Analysis
[1] "景気ウォッチャー調査:関東:季節調整値:景気の現状判断(方向性)DI:内閣府"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2002 33.8 31.1 39.3 41.9 44.4 39.7 39.9 41.4 40.1 36.7 36.3 38.3
2003 38.6 37.9 37.2 35.6 36.5 41.7 42.6 43.7 46.3 50.1 48.5 50.8
2004 50.9 52.9 50.2 52.7 50.6 50.6 53.6 50.0 46.5 46.9 47.9 45.4
2005 46.0 44.8 46.7 46.0 49.2 50.1 49.4 49.3 50.6 50.4 54.1 58.5
2006 54.7 54.2 54.7 52.5 51.4 48.5 47.1 48.7 49.5 51.2 49.8 50.9
2007 50.6 50.3 48.9 48.3 46.5 45.9 43.1 43.8 43.2 43.2 42.0 40.3
2008 34.1 36.5 34.7 32.3 28.6 26.8 26.6 27.5 27.4 21.8 21.4 19.1
2009 19.9 20.7 23.6 29.5 32.1 38.9 39.1 39.0 41.6 41.8 35.8 35.5
2010 40.5 40.5 41.1 42.3 43.7 45.2 45.1 43.4 39.9 41.0 46.2 46.2
2011 46.5 47.7 17.2 21.6 30.8 48.0 49.6 45.0 44.3 48.2 47.9 47.8
2012 45.2 44.9 45.6 44.9 41.9 41.3 41.1 43.5 41.1 41.1 40.9 43.9
2013 49.1 50.8 52.4 52.3 52.5 50.8 49.6 50.9 54.5 54.2 55.3 55.8
2014 55.2 49.1 53.8 39.7 44.5 47.2 50.2 49.4 49.1 45.6 42.5 44.1
2015 45.6 48.8 47.7 49.1 50.8 51.2 49.5 49.5 46.3 50.9 46.7 47.2
2016 46.1 42.9 41.0 41.2 41.3 39.4 43.6 45.5 45.4 47.1 49.0 49.8
2017 50.0 47.3 48.4 49.1 49.1 50.2 50.6 50.9 51.9 51.6 53.1 52.7
2018 50.1 49.1 50.2 49.4 49.5 50.7 48.6 48.7 49.0 49.3 49.6 46.3
2019 45.9 46.6 44.9 44.3 45.1 43.6 40.8 44.7 46.6 37.2
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-24.624 -1.631 1.513 4.094 7.414
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 40.19703 2.13739 18.807 <0.0000000000000002 ***
ID 0.09040 0.09314 0.971 0.338
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.546 on 37 degrees of freedom
Multiple R-squared: 0.02483, Adjusted R-squared: -0.001523
F-statistic: 0.9422 on 1 and 37 DF, p-value: 0.338
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.38462, p-value = 0.00581
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.95829, p-value = 0.00009605
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.068584, df = 1, p-value = 0.7934
Box-Ljung test
data: lm_residuals
X-squared = 11.383, df = 1, p-value = 0.000741
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-9.8365 -1.9553 0.6456 2.4062 6.0480
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 50.39874 0.81556 61.796 < 0.0000000000000002 ***
ID -0.05389 0.01707 -3.157 0.00225 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.659 on 80 degrees of freedom
Multiple R-squared: 0.1108, Adjusted R-squared: 0.09967
F-statistic: 9.967 on 1 and 80 DF, p-value: 0.002248
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.097561, p-value = 0.8332
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.58672, p-value = 0.00000000000000751
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.16811, df = 1, p-value = 0.6818
Box-Ljung test
data: lm_residuals
X-squared = 38.165, df = 1, p-value = 0.00000000065
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-23.213 -4.054 1.049 5.646 8.151
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 27.33063 1.83406 14.90 < 0.0000000000000002 ***
ID 0.37378 0.05317 7.03 0.00000000283 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.954 on 57 degrees of freedom
Multiple R-squared: 0.4644, Adjusted R-squared: 0.455
F-statistic: 49.43 on 1 and 57 DF, p-value: 0.000000002832
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.25424, p-value = 0.04374
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.61212, p-value = 0.00000000007104
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.099684, df = 1, p-value = 0.7522
Box-Ljung test
data: lm_residuals
X-squared = 29.727, df = 1, p-value = 0.00000004974
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-9.7822 -2.1031 0.7571 2.4469 6.1076
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 50.16534 0.84462 59.394 < 0.0000000000000002 ***
ID -0.05255 0.01834 -2.865 0.00538 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.718 on 77 degrees of freedom
Multiple R-squared: 0.09632, Adjusted R-squared: 0.08458
F-statistic: 8.207 on 1 and 77 DF, p-value: 0.005377
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 = 0.58492, p-value = 0.00000000000001932
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.6196, df = 1, p-value = 0.4312
Box-Ljung test
data: lm_residuals
X-squared = 36.695, df = 1, p-value = 0.000000001381