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):窯業・土石製品:内閣府"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2005 37.37 37.99 53.94 55.03 50.26 33.21 33.95 43.96 49.89
2006 43.63 49.60 51.07 48.36 50.42 54.06 41.97 32.66 52.60 52.45 57.03 97.84
2007 48.55 70.22 52.95 47.54 61.31 42.24 45.01 55.27 46.68 86.89 59.84 42.44
2008 60.20 50.76 47.33 65.06 62.64 51.69 56.14 71.90 68.03 54.57 40.76 42.75
2009 48.16 28.98 41.63 37.97 36.95 34.38 39.88 29.44 41.67 31.40 39.58 35.40
2010 32.48 32.70 31.40 60.77 25.79 32.97 30.40 26.96 28.04 41.65 27.52 39.14
2011 33.30 36.93 39.43 40.95 37.90 61.51 41.36 26.30 33.76 34.11 30.86 35.04
2012 42.01 43.35 37.04 41.29 30.59 31.82 31.03 56.01 25.23 31.17 32.57 36.80
2013 34.49 29.77 31.71 36.83 55.69 31.64 44.36 35.86 69.53 45.91 56.39 40.42
2014 40.66 30.65 30.95 32.48 30.73 37.60 43.45 46.40 47.57 45.34 34.61 34.08
2015 27.83 63.03 53.56 37.23 44.43 39.88 37.49 36.05 38.09 60.33 34.73 27.20
2016 42.48 31.22 38.89 30.32 35.63 43.73 32.94 44.65 33.02 32.85 44.17 89.09
2017 40.91 38.61 44.32 44.52 32.65 40.06 39.12 59.81 35.25 51.52 40.85 65.26
2018 48.24 51.23 43.78 66.77 58.30 44.11 53.65 38.53 41.19 45.37 80.92 48.27
2019 49.12 32.14 52.13 41.04 51.99 57.82 44.45 41.45 56.46
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-11.400 -5.365 -2.447 3.792 25.409
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 35.35953 2.78401 12.701 0.00000000000000465 ***
ID 0.03531 0.12131 0.291 0.773
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 8.526 on 37 degrees of freedom
Multiple R-squared: 0.002284, Adjusted R-squared: -0.02468
F-statistic: 0.0847 on 1 and 37 DF, p-value: 0.7727
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.20513, p-value = 0.3888
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 2.2165, p-value = 0.6956
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.0089446, df = 1, p-value = 0.9247
Box-Ljung test
data: lm_residuals
X-squared = 0.52065, df = 1, p-value = 0.4706
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-16.704 -7.084 -2.847 4.483 44.240
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 37.47800 2.51874 14.880 < 0.0000000000000002 ***
ID 0.15359 0.05337 2.878 0.00514 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 11.23 on 79 degrees of freedom
Multiple R-squared: 0.09491, Adjusted R-squared: 0.08345
F-statistic: 8.284 on 1 and 79 DF, p-value: 0.005143
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.14815, p-value = 0.338
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.948, p-value = 0.3632
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.046587, df = 1, p-value = 0.8291
Box-Ljung test
data: lm_residuals
X-squared = 0.045809, df = 1, p-value = 0.8305
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-15.245 -6.695 -1.470 3.711 26.052
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 46.93009 2.57922 18.195 < 0.0000000000000002 ***
ID -0.27051 0.07477 -3.618 0.000631 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 9.78 on 57 degrees of freedom
Multiple R-squared: 0.1868, Adjusted R-squared: 0.1725
F-statistic: 13.09 on 1 and 57 DF, p-value: 0.0006309
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.23729, p-value = 0.07193
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.4184, p-value = 0.007458
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 3.5004, df = 1, p-value = 0.06135
Box-Ljung test
data: lm_residuals
X-squared = 4.4353, df = 1, p-value = 0.0352
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-16.368 -7.634 -2.623 4.804 44.114
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 38.8620 2.6008 14.942 <0.0000000000000002 ***
ID 0.1359 0.0572 2.375 0.0201 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 11.37 on 76 degrees of freedom
Multiple R-squared: 0.0691, Adjusted R-squared: 0.05685
F-statistic: 5.641 on 1 and 76 DF, p-value: 0.02007
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 = 1.9684, p-value = 0.3984
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.0067398, df = 1, p-value = 0.9346
Box-Ljung test
data: lm_residuals
X-squared = 0.01384, df = 1, p-value = 0.9063