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):通信業:内閣府"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2005 988.60 1183.32 977.90 991.34 1059.05 745.73 1099.21 1328.42 1053.74
2006 961.17 868.10 976.75 1014.00 979.35 1011.06 943.29 944.72 886.65 913.54 935.95 865.43
2007 897.14 852.85 848.23 707.88 802.33 796.37 822.10 741.43 920.34 805.43 924.48 699.55
2008 1087.44 1057.61 761.15 931.20 901.77 1010.89 876.41 950.57 772.09 722.19 707.06 792.75
2009 737.47 731.95 717.91 758.59 715.78 652.51 765.26 697.34 736.63 705.95 643.50 726.90
2010 601.84 623.02 739.82 823.78 609.57 764.14 686.62 681.19 699.50 767.09 716.87 650.91
2011 678.21 722.08 688.57 741.14 683.94 729.94 775.09 827.62 812.74 833.83 869.08 653.70
2012 801.71 962.91 726.09 800.73 888.73 834.70 808.96 740.94 756.33 765.27 767.37 711.04
2013 645.51 680.84 549.01 773.20 866.86 695.48 829.73 724.05 760.51 772.55 765.72 727.37
2014 805.90 676.52 783.42 739.42 836.13 685.08 705.68 623.66 937.10 605.30 570.84 561.10
2015 614.51 801.90 638.90 417.91 431.59 529.84 386.68 569.09 468.77 446.20 466.25 585.79
2016 530.56 606.81 512.02 483.84 503.67 480.76 704.21 539.67 485.53 541.37 565.59 549.76
2017 570.71 416.88 488.16 563.46 436.49 527.40 430.28 432.91 505.49 487.05 477.66 380.22
2018 365.05 442.77 481.77 417.63 408.90 389.85 498.05 442.23 459.56 458.70 440.91 491.78
2019 437.40 551.88 425.38 475.07 501.81 418.58 460.43 388.03 453.77
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-115.78 -49.89 -11.20 45.32 186.20
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 671.7495 22.3047 30.117 < 0.0000000000000002 ***
ID 3.6195 0.9719 3.724 0.000651 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 68.31 on 37 degrees of freedom
Multiple R-squared: 0.2726, Adjusted R-squared: 0.253
F-statistic: 13.87 on 1 and 37 DF, p-value: 0.000651
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 = 1.9097, p-value = 0.3238
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.61803, df = 1, p-value = 0.4318
Box-Ljung test
data: lm_residuals
X-squared = 0.0064651, df = 1, p-value = 0.9359
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-216.766 -56.201 4.047 50.733 289.572
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 740.1000 20.0372 36.94 <0.0000000000000002 ***
ID -4.4082 0.4245 -10.38 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 89.33 on 79 degrees of freedom
Multiple R-squared: 0.5771, Adjusted R-squared: 0.5718
F-statistic: 107.8 on 1 and 79 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 = 0.11111, p-value = 0.7027
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.4239, p-value = 0.002759
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 5.6833, df = 1, p-value = 0.01713
Box-Ljung test
data: lm_residuals
X-squared = 6.4772, df = 1, p-value = 0.01093
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-181.27 -55.95 -13.32 48.70 245.38
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 766.7446 23.5822 32.514 <0.0000000000000002 ***
ID -0.6181 0.6836 -0.904 0.37
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 89.42 on 57 degrees of freedom
Multiple R-squared: 0.01414, Adjusted R-squared: -0.003158
F-statistic: 0.8174 on 1 and 57 DF, p-value: 0.3697
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.11864, p-value = 0.8052
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.0088, p-value = 0.00001101
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.37727, df = 1, p-value = 0.5391
Box-Ljung test
data: lm_residuals
X-squared = 11.98, df = 1, p-value = 0.0005378
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-224.57 -58.55 7.36 45.02 278.52
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 743.7570 20.0210 37.15 <0.0000000000000002 ***
ID -4.7322 0.4403 -10.75 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 87.56 on 76 degrees of freedom
Multiple R-squared: 0.6031, Adjusted R-squared: 0.5979
F-statistic: 115.5 on 1 and 76 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 = 0.10256, p-value = 0.81
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.4204, p-value = 0.003027
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 3.5411, df = 1, p-value = 0.05986
Box-Ljung test
data: lm_residuals
X-squared = 6.5095, df = 1, p-value = 0.01073
