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):自動車・同付属品:内閣府"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2005 645.66 534.52 523.98 506.77 510.54 465.70 525.39 522.84 569.49
2006 513.05 490.32 564.78 519.35 469.57 656.49 492.01 448.32 511.11 450.01 481.04 475.25
2007 495.36 497.97 453.02 459.22 522.72 468.71 448.22 493.41 464.84 487.51 535.26 497.76
2008 520.82 545.22 546.71 469.91 465.61 449.87 467.64 403.13 447.93 338.26 237.74 214.11
2009 133.35 123.92 141.25 154.65 185.51 152.16 142.89 171.28 193.02 185.59 189.03 197.50
2010 210.44 224.34 247.97 255.97 257.58 252.08 264.95 230.65 259.20 253.54 285.09 298.56
2011 278.47 284.22 239.52 268.98 262.73 281.26 306.26 333.27 296.38 310.26 330.82 348.25
2012 374.35 354.61 351.76 320.03 357.64 323.73 324.60 321.10 301.70 320.83 336.49 288.98
2013 359.64 316.42 332.09 313.80 323.82 355.43 318.46 335.23 317.23 348.96 334.17 359.90
2014 348.68 381.84 392.00 360.14 356.76 369.13 340.75 348.12 371.31 348.48 343.11 398.44
2015 336.52 373.07 348.41 451.14 383.44 375.48 437.52 388.63 388.76 380.04 410.65 378.33
2016 381.19 369.39 385.04 382.48 380.25 345.76 354.07 366.84 408.38 406.84 420.94 405.81
2017 401.90 364.47 415.48 416.61 406.55 457.62 453.43 427.93 418.59 426.80 406.80 424.45
2018 463.66 466.44 458.66 472.97 467.35 459.94 440.46 495.52 383.35 491.20 454.79 487.56
2019 444.96 446.24 418.59 430.59 407.26 377.43 412.82 354.67 393.82
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-64.81 -17.78 -3.38 16.53 60.18
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 213.3113 8.6640 24.620 < 0.0000000000000002 ***
ID 3.6020 0.3775 9.541 0.0000000000162 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 26.53 on 37 degrees of freedom
Multiple R-squared: 0.711, Adjusted R-squared: 0.7032
F-statistic: 91.03 on 1 and 37 DF, p-value: 0.00000000001625
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 = 0.74628, p-value = 0.000001704
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 4.0234, df = 1, p-value = 0.04487
Box-Ljung test
data: lm_residuals
X-squared = 11.705, df = 1, p-value = 0.0006234
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-95.392 -17.666 1.183 12.612 76.357
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 334.248 6.937 48.18 < 0.0000000000000002 ***
ID 1.448 0.147 9.85 0.00000000000000216 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 30.93 on 79 degrees of freedom
Multiple R-squared: 0.5512, Adjusted R-squared: 0.5455
F-statistic: 97.02 on 1 and 79 DF, p-value: 0.000000000000002156
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.1358, p-value = 0.4462
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.3069, p-value = 0.0004066
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 11.208, df = 1, p-value = 0.0008145
Box-Ljung test
data: lm_residuals
X-squared = 8.6444, df = 1, p-value = 0.003281
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-131.251 -46.796 -8.281 20.766 221.910
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 242.4250 21.2335 11.417 0.000000000000000234 ***
ID 1.2746 0.6155 2.071 0.0429 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 80.51 on 57 degrees of freedom
Multiple R-squared: 0.06997, Adjusted R-squared: 0.05365
F-statistic: 4.288 on 1 and 57 DF, p-value: 0.04292
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 = 0.18195, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 23.521, df = 1, p-value = 0.000001235
Box-Ljung test
data: lm_residuals
X-squared = 44.004, df = 1, p-value = 0.00000000003278
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-95.337 -17.782 1.459 12.544 76.278
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 338.7348 7.1598 47.310 < 0.0000000000000002 ***
ID 1.4451 0.1575 9.177 0.0000000000000613 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 31.31 on 76 degrees of freedom
Multiple R-squared: 0.5256, Adjusted R-squared: 0.5194
F-statistic: 84.21 on 1 and 76 DF, p-value: 0.00000000000006133
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.12821, p-value = 0.546
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.2905, p-value = 0.0003743
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 10.572, df = 1, p-value = 0.001148
Box-Ljung test
data: lm_residuals
X-squared = 8.7066, df = 1, p-value = 0.003171