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):鉄鋼業:内閣府"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2005 168.39 186.16 107.98 151.66 121.95 147.02 104.11 173.92 147.63
2006 135.15 167.20 100.41 150.37 98.45 422.49 130.01 139.93 142.96 144.30 152.04 119.14
2007 116.86 113.32 200.03 202.60 210.06 203.57 195.32 154.93 211.94 213.12 212.85 158.97
2008 539.63 192.00 164.32 171.57 376.67 185.25 235.84 194.63 122.16 206.74 118.85 513.31
2009 136.91 163.31 74.26 105.03 76.19 114.82 79.50 97.89 104.22 91.69 72.69 149.87
2010 181.04 85.78 122.02 81.49 101.34 132.91 98.36 189.00 107.17 89.35 103.18 87.03
2011 83.55 92.60 157.49 105.40 108.50 112.89 81.86 104.95 130.70 123.02 136.69 139.65
2012 92.19 93.42 129.55 89.88 85.35 91.84 191.47 75.23 98.04 74.58 109.21 78.36
2013 71.10 93.75 84.75 94.08 80.74 72.46 97.47 99.85 99.96 86.85 95.16 80.12
2014 83.10 97.72 89.11 106.16 83.18 84.13 94.66 104.47 94.38 111.66 82.77 82.24
2015 88.00 101.05 97.97 100.81 1003.84 111.76 176.67 90.90 93.97 173.03 104.22 126.53
2016 1458.52 90.20 122.95 120.98 108.24 133.08 235.84 83.90 108.64 120.52 137.46 115.38
2017 114.74 107.19 115.83 112.38 110.15 131.33 104.90 128.29 121.83 114.90 117.39 129.70
2018 94.31 171.14 116.63 148.12 144.65 111.32 103.89 165.68 138.03 87.54 125.51 145.03
2019 109.87 97.64 134.43 139.14 120.62 118.07 144.84 123.48 131.68
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-43.499 -23.691 -8.701 17.161 86.770
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 116.9073 10.2006 11.461 0.0000000000000983 ***
ID -0.3590 0.4445 -0.808 0.424
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 31.24 on 37 degrees of freedom
Multiple R-squared: 0.01733, Adjusted R-squared: -0.00923
F-statistic: 0.6525 on 1 and 37 DF, p-value: 0.4244
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 = 0.9114
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 2.1848, p-value = 0.6596
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.27575, df = 1, p-value = 0.5995
Box-Ljung test
data: lm_residuals
X-squared = 0.50282, df = 1, p-value = 0.4783
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-59.86 -42.28 -31.94 -16.77 1319.26
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 130.7304 40.6867 3.213 0.0019 **
ID 0.2305 0.8620 0.267 0.7899
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 181.4 on 79 degrees of freedom
Multiple R-squared: 0.000904, Adjusted R-squared: -0.01174
F-statistic: 0.07148 on 1 and 79 DF, p-value: 0.7899
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.45679, p-value = 0.00000005397
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 2.0582, p-value = 0.5588
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.1476, df = 1, p-value = 0.7008
Box-Ljung test
data: lm_residuals
X-squared = 0.074931, df = 1, p-value = 0.7843
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-86.05 -36.01 -6.16 18.48 347.58
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 180.1690 17.3759 10.369 0.0000000000000098 ***
ID -1.8053 0.5037 -3.584 0.000701 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 65.89 on 57 degrees of freedom
Multiple R-squared: 0.1839, Adjusted R-squared: 0.1696
F-statistic: 12.85 on 1 and 57 DF, p-value: 0.0007015
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.27119, p-value = 0.02566
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.8213, p-value = 0.2038
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 4.2364, df = 1, p-value = 0.03957
Box-Ljung test
data: lm_residuals
X-squared = 0.0057529, df = 1, p-value = 0.9395
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-66.89 -45.60 -33.04 -15.22 1316.60
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 139.10592 42.21977 3.295 0.0015 **
ID 0.08267 0.92860 0.089 0.9293
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 184.6 on 76 degrees of freedom
Multiple R-squared: 0.0001043, Adjusted R-squared: -0.01305
F-statistic: 0.007926 on 1 and 76 DF, p-value: 0.9293
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.44872, p-value = 0.0000001903
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 2.0646, p-value = 0.567
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.24437, df = 1, p-value = 0.6211
Box-Ljung test
data: lm_residuals
X-squared = 0.086687, df = 1, p-value = 0.7684