Analysis
[1] "一般職業紹介状況:有効求人倍率(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 1.17
2000 1.21 1.25 1.29 1.32 1.35 1.39 1.43 1.46 1.49 1.53 1.57 1.59
2001 1.56 1.54 1.50 1.49 1.48 1.47 1.44 1.41 1.37 1.30 1.28 1.25
2002 1.24 1.25 1.28 1.29 1.31 1.33 1.36 1.37 1.37 1.36 1.36 1.36
2003 1.38 1.39 1.39 1.39 1.40 1.40 1.43 1.48 1.53 1.58 1.61 1.64
2004 1.63 1.61 1.60 1.58 1.57 1.52 1.46 1.39 1.34 1.36 1.37 1.34
2005 1.31 1.31 1.34 1.33 1.33 1.34 1.36 1.38 1.37 1.40 1.41 1.43
2006 1.45 1.45 1.45 1.44 1.45 1.46 1.47 1.46 1.46 1.46 1.46 1.47
2007 1.44 1.43 1.44 1.47 1.47 1.47 1.45 1.44 1.42 1.39 1.34 1.34
2008 1.34 1.33 1.33 1.32 1.30 1.28 1.25 1.22 1.18 1.14 1.10 1.07
2009 1.01 0.91 0.85 0.80 0.76 0.74 0.72 0.70 0.71 0.71 0.71 0.70
2010 0.71 0.72 0.73 0.76 0.77 0.79 0.80 0.81 0.82 0.84 0.85 0.84
2011 0.85 0.87 0.88 0.86 0.83 0.84 0.87 0.89 0.91 0.93 0.95 0.97
2012 0.99 1.01 1.04 1.06 1.07 1.09 1.09 1.10 1.11 1.11 1.12 1.13
2013 1.15 1.16 1.18 1.20 1.21 1.24 1.25 1.26 1.26 1.29 1.31 1.33
2014 1.33 1.35 1.38 1.38 1.39 1.40 1.39 1.39 1.39 1.40 1.41 1.44
2015 1.45 1.46 1.47 1.47 1.49 1.50 1.53 1.56 1.57 1.59 1.60 1.62
2016 1.63 1.65 1.66 1.68 1.71 1.71 1.71 1.72 1.73 1.73 1.73 1.74
2017 1.74 1.76 1.76 1.77 1.78 1.79 1.79 1.79 1.78 1.80 1.81 1.83
2018 1.83 1.82 1.82 1.81 1.81 1.81 1.82 1.82 1.83 1.81 1.81 1.80
2019 1.79 1.80 1.79 1.80 1.80 1.79 1.75 1.75 1.73 1.72
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.072532 -0.005480 0.005731 0.020389 0.036310
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.6655061 0.0090014 73.93 <0.0000000000000002 ***
ID 0.0117632 0.0003922 29.99 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.02757 on 37 degrees of freedom
Multiple R-squared: 0.9605, Adjusted R-squared: 0.9594
F-statistic: 899.4 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 = 0.25641, p-value = 0.1547
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.21256, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.35663, df = 1, p-value = 0.5504
Box-Ljung test
data: lm_residuals
X-squared = 32.15, df = 1, p-value = 0.00000001427
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.217820 -0.043313 -0.002457 0.068545 0.115282
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.2516170 0.0170601 73.36 <0.0000000000000002 ***
ID 0.0083683 0.0003571 23.43 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.07654 on 80 degrees of freedom
Multiple R-squared: 0.8729, Adjusted R-squared: 0.8713
F-statistic: 549.2 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 = 0.20732, p-value = 0.05871
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.029089, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 11.93, df = 1, p-value = 0.0005522
Box-Ljung test
data: lm_residuals
X-squared = 72.277, df = 1, p-value < 0.00000000000000022
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.2193 -0.1285 -0.0522 0.1230 0.4168
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.881303 0.045336 19.439 <0.0000000000000002 ***
ID 0.001900 0.001314 1.446 0.154
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1719 on 57 degrees of freedom
Multiple R-squared: 0.03537, Adjusted R-squared: 0.01845
F-statistic: 2.09 on 1 and 57 DF, p-value: 0.1537
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 = 0.025329, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 20.201, df = 1, p-value = 0.00000697
Box-Ljung test
data: lm_residuals
X-squared = 53.143, df = 1, p-value = 0.0000000000003101
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-0.209532 -0.047024 -0.009944 0.069727 0.110664
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.2933009 0.0169786 76.17 <0.0000000000000002 ***
ID 0.0080536 0.0003688 21.84 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.07474 on 77 degrees of freedom
Multiple R-squared: 0.861, Adjusted R-squared: 0.8592
F-statistic: 477 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 = 0.13924, p-value = 0.4302
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.030883, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 11.768, df = 1, p-value = 0.0006024
Box-Ljung test
data: lm_residuals
X-squared = 69.676, df = 1, p-value < 0.00000000000000022