Analysis
[1] "一般職業紹介状況:新規求人倍率(パートタイム)【季節調整値】:厚生労働省"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1999 1.84
2000 1.91 1.94 1.98 1.99 2.06 2.18 2.19 2.17 2.30 2.34 2.38 2.39
2001 2.34 2.25 2.17 2.22 2.25 2.20 2.23 2.05 1.99 1.89 1.94 1.88
2002 1.83 1.88 1.93 1.94 1.98 2.00 2.01 2.02 1.95 1.89 1.93 1.94
2003 1.99 1.96 2.00 2.05 2.02 2.05 2.11 2.20 2.21 2.29 2.22 2.13
2004 2.17 2.17 2.22 2.23 2.08 1.89 1.82 1.74 1.83 1.96 1.83 1.79
2005 1.89 2.00 1.92 1.81 1.91 2.01 1.99 2.00 2.05 1.96 2.04 2.09
2006 2.10 2.13 2.01 1.93 2.17 2.10 2.08 2.10 2.08 2.07 2.13 2.11
2007 1.99 2.06 2.12 2.07 2.08 2.10 2.07 2.06 1.98 1.97 1.97 1.94
2008 1.97 1.97 1.89 1.86 1.85 1.77 1.77 1.78 1.70 1.60 1.53 1.51
2009 1.45 1.25 1.28 1.24 1.24 1.21 1.19 1.19 1.22 1.22 1.20 1.19
2010 1.21 1.24 1.24 1.29 1.27 1.29 1.28 1.31 1.34 1.36 1.34 1.33
2011 1.39 1.38 1.41 1.25 1.33 1.35 1.43 1.39 1.54 1.50 1.55 1.63
2012 1.63 1.65 1.67 1.69 1.78 1.76 1.77 1.80 1.75 1.78 1.82 1.82
2013 1.85 1.89 1.92 1.94 1.92 2.03 1.99 2.00 1.96 2.09 2.07 2.03
2014 2.12 2.25 2.19 2.18 2.15 2.17 2.17 2.17 2.15 2.18 2.20 2.33
2015 2.30 2.27 2.32 2.31 2.34 2.38 2.42 2.45 2.49 2.46 2.51 2.50
2016 2.70 2.57 2.57 2.73 2.72 2.63 2.63 2.75 2.75 2.70 2.72 2.77
2017 2.74 2.78 2.70 2.74 2.91 2.78 2.80 2.77 2.77 2.92 2.85 2.87
2018 2.92 2.83 2.87 2.84 2.83 2.92 2.89 2.84 2.94 2.86 2.83 2.84
2019 2.94 2.94 2.82 2.98 2.93 2.79 2.69 2.90 2.69 2.90
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.19839 -0.02012 0.01696 0.03134 0.10074
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.1109717 0.0209221 53.10 <0.0000000000000002 ***
ID 0.0177591 0.0009117 19.48 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.06408 on 37 degrees of freedom
Multiple R-squared: 0.9112, Adjusted R-squared: 0.9088
F-statistic: 379.5 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 = 0.6074, p-value = 0.00000004823
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.018493, df = 1, p-value = 0.8918
Box-Ljung test
data: lm_residuals
X-squared = 18.776, df = 1, p-value = 0.0000147
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.37222 -0.07612 -0.00252 0.07722 0.22927
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.9733333 0.0255183 77.33 <0.0000000000000002 ***
ID 0.0134430 0.0005341 25.17 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1145 on 80 degrees of freedom
Multiple R-squared: 0.8879, Adjusted R-squared: 0.8865
F-statistic: 633.4 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 = 0.097561, p-value = 0.8332
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.51454, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 7.6422, df = 1, p-value = 0.005702
Box-Ljung test
data: lm_residuals
X-squared = 43.972, df = 1, p-value = 0.0000000000333
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.2726 -0.1725 -0.1064 0.1580 0.5464
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.297387 0.055492 23.380 < 0.0000000000000002 ***
ID 0.006257 0.001609 3.889 0.000265 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2104 on 57 degrees of freedom
Multiple R-squared: 0.2097, Adjusted R-squared: 0.1959
F-statistic: 15.13 on 1 and 57 DF, p-value: 0.0002652
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.22034, p-value = 0.1141
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.072471, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 14.005, df = 1, p-value = 0.0001823
Box-Ljung test
data: lm_residuals
X-squared = 49.361, df = 1, p-value = 0.00000000000213
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.36357 -0.06880 0.00116 0.07428 0.22328
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.031616 0.025923 78.37 <0.0000000000000002 ***
ID 0.013102 0.000563 23.27 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1141 on 77 degrees of freedom
Multiple R-squared: 0.8755, Adjusted R-squared: 0.8739
F-statistic: 541.6 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 = 0.53701, p-value = 0.000000000000001004
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 6.5501, df = 1, p-value = 0.01049
Box-Ljung test
data: lm_residuals
X-squared = 41.609, df = 1, p-value = 0.0000000001115