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):運輸業・郵便業:内閣府"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2005 468.65 532.28 459.00 595.96 667.98 738.60 761.16 837.94 645.42
2006 633.13 666.47 695.93 829.98 947.62 1015.52 916.27 684.47 767.31 611.63 722.15 755.44
2007 744.14 780.30 663.81 580.36 647.03 470.88 801.43 968.75 652.59 943.65 958.90 964.05
2008 1558.00 729.44 815.81 859.61 1303.73 1104.26 733.37 565.11 736.45 665.22 627.02 681.14
2009 720.24 791.02 1163.96 754.61 507.97 554.67 525.02 610.66 633.79 536.74 532.66 729.01
2010 525.04 479.47 696.97 775.94 621.98 545.01 543.38 679.38 507.15 683.49 500.03 477.38
2011 608.92 563.88 554.35 431.53 419.53 744.05 790.53 541.58 677.52 494.83 1009.16 566.82
2012 509.96 583.56 445.22 486.94 493.43 557.02 619.42 672.70 702.73 639.09 718.07 546.60
2013 683.58 572.04 850.67 553.92 972.80 653.88 582.30 756.25 641.49 661.69 774.30 552.56
2014 724.90 1058.29 794.26 1028.73 776.30 609.37 712.52 721.14 751.56 1036.81 796.15 858.88
2015 968.61 663.59 808.36 1183.34 757.66 706.81 673.21 572.63 745.07 1517.49 823.32 829.97
2016 765.21 954.22 854.74 905.63 1043.16 1311.43 1056.49 1274.78 1415.19 944.99 790.79 1080.19
2017 797.17 962.06 764.01 844.55 691.32 755.47 1136.43 768.93 751.03 879.67 886.42 909.57
2018 997.96 850.52 924.25 716.49 837.58 872.51 997.09 1776.39 719.25 746.42 932.62 950.45
2019 713.33 1048.95 1208.54 1317.88 923.94 1768.81 958.30 1108.04 1106.87
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-175.63 -86.60 -39.54 84.87 410.21
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 582.5210 39.4369 14.771 <0.0000000000000002 ***
ID 0.6317 1.7184 0.368 0.715
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 120.8 on 37 degrees of freedom
Multiple R-squared: 0.003639, Adjusted R-squared: -0.02329
F-statistic: 0.1351 on 1 and 37 DF, p-value: 0.7153
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 = 0.9885
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.9934, p-value = 0.4234
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.12549, df = 1, p-value = 0.7232
Box-Ljung test
data: lm_residuals
X-squared = 0.00018303, df = 1, p-value = 0.9892
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-326.18 -135.12 -51.09 42.56 759.65
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 706.999 49.410 14.309 < 0.0000000000000002 ***
ID 4.555 1.047 4.351 0.0000401 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 220.3 on 79 degrees of freedom
Multiple R-squared: 0.1933, Adjusted R-squared: 0.1831
F-statistic: 18.93 on 1 and 79 DF, p-value: 0.00004011
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.18519, p-value = 0.1245
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.9181, p-value = 0.3137
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 3.0148, df = 1, p-value = 0.08251
Box-Ljung test
data: lm_residuals
X-squared = 0.13954, df = 1, p-value = 0.7087
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-203.57 -112.13 -28.53 61.62 573.16
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 733.556 43.567 16.837 <0.0000000000000002 ***
ID -2.985 1.263 -2.364 0.0215 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 165.2 on 57 degrees of freedom
Multiple R-squared: 0.08928, Adjusted R-squared: 0.0733
F-statistic: 5.588 on 1 and 57 DF, p-value: 0.02152
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 = 1.1709, p-value = 0.0002376
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 3.1305, df = 1, p-value = 0.07684
Box-Ljung test
data: lm_residuals
X-squared = 4.9104, df = 1, p-value = 0.0267
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-325.29 -134.23 -60.97 40.33 760.31
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 723.062 51.095 14.151 < 0.0000000000000002 ***
ID 4.508 1.124 4.011 0.00014 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 223.5 on 76 degrees of freedom
Multiple R-squared: 0.1747, Adjusted R-squared: 0.1639
F-statistic: 16.09 on 1 and 76 DF, p-value: 0.0001401
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.16667, p-value = 0.2297
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.8903, p-value = 0.2728
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 2.5485, df = 1, p-value = 0.1104
Box-Ljung test
data: lm_residuals
X-squared = 0.20876, df = 1, p-value = 0.6477