Analysis
[1] "景気ウォッチャー調査:東北: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 37.5 43.8 45.7 48.9 46.2 42.9 45.5 46.2 40.0 39.3 39.2
2003 37.5 37.1 36.2 38.2 39.4 39.8 41.6 42.6 44.9 47.7 48.9 49.5
2004 50.1 50.3 50.0 50.4 50.8 50.4 49.7 49.0 46.2 44.4 45.0 44.0
2005 45.8 45.2 45.5 44.2 45.2 45.1 45.1 46.0 47.6 49.8 53.2 51.7
2006 53.1 51.0 51.5 49.7 48.3 49.4 45.5 49.6 50.5 50.9 51.4 50.7
2007 49.2 50.1 47.6 50.2 47.5 46.3 45.7 44.8 43.9 43.5 40.1 38.4
2008 36.8 36.7 35.1 28.8 29.3 27.9 27.4 31.0 28.4 23.4 27.9 19.5
2009 22.4 22.8 31.6 34.5 38.9 40.3 41.9 44.6 44.0 43.5 36.3 38.5
2010 40.6 41.6 45.1 45.6 43.0 44.4 43.2 39.1 39.2 39.9 44.5 46.3
2011 47.9 46.9 18.1 31.8 41.3 46.9 50.0 51.0 47.7 53.6 52.0 50.5
2012 50.0 49.4 47.6 47.4 45.5 43.8 43.8 45.2 48.6 44.2 45.2 50.4
2013 53.0 57.5 54.6 54.3 52.2 49.9 51.2 51.5 52.6 53.6 56.1 54.5
2014 44.6 34.9 30.1 46.4 49.6 50.6 50.4 50.1 47.9 45.6 40.5 43.9
2015 46.5 49.0 49.8 49.8 50.1 50.6 49.1 49.1 46.8 47.6 48.3 47.8
2016 47.0 46.3 46.8 45.5 45.0 40.5 44.8 45.4 47.6 48.1 48.5 48.1
2017 47.2 48.1 47.6 47.4 47.4 48.4 48.7 47.3 47.5 51.2 50.5 49.2
2018 48.1 48.1 46.9 47.5 45.0 48.2 47.6 49.0 48.8 47.1 47.1 46.7
2019 46.4 46.8 47.9 47.1 45.5 43.2 43.4 37.9 33.1 43.0
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-25.819 -2.980 1.104 3.213 8.178
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 40.05398 1.90743 20.999 <0.0000000000000002 ***
ID 0.21474 0.08312 2.584 0.0139 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.842 on 37 degrees of freedom
Multiple R-squared: 0.1528, Adjusted R-squared: 0.1299
F-statistic: 6.675 on 1 and 37 DF, p-value: 0.01386
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 = 1.1087, p-value = 0.0008298
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.015726, df = 1, p-value = 0.9002
Box-Ljung test
data: lm_residuals
X-squared = 8.1447, df = 1, p-value = 0.004319
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-19.2591 -1.0196 0.9533 1.8696 7.2504
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 50.38663 0.91039 55.346 < 0.0000000000000002 ***
ID -0.06850 0.01906 -3.595 0.00056 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.084 on 80 degrees of freedom
Multiple R-squared: 0.1391, Adjusted R-squared: 0.1283
F-statistic: 12.92 on 1 and 80 DF, p-value: 0.0005598
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.31707, p-value = 0.0004799
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.65039, p-value = 0.0000000000002787
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 2.1074, df = 1, p-value = 0.1466
Box-Ljung test
data: lm_residuals
X-squared = 38.28, df = 1, p-value = 0.0000000006129
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-25.1389 -2.6657 0.7674 4.4711 9.1283
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 28.93086 1.63792 17.66 < 0.0000000000000002 ***
ID 0.40880 0.04748 8.61 0.00000000000674 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.211 on 57 degrees of freedom
Multiple R-squared: 0.5653, Adjusted R-squared: 0.5577
F-statistic: 74.13 on 1 and 57 DF, p-value: 0.000000000006737
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 = 0.76844, p-value = 0.00000002117
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.13189, df = 1, p-value = 0.7165
Box-Ljung test
data: lm_residuals
X-squared = 23.487, df = 1, p-value = 0.000001257
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-18.6776 -1.0342 0.9996 1.8550 7.1055
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 49.42824 0.91320 54.127 < 0.0000000000000002 ***
ID -0.05422 0.01983 -2.734 0.00777 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.02 on 77 degrees of freedom
Multiple R-squared: 0.08847, Adjusted R-squared: 0.07663
F-statistic: 7.473 on 1 and 77 DF, p-value: 0.007766
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 = 0.67446, p-value = 0.000000000002355
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 2.0083, df = 1, p-value = 0.1564
Box-Ljung test
data: lm_residuals
X-squared = 34.786, df = 1, p-value = 0.00000000368