Analysis
[1] "景気ウォッチャー調査:関東:北関東: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 33.3 31.9 36.7 38.4 42.8 35.6 38.0 41.0 38.1 35.6 36.1 35.9
2003 37.5 36.1 36.3 32.5 38.1 39.9 40.2 40.7 44.4 46.6 48.0 49.4
2004 45.7 49.3 49.9 51.5 48.4 50.1 52.6 49.5 42.1 45.8 46.0 43.0
2005 44.1 43.3 42.0 42.2 45.0 46.3 45.7 45.1 48.6 44.7 48.6 56.2
2006 52.1 52.7 53.6 51.0 50.0 46.6 46.2 47.5 45.7 48.4 46.6 50.4
2007 48.9 46.0 45.9 44.7 42.7 41.5 38.8 38.5 37.8 38.8 40.5 35.0
2008 33.4 35.3 31.0 29.2 25.9 24.2 25.5 26.2 25.7 20.4 22.1 16.7
2009 18.4 18.1 21.2 25.3 29.8 34.9 37.5 36.3 40.9 40.1 37.7 33.5
2010 40.4 39.8 41.4 42.4 44.5 45.0 48.2 45.8 39.8 38.1 45.0 47.6
2011 46.9 49.8 18.7 21.2 30.9 48.8 47.6 44.9 44.6 48.1 48.2 47.2
2012 45.0 42.9 43.9 43.3 41.1 41.0 41.7 41.4 41.8 39.0 39.7 42.1
2013 45.9 47.5 48.4 49.1 49.8 48.2 46.7 48.3 51.2 51.7 56.1 54.2
2014 53.8 47.2 52.6 36.9 42.7 44.9 47.1 49.8 47.9 44.5 42.9 41.3
2015 44.1 45.7 45.5 46.3 47.1 48.0 46.7 46.2 45.7 49.2 44.9 44.9
2016 47.9 42.5 42.1 38.7 40.0 40.1 44.0 44.5 45.2 47.9 48.5 49.7
2017 47.7 48.2 46.7 48.1 47.6 48.3 48.2 48.5 48.6 49.1 53.5 53.7
2018 48.7 43.9 48.5 48.7 49.3 53.3 47.2 48.7 48.8 47.8 48.8 47.3
2019 45.1 45.9 44.8 42.3 44.6 42.5 38.3 43.4 44.5 35.0
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-22.9666 -1.3792 0.6549 4.1541 8.1860
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 40.71997 2.16445 18.813 <0.0000000000000002 ***
ID 0.05259 0.09431 0.558 0.58
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.629 on 37 degrees of freedom
Multiple R-squared: 0.008334, Adjusted R-squared: -0.01847
F-statistic: 0.3109 on 1 and 37 DF, p-value: 0.5805
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.25641, p-value = 0.1547
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 1.0074, p-value = 0.000205
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.15958, df = 1, p-value = 0.6895
Box-Ljung test
data: lm_residuals
X-squared = 10.352, df = 1, p-value = 0.001293
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-10.6368 -2.0383 0.3216 2.2654 8.3998
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 48.05962 0.84350 56.977 <0.0000000000000002 ***
ID -0.03267 0.01766 -1.851 0.0679 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.784 on 80 degrees of freedom
Multiple R-squared: 0.04105, Adjusted R-squared: 0.02907
F-statistic: 3.425 on 1 and 80 DF, p-value: 0.06791
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.14634, p-value = 0.3453
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.76038, p-value = 0.00000000005549
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.047283, df = 1, p-value = 0.8279
Box-Ljung test
data: lm_residuals
X-squared = 27.707, df = 1, p-value = 0.0000001411
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-20.8430 -4.4655 -0.2953 6.0481 11.7224
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 26.13174 1.92811 13.553 < 0.0000000000000002 ***
ID 0.38318 0.05589 6.856 0.00000000554 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.311 on 57 degrees of freedom
Multiple R-squared: 0.4519, Adjusted R-squared: 0.4423
F-statistic: 47 on 1 and 57 DF, p-value: 0.000000005535
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.10169, p-value = 0.9239
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.59679, p-value = 0.00000000003685
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.29517, df = 1, p-value = 0.5869
Box-Ljung test
data: lm_residuals
X-squared = 30.538, df = 1, p-value = 0.00000003274
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-10.7250 -2.0441 0.3001 2.2931 8.3004
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 48.07887 0.87425 54.994 <0.0000000000000002 ***
ID -0.03491 0.01899 -1.839 0.0698 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.848 on 77 degrees of freedom
Multiple R-squared: 0.04207, Adjusted R-squared: 0.02963
F-statistic: 3.381 on 1 and 77 DF, p-value: 0.0698
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.17722, p-value = 0.1677
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.76024, p-value = 0.0000000001158
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.0088582, df = 1, p-value = 0.925
Box-Ljung test
data: lm_residuals
X-squared = 26.906, df = 1, p-value = 0.0000002136