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):情報通信機械:内閣府"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2005 309.66 261.27 248.13 216.98 212.71 248.72 241.28 298.78 303.11
2006 237.07 320.25 325.44 291.07 324.47 513.30 318.59 407.81 296.86 287.47 233.20 281.30
2007 355.89 285.84 292.46 285.82 247.48 209.81 291.22 254.43 224.73 252.38 289.06 270.49
2008 297.15 258.58 187.45 231.32 250.76 195.24 215.06 190.07 199.24 202.13 134.21 124.19
2009 119.92 100.29 112.54 104.93 111.27 140.34 102.71 105.10 147.11 271.38 126.82 124.25
2010 172.40 178.08 201.32 139.21 121.71 153.24 180.20 175.00 163.59 188.62 274.46 186.19
2011 141.51 180.73 149.97 164.04 213.99 149.59 150.66 265.54 158.66 121.95 184.73 153.29
2012 140.72 165.18 149.15 167.33 184.17 264.03 206.29 178.58 181.93 157.08 171.39 178.97
2013 143.81 117.84 146.26 128.48 166.70 156.55 124.50 159.65 231.28 125.53 160.63 162.97
2014 146.25 157.02 178.36 165.87 151.08 152.09 169.24 165.44 169.41 182.77 165.99 169.64
2015 164.05 205.14 230.98 184.36 176.58 187.00 199.17 161.57 169.85 165.93 187.91 237.07
2016 194.33 164.54 155.14 169.42 124.10 134.68 146.10 136.00 151.88 136.19 123.94 134.42
2017 133.55 148.43 123.89 149.11 199.26 147.52 165.69 169.87 145.26 188.47 170.21 178.90
2018 206.90 166.19 172.39 202.76 185.81 174.22 168.90 203.29 190.01 224.24 186.72 180.96
2019 112.10 142.69 195.61 131.85 294.18 108.52 146.42 167.06 189.30
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-54.325 -24.966 -7.471 7.582 100.373
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 171.3029 12.8778 13.302 0.00000000000000113 ***
ID 0.1989 0.5611 0.354 0.725
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 39.44 on 37 degrees of freedom
Multiple R-squared: 0.003384, Adjusted R-squared: -0.02355
F-statistic: 0.1256 on 1 and 37 DF, p-value: 0.725
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.28205, p-value = 0.08974
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.7528, p-value = 0.1698
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 1.3137, df = 1, p-value = 0.2517
Box-Ljung test
data: lm_residuals
X-squared = 0.057424, df = 1, p-value = 0.8106
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-66.352 -20.307 0.438 13.786 119.527
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 157.7824 6.9302 22.767 <0.0000000000000002 ***
ID 0.2191 0.1468 1.492 0.14
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 30.9 on 79 degrees of freedom
Multiple R-squared: 0.02741, Adjusted R-squared: 0.0151
F-statistic: 2.227 on 1 and 79 DF, p-value: 0.1396
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.11111, p-value = 0.7027
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.7774, p-value = 0.1305
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 3.0434, df = 1, p-value = 0.08107
Box-Ljung test
data: lm_residuals
X-squared = 0.99295, df = 1, p-value = 0.319
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-61.695 -27.484 -4.184 17.838 108.171
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 159.9349 11.2941 14.161 <0.0000000000000002 ***
ID 0.2050 0.3274 0.626 0.534
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 42.83 on 57 degrees of freedom
Multiple R-squared: 0.00683, Adjusted R-squared: -0.01059
F-statistic: 0.392 on 1 and 57 DF, p-value: 0.5338
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.18644, p-value = 0.2582
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.2688, p-value = 0.001088
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 2.3619, df = 1, p-value = 0.1243
Box-Ljung test
data: lm_residuals
X-squared = 6.4799, df = 1, p-value = 0.01091
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-64.779 -20.555 -0.305 15.300 121.032
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 161.9982 7.0959 22.830 <0.0000000000000002 ***
ID 0.1507 0.1561 0.965 0.337
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 31.03 on 76 degrees of freedom
Multiple R-squared: 0.01211, Adjusted R-squared: -0.0008837
F-statistic: 0.932 on 1 and 76 DF, p-value: 0.3374
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 = 0.81
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.8068, p-value = 0.1645
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 3.0379, df = 1, p-value = 0.08134
Box-Ljung test
data: lm_residuals
X-squared = 0.61619, df = 1, p-value = 0.4325