Analysis
[1] "景気ウォッチャー調査:中国:季節調整値:景気の先行き判断(方向性)DI:内閣府"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2002 42.8 39.8 45.9 50.3 48.0 47.3 46.7 46.8 46.4 43.4 42.6 42.4
2003 44.6 42.9 39.8 42.8 43.8 44.4 46.7 47.3 49.8 55.2 52.3 49.0
2004 51.4 52.3 54.0 53.2 52.7 54.2 52.9 51.4 48.8 50.1 48.6 44.4
2005 48.8 49.3 48.0 50.0 52.2 52.1 52.2 53.1 54.0 54.5 54.8 56.2
2006 55.5 54.8 55.6 53.8 50.0 49.4 51.0 50.4 51.9 51.4 53.3 53.3
2007 53.7 52.5 49.9 50.8 48.6 48.0 46.2 46.8 45.8 45.6 42.9 41.8
2008 39.6 38.3 39.0 35.5 35.1 31.7 30.4 32.9 34.4 26.7 26.4 20.5
2009 23.7 24.3 39.5 40.3 41.7 44.4 45.2 45.3 46.9 46.9 40.0 42.1
2010 44.5 45.2 46.3 46.7 47.0 47.7 46.0 40.7 42.8 41.8 44.2 48.3
2011 46.7 43.5 24.3 38.6 43.0 45.2 47.7 47.2 45.8 48.0 46.1 46.5
2012 45.7 47.8 47.3 46.6 46.7 43.4 41.5 45.3 45.9 45.4 44.6 52.2
2013 56.7 55.6 56.9 56.5 53.9 54.5 54.4 54.1 57.2 56.7 57.3 56.4
2014 53.2 41.2 34.2 47.9 51.2 52.0 50.1 53.8 48.4 48.2 45.0 46.1
2015 48.2 49.7 51.2 51.3 52.3 52.0 52.2 50.3 49.9 50.9 51.7 51.0
2016 49.9 46.6 45.7 44.0 45.3 40.3 47.6 48.1 51.5 49.3 51.2 49.3
2017 49.8 51.2 49.6 51.4 50.9 51.7 53.0 52.9 52.9 52.7 50.9 52.2
2018 52.0 51.2 51.3 51.8 49.9 50.4 45.9 50.5 52.9 51.4 51.2 50.5
2019 49.8 48.2 48.8 46.9 46.4 45.7 46.7 41.3 37.3 42.6
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-20.3105 -1.3944 0.8574 2.3365 6.1378
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 43.36613 1.38322 31.352 <0.0000000000000002 ***
ID 0.06913 0.06027 1.147 0.259
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.236 on 37 degrees of freedom
Multiple R-squared: 0.03433, Adjusted R-squared: 0.008234
F-statistic: 1.315 on 1 and 37 DF, p-value: 0.2588
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.17949, p-value = 0.5622
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.2644, p-value = 0.004941
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.036637, df = 1, p-value = 0.8482
Box-Ljung test
data: lm_residuals
X-squared = 4.5932, df = 1, p-value = 0.0321
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-17.619 -1.432 1.209 2.515 5.212
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 52.82873 0.89295 59.162 < 0.0000000000000002 ***
ID -0.06731 0.01869 -3.601 0.000548 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.006 on 80 degrees of freedom
Multiple R-squared: 0.1395, Adjusted R-squared: 0.1287
F-statistic: 12.97 on 1 and 80 DF, p-value: 0.0005479
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.15854, p-value = 0.2552
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.59827, p-value = 0.00000000000001498
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 1.1308, df = 1, p-value = 0.2876
Box-Ljung test
data: lm_residuals
X-squared = 40.04, df = 1, p-value = 0.0000000002488
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-19.7019 -1.8291 0.3559 4.5420 8.5308
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 33.04944 1.56037 21.180 < 0.0000000000000002 ***
ID 0.31293 0.04523 6.918 0.00000000436 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.917 on 57 degrees of freedom
Multiple R-squared: 0.4564, Adjusted R-squared: 0.4469
F-statistic: 47.86 on 1 and 57 DF, p-value: 0.000000004355
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 = 0.61441, p-value = 0.00000000007821
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 2.4559, df = 1, p-value = 0.1171
Box-Ljung test
data: lm_residuals
X-squared = 29.096, df = 1, p-value = 0.00000006886
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-17.1679 -1.5598 0.8128 2.4997 5.7072
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 52.04255 0.90982 57.201 < 0.0000000000000002 ***
ID -0.05622 0.01976 -2.845 0.00568 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.005 on 77 degrees of freedom
Multiple R-squared: 0.09513, Adjusted R-squared: 0.08338
F-statistic: 8.095 on 1 and 77 DF, p-value: 0.005683
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.13924, p-value = 0.4302
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.61962, p-value = 0.0000000000001376
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 1.3412, df = 1, p-value = 0.2468
Box-Ljung test
data: lm_residuals
X-squared = 37.027, df = 1, p-value = 0.000000001165