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 36.1 32.6 41.4 45.1 44.2 43.1 41.5 42.5 40.6 37.3 38.0 39.9
2003 38.9 37.8 36.9 37.4 36.7 41.1 45.1 49.5 44.0 47.0 46.2 46.3
2004 48.8 48.1 48.6 50.9 48.2 50.2 48.8 47.5 48.8 49.2 47.5 46.1
2005 46.5 46.0 47.3 45.8 47.9 46.2 48.1 49.3 49.3 49.5 52.4 53.5
2006 57.0 55.2 51.7 49.2 51.8 50.6 46.4 48.8 47.8 49.0 49.3 50.5
2007 50.5 47.9 48.9 48.0 42.7 42.6 43.2 42.4 42.0 42.7 41.4 36.1
2008 33.9 36.2 34.9 30.1 29.2 29.6 28.2 28.1 28.8 23.6 25.6 17.3
2009 19.4 25.5 26.9 32.1 35.5 37.3 38.5 43.9 42.7 44.5 40.1 36.0
2010 41.9 40.5 41.6 44.3 44.1 44.8 43.5 39.5 36.4 41.2 42.7 45.5
2011 48.3 46.1 21.8 30.7 37.8 45.0 43.8 45.1 47.6 47.2 49.4 45.9
2012 44.9 44.1 46.1 44.2 42.5 40.5 42.9 43.0 42.3 39.1 42.4 47.3
2013 50.2 53.7 52.5 50.4 48.3 49.6 49.6 49.8 50.0 52.7 54.5 53.9
2014 45.0 38.5 30.3 45.9 48.0 46.8 48.2 51.3 46.6 45.6 44.5 45.3
2015 48.2 49.6 50.4 48.6 51.7 51.6 49.3 46.9 47.9 49.0 49.7 48.1
2016 49.3 46.9 44.5 43.8 42.1 38.2 50.0 47.0 48.7 48.2 47.0 49.1
2017 47.0 48.7 49.7 50.4 49.2 50.3 48.8 50.6 49.2 54.2 52.6 51.9
2018 50.0 49.2 49.5 50.0 50.6 49.6 50.4 50.1 51.2 47.1 50.1 47.1
2019 48.6 49.8 49.0 44.5 43.5 42.3 44.5 37.1 36.9 41.0
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-20.4622 -1.1697 0.8754 2.9195 6.4840
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 40.79123 1.62340 25.127 <0.0000000000000002 ***
ID 0.08172 0.07074 1.155 0.255
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.972 on 37 degrees of freedom
Multiple R-squared: 0.03481, Adjusted R-squared: 0.008728
F-statistic: 1.335 on 1 and 37 DF, p-value: 0.2554
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 = 1.1163, p-value = 0.0009135
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.055587, df = 1, p-value = 0.8136
Box-Ljung test
data: lm_residuals
X-squared = 7.7307, df = 1, p-value = 0.005429
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-18.392 -1.272 1.024 2.664 6.716
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 49.11400 0.90863 54.053 <0.0000000000000002 ***
ID -0.02811 0.01902 -1.478 0.143
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.076 on 80 degrees of freedom
Multiple R-squared: 0.02658, Adjusted R-squared: 0.01441
F-statistic: 2.184 on 1 and 80 DF, p-value: 0.1434
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.13415, p-value = 0.4541
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.70048, p-value = 0.000000000003549
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.078316, df = 1, p-value = 0.7796
Box-Ljung test
data: lm_residuals
X-squared = 34.462, df = 1, p-value = 0.000000004346
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-19.3268 -3.6783 0.8239 4.4481 9.4251
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 28.87329 1.58101 18.263 < 0.0000000000000002 ***
ID 0.35010 0.04583 7.639 0.000000000274 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.995 on 57 degrees of freedom
Multiple R-squared: 0.5059, Adjusted R-squared: 0.4972
F-statistic: 58.35 on 1 and 57 DF, p-value: 0.0000000002741
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.22034, p-value = 0.1141
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.61377, p-value = 0.00000000007615
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.90037, df = 1, p-value = 0.3427
Box-Ljung test
data: lm_residuals
X-squared = 29.625, df = 1, p-value = 0.00000005242
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-18.019 -1.280 1.129 2.430 6.696
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 48.54684 0.92984 52.210 <0.0000000000000002 ***
ID -0.01896 0.02019 -0.939 0.351
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.093 on 77 degrees of freedom
Multiple R-squared: 0.01131, Adjusted R-squared: -0.001527
F-statistic: 0.881 on 1 and 77 DF, p-value: 0.3509
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.18987, p-value = 0.116
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.70791, p-value = 0.00000000001157
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.084006, df = 1, p-value = 0.7719
Box-Ljung test
data: lm_residuals
X-squared = 32.613, df = 1, p-value = 0.00000001125