3.0 2.9 2.8 2.6 2.8 2.9
2016 2.9 3.0 2.7 2.5 2.7 2.7 2.2 2.6 2.5 2.5 2.5 2.4
2017 2.3 2.5 2.4 2.5 2.8 2.4 2.4 2.4 2.4 2.2 2.4 2.4
2018 2.3 2.1 2.1 2.3 1.9 2.1 2.2 2.1 2.0 2.2 2.0 2.0
2019 1.6 1.8 2.3 2.3 2.1 2.1 2.3 2.0 1.9 2.2
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.33638 -0.14850 -0.01134 0.13898 0.48963
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.23671 0.06379 66.416 < 0.0000000000000002 ***
ID -0.02504 0.00278 -9.009 0.0000000000729 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1954 on 37 degrees of freedom
Multiple R-squared: 0.6868, Adjusted R-squared: 0.6784
F-statistic: 81.15 on 1 and 37 DF, p-value: 0.00000000007295
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 = 1.4967, p-value = 0.03702
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.0021354, df = 1, p-value = 0.9631
Box-Ljung test
data: lm_residuals
X-squared = 2.6363, df = 1, p-value = 0.1044
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.46732 -0.13358 0.02086 0.10324 0.51366
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.3639566 0.0437862 76.83 <0.0000000000000002 ***
ID -0.0177621 0.0009165 -19.38 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1964 on 80 degrees of freedom
Multiple R-squared: 0.8244, Adjusted R-squared: 0.8222
F-statistic: 375.6 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 = 0.085366, p-value = 0.9286
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.3897, p-value = 0.001551
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 1.6084, df = 1, p-value = 0.2047
Box-Ljung test
data: lm_residuals
X-squared = 7.0457, df = 1, p-value = 0.007945
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.76249 -0.28492 -0.03399 0.34680 0.74990
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.6643483 0.1075505 34.071 <0.0000000000000002 ***
ID -0.0006195 0.0031177 -0.199 0.843
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4078 on 57 degrees of freedom
Multiple R-squared: 0.0006922, Adjusted R-squared: -0.01684
F-statistic: 0.03949 on 1 and 57 DF, p-value: 0.8432
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 = 0.41148, p-value = 0.000000000000001678
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 15.815, df = 1, p-value = 0.00006985
Box-Ljung test
data: lm_residuals
X-squared = 36.885, df = 1, p-value = 0.000000001253
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.47590 -0.12311 0.02663 0.09770 0.53678
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.2840312 0.0435528 75.40 <0.0000000000000002 ***
ID -0.0172590 0.0009459 -18.25 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1917 on 77 degrees of freedom
Multiple R-squared: 0.8122, Adjusted R-squared: 0.8097
F-statistic: 332.9 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 = 0.10127, p-value = 0.8161
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.4163, p-value = 0.002715
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.84816, df = 1, p-value = 0.3571
Box-Ljung test
data: lm_residuals
X-squared = 5.9124, df = 1, p-value = 0.01504