Analysis
[1] "景気ウォッチャー調査:近畿: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 36.4 34.4 43.3 43.5 43.0 42.4 40.0 43.0 39.2 36.0 34.5 36.2
2003 38.1 39.5 37.7 36.5 37.9 42.4 45.1 48.9 52.2 55.7 54.1 55.5
2004 55.2 54.7 55.4 58.0 57.5 53.8 56.1 50.9 48.4 49.6 49.1 47.4
2005 50.9 48.5 47.6 48.8 50.9 52.6 51.6 53.0 53.3 55.1 58.7 63.3
2006 59.8 57.4 58.2 54.2 53.6 53.0 50.5 52.9 53.2 52.6 51.7 53.2
2007 53.0 54.6 51.3 50.5 47.1 47.0 46.0 44.7 45.6 43.2 41.9 40.2
2008 37.3 35.7 35.1 33.4 34.0 29.8 28.0 29.3 27.1 25.9 24.5 20.0
2009 24.1 23.4 28.1 34.3 33.3 40.5 41.5 42.3 45.9 46.7 37.8 39.5
2010 43.9 45.7 46.3 46.5 46.9 45.5 45.8 45.1 43.6 45.7 50.1 48.7
2011 51.5 50.2 27.6 25.1 34.4 47.3 46.3 47.8 48.2 49.1 48.3 49.7
2012 47.4 47.4 47.6 49.5 45.6 42.7 43.5 44.5 45.4 45.6 47.0 51.1
2013 55.0 56.0 56.8 56.1 56.5 54.7 51.4 52.1 56.6 57.0 59.2 57.0
2014 56.7 55.1 56.6 40.5 44.4 48.0 50.2 50.2 51.2 50.3 48.0 48.0
2015 48.5 52.2 50.9 52.4 52.4 53.5 51.6 52.2 52.3 52.4 48.5 49.3
2016 47.6 45.1 40.9 42.0 43.0 40.3 46.4 46.2 45.7 46.4 50.2 53.3
2017 50.0 49.7 49.2 48.9 51.4 52.2 52.9 51.3 51.9 52.3 53.4 55.7
2018 52.0 50.8 50.6 50.7 48.6 49.2 47.5 49.7 49.0 51.3 50.8 49.9
2019 45.4 48.0 47.0 47.2 45.1 45.0 43.8 45.3 50.4 39.9
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-19.9573 -0.9318 1.5192 2.7515 6.7015
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 43.41808 1.81510 23.921 <0.0000000000000002 ***
ID 0.08628 0.07909 1.091 0.282
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.559 on 37 degrees of freedom
Multiple R-squared: 0.03116, Adjusted R-squared: 0.004972
F-statistic: 1.19 on 1 and 37 DF, p-value: 0.2824
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 = 0.85307, p-value = 0.00001542
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.10741, df = 1, p-value = 0.7431
Box-Ljung test
data: lm_residuals
X-squared = 13.236, df = 1, p-value = 0.0002746
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-11.7161 -2.0314 0.6338 2.6911 7.1605
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 53.55303 0.84704 63.224 < 0.0000000000000002 ***
ID -0.08356 0.01773 -4.713 0.0000102 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.8 on 80 degrees of freedom
Multiple R-squared: 0.2173, Adjusted R-squared: 0.2075
F-statistic: 22.21 on 1 and 80 DF, p-value: 0.00001016
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 = 0.62473, p-value = 0.00000000000006865
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 1.1647, df = 1, p-value = 0.2805
Box-Ljung test
data: lm_residuals
X-squared = 37.787, df = 1, p-value = 0.0000000007892
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-18.937 -4.652 1.539 5.146 9.668
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 30.02624 1.71368 17.521 < 0.0000000000000002 ***
ID 0.38918 0.04968 7.834 0.00000000013 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.498 on 57 degrees of freedom
Multiple R-squared: 0.5185, Adjusted R-squared: 0.51
F-statistic: 61.38 on 1 and 57 DF, p-value: 0.0000000001296
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 = 0.49058, p-value = 0.0000000000002056
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.88974, df = 1, p-value = 0.3455
Box-Ljung test
data: lm_residuals
X-squared = 34.813, df = 1, p-value = 0.000000003629
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-11.4147 -2.1820 0.5926 2.8412 7.1283
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 52.90243 0.87055 60.769 < 0.0000000000000002 ***
ID -0.07598 0.01891 -4.019 0.000135 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.832 on 77 degrees of freedom
Multiple R-squared: 0.1734, Adjusted R-squared: 0.1626
F-statistic: 16.15 on 1 and 77 DF, p-value: 0.0001353
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.12658, p-value = 0.5543
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.63634, p-value = 0.0000000000003379
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 1.9765, df = 1, p-value = 0.1598
Box-Ljung test
data: lm_residuals
X-squared = 35.242, df = 1, p-value = 0.000000002912