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):パルプ・紙紙加工品:内閣府"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2005 52.18 57.62 78.10 54.71 83.23 92.94 75.77 62.11 62.84
2006 95.87 165.28 73.20 160.16 93.42 122.23 56.52 53.60 77.02 163.68 79.75 53.87
2007 59.78 79.37 80.13 66.73 66.24 35.56 65.26 60.58 39.61 58.99 51.50 66.64
2008 39.89 35.80 51.51 30.21 44.61 43.80 59.41 43.96 52.03 45.45 37.51 47.41
2009 35.35 36.83 29.24 28.11 23.92 26.81 33.60 36.55 31.45 31.56 25.33 34.49
2010 45.54 41.35 43.56 46.36 37.22 40.60 35.00 25.39 36.74 31.14 29.57 28.95
2011 22.59 29.62 21.43 30.52 30.86 48.53 26.79 43.07 22.40 60.48 22.56 23.32
2012 38.45 28.99 24.59 40.99 37.16 29.37 38.65 32.47 24.80 26.00 83.98 47.34
2013 80.11 31.79 55.58 18.35 42.75 16.94 162.41 21.59 46.31 45.87 156.62 46.04
2014 33.19 37.16 28.33 24.91 41.10 32.31 44.30 95.95 99.70 30.28 19.43 43.99
2015 17.25 26.15 213.59 76.76 31.50 32.91 34.12 25.06 22.05 58.00 28.52 27.69
2016 25.55 37.29 33.11 21.99 51.31 35.22 29.62 31.91 27.28 31.94 50.82 32.23
2017 27.47 129.87 37.36 28.89 26.02 56.89 43.93 42.25 24.42 31.00 29.03 101.80
2018 46.24 51.67 33.30 54.43 32.43 33.43 36.74 32.24 56.62 37.79 48.26 46.35
2019 61.55 29.39 40.31 26.23 47.92 35.61 51.50 35.32 29.99
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-13.426 -8.150 -3.178 5.724 46.973
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 32.9199 3.9513 8.331 0.000000000518 ***
ID 0.1075 0.1722 0.625 0.536
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 12.1 on 37 degrees of freedom
Multiple R-squared: 0.01043, Adjusted R-squared: -0.01631
F-statistic: 0.3901 on 1 and 37 DF, p-value: 0.5361
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 = 1.9987, p-value = 0.43
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 3.2781, df = 1, p-value = 0.07021
Box-Ljung test
data: lm_residuals
X-squared = 0.0035793, df = 1, p-value = 0.9523
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-35.753 -17.306 -8.504 5.592 165.082
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 53.8890 7.3711 7.311 0.000000000189 ***
ID -0.1993 0.1562 -1.276 0.206
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 32.86 on 79 degrees of freedom
Multiple R-squared: 0.0202, Adjusted R-squared: 0.007796
F-statistic: 1.629 on 1 and 79 DF, p-value: 0.2056
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.19753, p-value = 0.08471
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 2.0616, p-value = 0.5649
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 3.0884, df = 1, p-value = 0.07885
Box-Ljung test
data: lm_residuals
X-squared = 0.10451, df = 1, p-value = 0.7465
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-15.829 -8.387 -2.069 6.954 46.247
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 36.42947 3.37966 10.779 0.00000000000000224 ***
ID 0.02370 0.09797 0.242 0.81
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 12.82 on 57 degrees of freedom
Multiple R-squared: 0.001025, Adjusted R-squared: -0.0165
F-statistic: 0.0585 on 1 and 57 DF, p-value: 0.8098
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.15254, p-value = 0.5021
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.6956, p-value = 0.09352
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 5.2199, df = 1, p-value = 0.02233
Box-Ljung test
data: lm_residuals
X-squared = 1.0777, df = 1, p-value = 0.2992
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-35.379 -17.127 -8.745 5.384 165.293
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 52.8935 7.6081 6.952 0.00000000108 ***
ID -0.1915 0.1673 -1.144 0.256
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 33.27 on 76 degrees of freedom
Multiple R-squared: 0.01694, Adjusted R-squared: 0.004008
F-statistic: 1.31 on 1 and 76 DF, p-value: 0.256
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.24359, p-value = 0.01923
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 2.0398, p-value = 0.5236
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 3.7347, df = 1, p-value = 0.05329
Box-Ljung test
data: lm_residuals
X-squared = 0.060408, df = 1, p-value = 0.8059