Analysis
[1] "一般職業紹介状況:新規求人倍率:正社員:季節調整値:厚生労働省"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2004 0.84 0.85
2005 0.87 0.90 0.89 0.89 0.88 0.88 0.89 0.87 0.89 0.89 0.90 0.95
2006 0.95 0.94 0.91 0.93 0.95 0.94 0.92 0.91 0.91 0.92 0.93 0.95
2007 0.90 0.91 0.92 0.91 0.91 0.90 0.89 0.89 0.87 0.88 0.89 0.89
2008 0.89 0.88 0.86 0.86 0.84 0.80 0.78 0.78 0.72 0.70 0.66 0.57
2009 0.52 0.46 0.46 0.46 0.45 0.45 0.44 0.45 0.46 0.45 0.44 0.44
2010 0.46 0.48 0.47 0.49 0.50 0.51 0.51 0.52 0.54 0.57 0.56 0.57
2011 0.60 0.60 0.58 0.59 0.58 0.59 0.64 0.63 0.67 0.68 0.71 0.70
2012 0.72 0.74 0.72 0.75 0.78 0.78 0.78 0.79 0.76 0.77 0.78 0.78
2013 0.80 0.82 0.81 0.83 0.84 0.86 0.86 0.89 0.88 0.93 0.93 0.94
2014 0.97 1.00 0.97 0.98 0.98 0.99 1.00 0.99 1.00 1.04 1.02 1.06
2015 1.07 1.04 1.07 1.09 1.08 1.09 1.12 1.11 1.14 1.13 1.16 1.18
2016 1.25 1.20 1.22 1.27 1.27 1.25 1.26 1.28 1.31 1.30 1.34 1.35
2017 1.35 1.37 1.38 1.40 1.46 1.45 1.43 1.45 1.47 1.51 1.53 1.57
2018 1.52 1.56 1.58 1.59 1.61 1.65 1.59 1.61 1.67 1.62 1.64 1.65
2019 1.64 1.69 1.69 1.68 1.64 1.64 1.62 1.68 1.61 1.68
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.041389 -0.010263 0.000005 0.017162 0.037430
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.4191363 0.0064368 65.12 <0.0000000000000002 ***
ID 0.0101073 0.0002805 36.04 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.01971 on 37 degrees of freedom
Multiple R-squared: 0.9723, Adjusted R-squared: 0.9715
F-statistic: 1299 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 = 0.10256, p-value = 0.9885
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.80055, p-value = 0.000005482
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 7.2129, df = 1, p-value = 0.007238
Box-Ljung test
data: lm_residuals
X-squared = 12.551, df = 1, p-value = 0.0003961
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.128261 -0.026099 0.000497 0.018456 0.089282
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.7795303 0.0084185 92.60 <0.0000000000000002 ***
ID 0.0118362 0.0001762 67.17 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.03777 on 80 degrees of freedom
Multiple R-squared: 0.9826, Adjusted R-squared: 0.9824
F-statistic: 4512 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 = 0.13415, p-value = 0.4541
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.58464, p-value = 0.000000000000006618
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 15.344, df = 1, p-value = 0.00008961
Box-Ljung test
data: lm_residuals
X-squared = 39.998, df = 1, p-value = 0.0000000002543
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.14272 -0.09477 -0.02999 0.06058 0.33068
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.5054588 0.0303745 16.641 < 0.0000000000000002 ***
ID 0.0038632 0.0008805 4.387 0.0000501 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1152 on 57 degrees of freedom
Multiple R-squared: 0.2525, Adjusted R-squared: 0.2393
F-statistic: 19.25 on 1 and 57 DF, p-value: 0.00005012
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.13559, p-value = 0.6544
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.051046, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 20.116, df = 1, p-value = 0.000007287
Box-Ljung test
data: lm_residuals
X-squared = 50.071, df = 1, p-value = 0.000000000001483
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.128789 -0.025733 0.000496 0.019557 0.089064
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.8139533 0.0087299 93.24 <0.0000000000000002 ***
ID 0.0118569 0.0001896 62.54 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.03843 on 77 degrees of freedom
Multiple R-squared: 0.9807, Adjusted R-squared: 0.9804
F-statistic: 3911 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 = 0.16456, p-value = 0.2361
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.58138, p-value = 0.00000000000001568
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 14.186, df = 1, p-value = 0.0001656
Box-Ljung test
data: lm_residuals
X-squared = 38.752, df = 1, p-value = 0.0000000004812