Analysis
[1] "景気ウォッチャー調査:北海道: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 35.4 34.3 39.3 39.6 40.3 37.6 40.1 40.4 42.6 38.2 41.2 41.0
2003 40.5 41.2 40.9 30.9 33.2 39.2 41.2 43.2 45.4 46.0 46.9 47.2
2004 47.0 48.1 47.1 49.5 48.6 49.1 50.7 49.5 46.9 48.5 45.3 47.2
2005 48.7 45.9 48.5 47.2 49.5 48.6 48.7 47.5 50.6 46.9 53.7 54.0
2006 54.9 53.8 54.2 52.0 52.1 45.6 47.3 49.3 49.1 53.6 50.7 51.2
2007 48.5 49.9 46.9 46.8 43.9 47.9 44.4 44.6 43.3 40.2 38.9 36.7
2008 39.5 38.5 36.2 33.3 31.9 27.0 26.5 25.7 29.3 29.1 28.9 24.8
2009 24.2 24.7 27.0 33.7 36.1 42.4 42.7 45.3 44.2 46.2 44.3 45.9
2010 45.7 46.4 47.5 48.1 47.9 46.1 49.7 47.3 45.6 44.5 47.3 48.3
2011 48.3 51.5 25.1 24.7 32.5 45.0 49.8 47.5 48.0 48.2 49.9 49.8
2012 50.5 48.0 49.6 47.5 47.0 43.8 45.2 46.3 46.2 47.7 47.1 49.6
2013 50.2 53.6 53.7 54.4 52.8 56.5 55.0 54.4 57.3 58.6 57.9 57.5
2014 56.3 55.5 50.5 39.1 42.0 45.8 47.5 46.8 47.7 46.4 40.7 41.4
2015 46.3 49.1 50.3 54.1 52.3 51.1 51.4 50.3 50.1 48.2 48.8 49.5
2016 48.7 46.1 42.8 44.9 46.9 45.2 45.7 47.0 47.4 49.2 50.8 47.5
2017 48.9 48.2 48.5 45.9 50.4 49.9 51.2 51.9 48.8 49.8 49.6 50.9
2018 49.1 47.5 47.5 47.8 47.0 48.6 48.0 48.7 37.1 42.0 51.7 52.9
2019 48.8 51.4 45.5 49.6 48.0 47.9 42.8 41.3 49.3 40.5
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-21.1423 -0.0943 1.1863 2.6149 5.7475
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 44.98920 1.90192 23.655 <0.0000000000000002 ***
ID 0.04490 0.08288 0.542 0.591
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.825 on 37 degrees of freedom
Multiple R-squared: 0.00787, Adjusted R-squared: -0.01894
F-statistic: 0.2935 on 1 and 37 DF, p-value: 0.5912
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.30769, p-value = 0.04927
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.8246, p-value = 0.000008902
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.02949, df = 1, p-value = 0.8637
Box-Ljung test
data: lm_residuals
X-squared = 14.346, df = 1, p-value = 0.0001521
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-11.7601 -2.1940 0.5546 2.3262 7.2859
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 52.07082 0.88728 58.686 < 0.0000000000000002 ***
ID -0.07567 0.01857 -4.074 0.000108 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.981 on 80 degrees of freedom
Multiple R-squared: 0.1719, Adjusted R-squared: 0.1615
F-statistic: 16.6 on 1 and 80 DF, p-value: 0.0001079
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.085366, p-value = 0.9286
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.7272, p-value = 0.00000000001256
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 2.9148, df = 1, p-value = 0.08777
Box-Ljung test
data: lm_residuals
X-squared = 33.087, df = 1, p-value = 0.000000008812
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-19.685 -4.251 1.233 5.586 8.572
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 31.35447 1.75983 17.817 < 0.0000000000000002 ***
ID 0.36197 0.05101 7.095 0.00000000221 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.673 on 57 degrees of freedom
Multiple R-squared: 0.469, Adjusted R-squared: 0.4597
F-statistic: 50.34 on 1 and 57 DF, p-value: 0.000000002207
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.11864, p-value = 0.8052
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.47366, p-value = 0.00000000000007958
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 1.1617, df = 1, p-value = 0.2811
Box-Ljung test
data: lm_residuals
X-squared = 36.122, df = 1, p-value = 0.000000001853
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-11.6935 -2.4761 0.5595 2.4070 7.3624
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 51.75579 0.91822 56.365 < 0.0000000000000002 ***
ID -0.07402 0.01994 -3.712 0.000387 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.042 on 77 degrees of freedom
Multiple R-squared: 0.1518, Adjusted R-squared: 0.1408
F-statistic: 13.78 on 1 and 77 DF, p-value: 0.0003871
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.063291, p-value = 0.9977
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.72269, p-value = 0.00000000002266
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 4.4683, df = 1, p-value = 0.03453
Box-Ljung test
data: lm_residuals
X-squared = 31.953, df = 1, p-value = 0.00000001579