Analysis
[1] "景気ウォッチャー調査:北海道: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 37.0 36.9 40.9 41.5 46.1 41.1 42.5 44.0 44.8 40.0 41.9 39.7
2003 43.2 41.3 38.1 39.1 39.6 43.5 44.4 45.1 44.7 47.6 50.1 49.1
2004 46.6 47.1 49.8 51.0 50.4 50.2 50.4 50.0 50.4 51.0 47.5 49.1
2005 49.1 49.2 48.4 47.5 50.0 50.3 51.2 49.6 50.7 51.1 51.2 57.2
2006 54.4 56.0 55.5 51.8 52.1 48.9 49.3 49.8 51.6 55.4 53.6 50.5
2007 52.2 49.6 48.6 51.4 48.8 48.4 47.1 48.1 44.5 43.3 42.4 41.5
2008 39.0 39.9 37.2 35.3 32.7 32.1 28.9 30.6 32.6 31.6 34.7 26.5
2009 23.3 26.9 35.2 41.1 40.5 43.8 46.3 47.1 47.6 47.3 40.1 41.0
2010 43.9 46.2 47.2 45.2 45.8 46.4 50.1 43.5 44.7 45.3 43.1 45.5
2011 47.3 45.9 26.0 35.2 42.2 45.4 48.3 49.1 48.4 49.7 50.4 48.2
2012 50.5 48.7 49.3 51.0 47.8 46.3 46.7 48.2 51.5 48.8 46.5 55.2
2013 56.0 58.0 57.2 55.3 54.3 55.1 57.6 56.5 57.1 58.2 57.7 54.4
2014 48.9 38.4 34.9 50.2 50.6 50.2 50.1 49.3 45.9 47.0 42.6 47.1
2015 48.5 50.0 53.3 52.5 54.4 53.0 51.8 50.2 51.9 48.0 50.5 49.6
2016 50.1 42.6 49.0 49.1 47.7 43.2 47.9 48.9 48.9 50.0 50.0 49.9
2017 49.2 49.3 49.3 49.4 50.2 50.6 49.9 51.8 52.0 51.5 51.3 50.4
2018 50.6 49.8 48.5 48.6 49.0 50.9 50.3 51.2 47.5 51.0 54.7 54.3
2019 51.3 51.8 52.4 49.5 46.2 46.9 44.3 41.5 42.1 47.5
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-19.8501 -1.0477 0.7639 2.4234 5.6596
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 42.67827 1.45419 29.349 < 0.0000000000000002 ***
ID 0.17621 0.06337 2.781 0.00848 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.454 on 37 degrees of freedom
Multiple R-squared: 0.1729, Adjusted R-squared: 0.1505
F-statistic: 7.734 on 1 and 37 DF, p-value: 0.008476
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 = 1.1277, p-value = 0.001054
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.047597, df = 1, p-value = 0.8273
Box-Ljung test
data: lm_residuals
X-squared = 6.7646, df = 1, p-value = 0.009299
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-16.6578 -1.2281 0.1496 2.4474 6.3675
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 52.38166 0.89400 58.593 < 0.0000000000000002 ***
ID -0.05492 0.01871 -2.935 0.00435 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.011 on 80 degrees of freedom
Multiple R-squared: 0.09721, Adjusted R-squared: 0.08593
F-statistic: 8.614 on 1 and 80 DF, p-value: 0.004352
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 = 0.61481, p-value = 0.00000000000003916
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 4.2694, df = 1, p-value = 0.0388
Box-Ljung test
data: lm_residuals
X-squared = 40.17, df = 1, p-value = 0.0000000002328
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-19.421 -2.367 0.839 3.072 8.600
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 32.93618 1.39993 23.53 < 0.0000000000000002 ***
ID 0.35670 0.04058 8.79 0.00000000000341 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.308 on 57 degrees of freedom
Multiple R-squared: 0.5754, Adjusted R-squared: 0.568
F-statistic: 77.26 on 1 and 57 DF, p-value: 0.000000000003411
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.13559, p-value = 0.6544
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.66314, p-value = 0.0000000005495
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 1.1975, df = 1, p-value = 0.2738
Box-Ljung test
data: lm_residuals
X-squared = 27.449, df = 1, p-value = 0.0000001613
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-16.0747 -1.1414 0.0551 2.3240 7.0223
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 51.46183 0.89857 57.27 <0.0000000000000002 ***
ID -0.04060 0.01952 -2.08 0.0408 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.955 on 77 degrees of freedom
Multiple R-squared: 0.05321, Adjusted R-squared: 0.04091
F-statistic: 4.327 on 1 and 77 DF, p-value: 0.04083
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.16456, p-value = 0.2361
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.65005, p-value = 0.0000000000006906
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 4.8308, df = 1, p-value = 0.02796
Box-Ljung test
data: lm_residuals
X-squared = 36.662, df = 1, p-value = 0.000000001405