Analysis
[1] "一般職業紹介状況:有効求人:正社員:季節調整値:厚生労働省"
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2004 946326 954598
2005 955678 953579 973470 986305 997678 989478 983307 982812 987881 996502 1003023 1001328
2006 1008140 1009889 1020053 1021668 1015889 1014791 1019002 1013217 1005324 999655 995428 984605
2007 981590 977603 969473 964041 963211 960154 956146 949849 940830 923817 907741 902767
2008 894595 893749 889358 881970 878737 864661 848033 830434 809697 788636 770686 760790
2009 727807 680232 635896 607127 575776 563380 552371 549095 549615 547802 545337 548952
2010 555038 559427 566107 581147 590254 602938 614646 625392 633661 654570 669854 681164
2011 694364 714191 710121 714704 718154 727576 735506 749589 755136 766783 777187 784395
2012 793077 801863 815752 827947 837198 843849 848062 847931 846823 848659 847630 854409
2013 857902 866129 868850 872215 879827 888950 895776 900269 914115 917728 925382 933832
2014 940404 941053 945634 952474 959080 962887 963534 962804 962521 963514 965473 963111
2015 971737 977452 980658 983754 988972 988636 1001547 1017448 1016981 1021009 1033763 1040511
2016 1042837 1056408 1059016 1063462 1065768 1066365 1076757 1083016 1087364 1095423 1099329 1106684
2017 1115421 1122581 1135512 1142076 1143930 1154635 1162097 1170548 1181051 1190041 1197596 1208051
2018 1204285 1203989 1202984 1209866 1219039 1229044 1226499 1229162 1229954 1231893 1239038 1251417
2019 1239480 1236677 1240260 1232512 1235537 1230996 1231589 1234318 1226413 1215551
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-34086 -9088 4606 9384 28004
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 529858.1 4490.7 117.99 <0.0000000000000002 ***
ID 9195.8 195.7 46.99 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 13750 on 37 degrees of freedom
Multiple R-squared: 0.9835, Adjusted R-squared: 0.9831
F-statistic: 2208 on 1 and 37 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 = 0.25641, p-value = 0.1547
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.18159, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 3.443, df = 1, p-value = 0.06352
Box-Ljung test
data: lm_residuals
X-squared = 28.345, df = 1, p-value = 0.0000001015
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-67623 -7732 -1035 12575 38606
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 859276.38 4203.16 204.44 <0.0000000000000002 ***
ID 5169.48 87.98 58.76 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 18860 on 80 degrees of freedom
Multiple R-squared: 0.9774, Adjusted R-squared: 0.9771
F-statistic: 3453 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 = 0.14634, p-value = 0.3453
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.086076, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 18.946, df = 1, p-value = 0.00001345
Box-Ljung test
data: lm_residuals
X-squared = 65.231, df = 1, p-value = 0.0000000000000006661
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-140739 -89987 592 60055 246593
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 629148.2 26601.5 23.651 < 0.0000000000000002 ***
ID 2996.2 771.1 3.885 0.000269 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 100900 on 57 degrees of freedom
Multiple R-squared: 0.2094, Adjusted R-squared: 0.1955
F-statistic: 15.1 on 1 and 57 DF, p-value: 0.0002686
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 = 0.023896, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 26.185, df = 1, p-value = 0.0000003102
Box-Ljung test
data: lm_residuals
X-squared = 53.924, df = 1, p-value = 0.0000000000002084
Call:
lm(formula = value ~ ID)
Residuals:
Min 1Q Median 3Q Max
-67203 -9296 -850 12745 38675
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 875624.86 4358.40 200.91 <0.0000000000000002 ***
ID 5153.53 94.66 54.44 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 19190 on 77 degrees of freedom
Multiple R-squared: 0.9747, Adjusted R-squared: 0.9744
F-statistic: 2964 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 = 0.11392, p-value = 0.6878
alternative hypothesis: two-sided
Durbin-Watson test
data: value ~ ID
DW = 0.085668, p-value < 0.00000000000000022
alternative hypothesis: true autocorrelation is greater than 0
studentized Breusch-Pagan test
data: value ~ ID
BP = 18.024, df = 1, p-value = 0.00002182
Box-Ljung test
data: lm_residuals
X-squared = 62.983, df = 1, p-value = 0.000000000000002109
