Analysis
[1] "機械受注統計調査:非製造業業種別受注額(季調系列・月次)(単位:億円):リース業:内閣府"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2005 132.41 120.20 117.57 119.82 138.02 128.12 79.64 113.62 120.59
2006 103.47 105.37 125.11 133.91 135.01 131.37 158.38 131.12 135.54 143.21 140.79 162.40
2007 163.34 143.25 168.95 152.53 169.69 130.90 106.72 165.44 124.61 164.26 145.74 133.92
2008 150.63 132.54 125.00 112.58 87.15 112.78 117.36 118.88 109.70 111.20 141.32 76.73
2009 83.70 99.40 70.88 70.99 90.46 50.81 55.09 129.29 102.65 72.85 75.73 127.85
2010 77.28 77.64 77.18 69.08 71.13 110.45 90.90 63.14 90.98 88.78 65.50 77.09
2011 91.86 98.01 102.75 94.75 96.05 93.07 102.75 73.72 71.47 59.00 95.02 119.66
2012 102.34 96.54 90.53 93.45 92.88 137.88 121.56 102.69 81.08 132.88 127.15 119.44
2013 114.38 109.86 106.00 137.78 149.81 92.65 136.83 105.51 137.75 179.88 154.15 95.92
2014 108.25 111.19 103.47 130.88 100.44 114.86 81.70 215.54 105.27 92.57 113.30 89.98
2015 119.20 110.19 122.51 98.64 188.84 117.87 128.93 112.73 90.41 126.13 113.76 117.28
2016 115.41 99.70 113.38 88.35 81.83 114.82 116.04 121.17 115.06 111.31 96.67 128.65
2017 100.19 130.41 132.83 144.94 116.73 107.41 127.95 143.05 220.83 95.45 123.75 96.55
2018 107.30 114.26 108.17 115.07 136.15 98.00 94.09 110.75 127.86 112.88 109.31 154.68
2019 157.29 100.90 122.77 126.08 180.79 108.40 140.23 107.70 150.34
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-38.642 -11.126 -1.161 7.700 50.057
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 75.0863 5.7502 13.058 0.000000000000002 ***
ID 0.9022 0.2506 3.601 0.000926 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 17.61 on 37 degrees of freedom
Multiple R-squared: 0.2595, Adjusted R-squared: 0.2395
F-statistic: 12.97 on 1 and 37 DF, p-value: 0.0009257
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.15385, p-value = 0.7523
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.6625, p-value = 0.1066
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.0016307, df = 1, p-value = 0.9678
Box-Ljung test
data: lm_residuals
X-squared = 1.1409, df = 1, p-value = 0.2855
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-38.663 -14.932 -5.812 9.204 99.109
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 117.34732 5.94760 19.730 <0.0000000000000002 ***
ID 0.07673 0.12601 0.609 0.544
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 26.52 on 79 degrees of freedom
Multiple R-squared: 0.004672, Adjusted R-squared: -0.007928
F-statistic: 0.3708 on 1 and 79 DF, p-value: 0.5443
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.16049, p-value = 0.2488
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 2.1041, p-value = 0.6388
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.096036, df = 1, p-value = 0.7566
Box-Ljung test
data: lm_residuals
X-squared = 0.28836, df = 1, p-value = 0.5913
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-40.083 -16.129 -2.084 14.878 52.118
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 87.5103 5.6172 15.579 <0.0000000000000002 ***
ID 0.2416 0.1628 1.484 0.143
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 21.3 on 57 degrees of freedom
Multiple R-squared: 0.03718, Adjusted R-squared: 0.02029
F-statistic: 2.201 on 1 and 57 DF, p-value: 0.1434
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.10169, p-value = 0.9239
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.3617, p-value = 0.00379
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 3.037, df = 1, p-value = 0.08139
Box-Ljung test
data: lm_residuals
X-squared = 6.3064, df = 1, p-value = 0.01203
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-38.983 -15.067 -6.292 8.997 99.151
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 118.75513 6.16887 19.251 <0.0000000000000002 ***
ID 0.05414 0.13568 0.399 0.691
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 26.98 on 76 degrees of freedom
Multiple R-squared: 0.002091, Adjusted R-squared: -0.01104
F-statistic: 0.1592 on 1 and 76 DF, p-value: 0.691
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.17949, p-value = 0.1624
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 2.0939, p-value = 0.6176
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.28027, df = 1, p-value = 0.5965
Box-Ljung test
data: lm_residuals
X-squared = 0.26318, df = 1, p-value = 0.6079