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):繊維工業:内閣府"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2005 29.52 41.43 38.67 31.90 31.69 29.08 26.03 26.91 23.15
2006 31.17 29.89 33.25 40.24 37.63 38.17 30.92 31.95 29.90 38.83 34.06 40.95
2007 23.55 134.91 33.92 18.84 24.18 18.26 36.12 33.98 39.11 31.87 28.65 32.86
2008 36.93 31.84 26.79 35.87 37.23 32.84 29.25 28.87 26.05 26.21 27.69 23.58
2009 11.75 21.34 20.89 14.39 14.86 16.60 15.51 15.64 17.92 15.80 17.15 19.32
2010 15.77 15.37 19.31 21.40 20.78 20.78 23.52 22.38 18.72 20.36 19.81 16.69
2011 18.24 18.88 20.72 34.87 26.86 29.50 25.08 23.60 27.37 21.24 24.39 25.99
2012 21.41 26.57 21.74 16.84 21.41 24.69 23.82 22.14 25.50 20.11 19.32 21.24
2013 20.04 19.66 25.16 27.87 19.23 14.84 16.46 19.02 22.50 23.68 23.32 8.23
2014 30.93 16.09 16.62 12.15 24.34 16.08 22.22 18.57 17.27 25.19 19.12 15.48
2015 19.88 17.09 16.45 15.54 18.62 15.86 21.94 22.05 38.76 18.08 17.18 20.45
2016 20.43 18.09 19.37 15.50 18.77 29.57 20.85 18.64 22.07 17.95 24.76 19.26
2017 24.25 29.31 27.87 39.26 19.78 70.38 19.08 22.74 20.12 27.77 24.94 23.53
2018 26.44 28.17 25.57 30.69 28.92 15.94 28.66 36.62 23.85 26.33 35.89 58.99
2019 28.82 29.04 31.21 12.66 55.48 28.18 24.53 49.41 25.67
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-6.4094 -2.3258 -0.5573 1.6956 13.2440
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 19.05557 1.24192 15.34 <0.0000000000000002 ***
ID 0.13529 0.05412 2.50 0.017 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.804 on 37 degrees of freedom
Multiple R-squared: 0.1445, Adjusted R-squared: 0.1214
F-statistic: 6.25 on 1 and 37 DF, p-value: 0.01698
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.23077, p-value = 0.2523
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.1435, p-value = 0.00128
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.075198, df = 1, p-value = 0.7839
Box-Ljung test
data: lm_residuals
X-squared = 7.0248, df = 1, p-value = 0.008039
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-18.484 -4.992 -1.734 2.362 43.592
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.09540 2.00372 8.033 0.00000000000753 ***
ID 0.19801 0.04245 4.664 0.00001241889138 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 8.933 on 79 degrees of freedom
Multiple R-squared: 0.2159, Adjusted R-squared: 0.206
F-statistic: 21.76 on 1 and 79 DF, p-value: 0.00001242
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.22222, p-value = 0.03633
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 2.3427, p-value = 0.9256
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 3.0305, df = 1, p-value = 0.08172
Box-Ljung test
data: lm_residuals
X-squared = 2.5963, df = 1, p-value = 0.1071
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-10.2801 -3.1283 -0.6408 3.2709 15.1637
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 22.070812 1.356851 16.266 <0.0000000000000002 ***
ID -0.004524 0.039333 -0.115 0.909
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.145 on 57 degrees of freedom
Multiple R-squared: 0.0002321, Adjusted R-squared: -0.01731
F-statistic: 0.01323 on 1 and 57 DF, p-value: 0.9088
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.16949, p-value = 0.3674
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.65777, p-value = 0.0000000004473
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 10.247, df = 1, p-value = 0.001369
Box-Ljung test
data: lm_residuals
X-squared = 21.687, df = 1, p-value = 0.00000321
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-18.811 -4.799 -2.007 2.004 43.609
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 15.87440 2.06454 7.689 0.0000000000433 ***
ID 0.21365 0.04541 4.705 0.0000111446133 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 9.029 on 76 degrees of freedom
Multiple R-squared: 0.2256, Adjusted R-squared: 0.2154
F-statistic: 22.14 on 1 and 76 DF, p-value: 0.00001114
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 = 0.07495
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 2.3782, p-value = 0.9423
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 2.7805, df = 1, p-value = 0.09542
Box-Ljung test
data: lm_residuals
X-squared = 3.3761, df = 1, p-value = 0.06615