Analysis
[1] "機械受注統計調査:製造業業種別受注額(季調系列・月次)(単位:億円):金属製品:内閣府"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2005 76.69 77.75 77.78 69.72 86.69 83.48 90.42 80.63 90.07
2006 70.62 70.24 65.21 85.92 83.40 80.74 87.71 83.38 89.19 77.47 80.22 58.96
2007 86.83 87.94 83.98 89.17 81.19 78.02 85.36 80.59 67.68 89.87 81.67 82.32
2008 77.01 103.60 87.68 80.05 80.65 74.07 74.78 62.91 62.08 54.65 53.11 46.11
2009 28.87 34.90 37.83 31.49 34.18 35.65 30.37 37.63 41.74 40.66 68.42 48.20
2010 63.86 41.16 49.23 47.17 48.24 55.60 54.60 53.51 52.49 44.15 48.43 44.85
2011 61.64 59.80 55.24 59.62 67.79 65.28 64.31 67.03 69.33 67.59 69.88 68.97
2012 69.35 63.48 77.38 79.23 60.28 64.75 63.45 59.77 58.56 66.35 59.02 70.48
2013 58.20 74.88 70.26 56.70 66.91 62.03 65.54 74.52 83.42 66.61 73.12 68.68
2014 69.61 69.51 64.83 75.58 62.74 64.47 76.33 72.56 81.61 88.36 85.90 86.33
2015 77.92 88.27 80.52 82.41 122.42 105.31 97.85 88.06 65.10 78.59 94.83 91.25
2016 90.28 64.56 118.70 66.93 59.76 80.32 174.20 83.29 78.72 108.76 66.45 62.97
2017 95.53 88.13 85.02 92.20 85.64 88.87 79.15 74.81 100.58 80.87 92.09 109.72
2018 81.18 110.72 104.94 125.25 113.54 91.53 100.16 120.53 104.22 79.32 113.67 106.31
2019 72.08 104.38 103.09 107.06 79.41 104.06 89.56 101.52 129.79
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-12.162 -5.243 -1.119 5.226 18.453
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 48.8833 2.4859 19.664 < 0.0000000000000002 ***
ID 0.5419 0.1083 5.003 0.000014 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.614 on 37 degrees of freedom
Multiple R-squared: 0.4035, Adjusted R-squared: 0.3874
F-statistic: 25.03 on 1 and 37 DF, p-value: 0.00001398
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 = 0.5622
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.6045, p-value = 0.076
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 1.0636, df = 1, p-value = 0.3024
Box-Ljung test
data: lm_residuals
X-squared = 1.3602, df = 1, p-value = 0.2435
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-30.194 -8.628 -0.463 6.435 86.082
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 67.82692 3.78179 17.935 < 0.0000000000000002 ***
ID 0.47188 0.08013 5.889 0.00000009 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 16.86 on 79 degrees of freedom
Multiple R-squared: 0.3051, Adjusted R-squared: 0.2963
F-statistic: 34.68 on 1 and 79 DF, p-value: 0.00000009001
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.20988, p-value = 0.05619
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.9792, p-value = 0.4172
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.43118, df = 1, p-value = 0.5114
Box-Ljung test
data: lm_residuals
X-squared = 0.0016524, df = 1, p-value = 0.9676
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-20.987 -7.992 0.223 6.798 34.410
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 45.87477 3.13466 14.635 < 0.0000000000000002 ***
ID 0.36548 0.09087 4.022 0.000172 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 11.89 on 57 degrees of freedom
Multiple R-squared: 0.2211, Adjusted R-squared: 0.2074
F-statistic: 16.18 on 1 and 57 DF, p-value: 0.0001717
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.084746, p-value = 0.9854
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.58493, p-value = 0.00000000002187
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 18.071, df = 1, p-value = 0.00002129
Box-Ljung test
data: lm_residuals
X-squared = 24.905, df = 1, p-value = 0.0000006024
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-30.131 -8.441 -0.536 6.903 86.046
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 69.41202 3.91822 17.715 < 0.0000000000000002 ***
ID 0.46856 0.08618 5.437 0.000000632 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 17.14 on 76 degrees of freedom
Multiple R-squared: 0.28, Adjusted R-squared: 0.2706
F-statistic: 29.56 on 1 and 76 DF, p-value: 0.0000006323
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 = 0.316
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.97, p-value = 0.4011
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.26854, df = 1, p-value = 0.6043
Box-Ljung test
data: lm_residuals
X-squared = 0.00020913, df = 1, p-value = 0.9885