Analysis
[1] "労働力調査(主要項目):非労働力人口(万人):季節調整値:男女計:総務省"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1999 4024
2000 4028 4050 4064 4051 4058 4069 4068 4074 4062 4045 4043 4070
2001 4072 4059 4094 4112 4118 4135 4138 4144 4164 4168 4139 4162
2002 4192 4188 4188 4216 4244 4243 4240 4232 4246 4249 4252 4256
2003 4265 4276 4250 4269 4275 4250 4282 4311 4307 4316 4314 4299
2004 4320 4316 4321 4325 4343 4345 4325 4310 4339 4353 4369 4355
2005 4331 4353 4367 4348 4329 4352 4334 4351 4323 4326 4360 4367
2006 4345 4353 4372 4381 4366 4359 4362 4353 4347 4335 4352 4366
2007 4395 4368 4355 4362 4364 4366 4380 4389 4394 4381 4365 4374
2008 4394 4406 4403 4386 4384 4390 4415 4411 4433 4436 4410 4407
2009 4410 4413 4438 4434 4443 4460 4452 4435 4443 4471 4473 4473
2010 4455 4477 4460 4474 4482 4481 4472 4474 4451 4466 4493 4487
2011 4477 4482 4515 4522 4530 4525 4524 4530 4538 4534 4521 4515
2012 4539 4529 4544 4539 4547 4538 4540 4549 4552 4535 4542 4560
2013 4525 4512 4520 4505 4512 4532 4529 4512 4511 4492 4469 4504
2014 4529 4512 4490 4498 4484 4490 4489 4495 4489 4488 4489 4479
2015 4488 4473 4479 4507 4492 4481 4486 4483 4456 4469 4482 4455
2016 4427 4456 4478 4464 4458 4422 4413 4418 4419 4416 4419 4385
2017 4395 4430 4435 4411 4384 4370 4366 4357 4359 4364 4359 4340
2018 4329 4294 4257 4257 4274 4290 4276 4256 4253 4229 4207 4228
2019 4251 4214 4182 4218 4224 4216 4213 4197 4180 4155
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-35.948 -9.981 1.588 10.230 21.897
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4455.2159 4.3413 1026.25 < 0.0000000000000002 ***
ID 2.6443 0.1892 13.98 0.000000000000000244 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 13.3 on 37 degrees of freedom
Multiple R-squared: 0.8408, Adjusted R-squared: 0.8365
F-statistic: 195.4 on 1 and 37 DF, p-value: 0.0000000000000002436
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 = 0.9574, p-value = 0.00009468
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 1.0049, df = 1, p-value = 0.3161
Box-Ljung test
data: lm_residuals
X-squared = 10.855, df = 1, p-value = 0.0009855
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-70.529 -26.873 2.104 30.773 77.248
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4581.3496 8.3647 547.70 <0.0000000000000002 ***
ID -4.3843 0.1751 -25.04 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 37.53 on 80 degrees of freedom
Multiple R-squared: 0.8869, Adjusted R-squared: 0.8854
F-statistic: 627.1 on 1 and 80 DF, p-value: < 0.00000000000000022
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.073171, p-value = 0.9818
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.23032, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.38462, df = 1, p-value = 0.5351
Box-Ljung test
data: lm_residuals
X-squared = 61.878, df = 1, p-value = 0.000000000000003664
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-46.530 -10.562 2.186 14.547 26.973
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4405.2350 4.3760 1006.67 <0.0000000000000002 ***
ID 2.6430 0.1269 20.84 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 16.59 on 57 degrees of freedom
Multiple R-squared: 0.8839, Adjusted R-squared: 0.8819
F-statistic: 434.1 on 1 and 57 DF, p-value: < 0.00000000000000022
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 = 0.74526, p-value = 0.00000001004
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 3.6818, df = 1, p-value = 0.05501
Box-Ljung test
data: lm_residuals
X-squared = 19.127, df = 1, p-value = 0.00001223
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-71.294 -29.249 -2.204 28.046 76.498
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4576.6524 8.2917 551.95 <0.0000000000000002 ***
ID -4.5448 0.1801 -25.24 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 36.5 on 77 degrees of freedom
Multiple R-squared: 0.8921, Adjusted R-squared: 0.8907
F-statistic: 636.9 on 1 and 77 DF, p-value: < 0.00000000000000022
Two-sample Kolmogorov-Smirnov test
data: lm_residuals and rnorm(n = length(lm_residuals), mean = 0, sd = sd(lm_residuals))
D = 0.075949, p-value = 0.978
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.24963, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 0.022477, df = 1, p-value = 0.8808
Box-Ljung test
data: lm_residuals
X-squared = 57.08, df = 1, p-value = 0.00000000000004186