Chap2. Use the data in BWGHT to answer this question.
#install.packages("wooldridge")
library(wooldridge)
data<-wooldridge::bwghtlibrary(wooldridge)
Datanew<-wooldridge::wage2
attach(Datanew)
Datanew## wage hours IQ KWW educ exper tenure age married black south urban sibs
## 1 769 40 93 35 12 11 2 31 1 0 0 1 1
## 2 808 50 119 41 18 11 16 37 1 0 0 1 1
## 3 825 40 108 46 14 11 9 33 1 0 0 1 1
## 4 650 40 96 32 12 13 7 32 1 0 0 1 4
## 5 562 40 74 27 11 14 5 34 1 0 0 1 10
## 6 1400 40 116 43 16 14 2 35 1 1 0 1 1
## 7 600 40 91 24 10 13 0 30 0 0 0 1 1
## 8 1081 40 114 50 18 8 14 38 1 0 0 1 2
## 9 1154 45 111 37 15 13 1 36 1 0 0 0 2
## 10 1000 40 95 44 12 16 16 36 1 0 0 1 1
## 11 930 43 132 44 18 8 13 38 1 0 0 0 1
## 12 921 38 102 45 14 9 11 33 1 0 0 0 1
## 13 900 45 125 40 15 4 3 30 0 0 0 0 2
## 14 1318 38 119 24 16 7 2 28 1 0 0 1 3
## 15 1792 40 118 47 16 9 9 34 1 0 0 1 1
## 16 958 50 105 37 10 17 2 35 1 0 0 1 1
## 17 1360 45 109 39 15 6 9 36 1 0 0 1 3
## 18 850 40 72 36 11 19 10 38 1 0 0 1 2
## 19 830 44 105 29 14 4 7 29 1 0 0 1 3
## 20 471 44 101 34 12 13 7 31 1 0 0 1 3
## 21 1275 40 123 37 14 9 1 31 1 0 0 1 0
## 22 1615 50 113 49 16 10 4 36 1 0 0 1 2
## 23 873 65 95 36 12 14 3 38 1 0 0 1 5
## 24 2137 45 145 50 16 17 8 38 1 0 0 1 2
## 25 1053 38 114 35 16 12 7 32 1 0 0 1 0
## 26 1602 60 124 32 16 8 9 29 1 0 0 1 3
## 27 1188 40 93 40 13 16 5 35 1 0 0 1 2
## 28 800 40 115 39 18 11 1 35 1 0 1 1 0
## 29 1417 48 125 41 17 9 4 34 1 0 0 1 3
## 30 635 40 128 35 18 8 13 36 0 0 0 1 2
## 31 1000 40 103 40 12 17 2 34 1 0 0 1 1
## 32 1424 50 98 41 14 15 4 35 1 0 0 1 1
## 33 2668 75 108 41 13 12 2 32 1 0 1 1 1
## 34 666 75 129 40 18 8 12 38 1 0 0 0 2
## 35 1779 40 132 42 17 8 9 32 1 0 0 1 1
## 36 782 40 92 24 13 9 10 31 1 0 0 1 3
## 37 1572 35 108 39 14 7 5 28 1 1 0 1 1
## 38 1274 40 106 35 13 11 12 31 1 0 0 1 0
## 39 714 35 105 37 16 9 2 29 1 0 0 1 1
## 40 1081 40 123 46 18 11 7 36 1 0 0 0 1
## 41 692 40 108 48 12 21 11 38 1 0 0 1 3
## 42 1318 38 122 47 17 10 9 33 1 0 0 1 1
## 43 1239 45 109 44 12 16 4 38 1 0 0 1 3
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## 45 1748 55 125 50 18 12 12 34 1 0 0 1 1
## 46 981 40 122 41 14 9 15 35 0 0 0 1 3
## 47 770 40 105 34 17 7 11 30 1 0 0 1 3
## 48 1154 50 94 37 16 11 5 33 1 0 0 1 1
## 49 1155 40 102 36 12 16 5 34 1 0 0 1 1
## 50 808 60 109 47 13 14 12 38 1 0 0 1 2
## 51 1100 40 105 45 12 22 14 38 1 0 0 1 1
## 52 1154 40 134 46 18 10 10 37 1 0 1 1 2
## 53 1749 40 108 32 12 13 9 29 0 0 0 1 7
## 54 1000 50 104 36 16 11 8 30 1 0 0 1 1
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## 56 769 55 120 49 18 8 12 32 0 0 0 1 1
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## 59 1452 45 115 45 17 1 7 30 1 0 0 1 3
## 60 800 40 96 33 12 17 12 35 1 0 0 1 1
## 61 1748 55 123 48 17 10 7 36 1 0 0 1 1
## 62 1151 40 98 34 12 12 12 29 1 0 0 1 3
## 63 840 50 96 34 12 18 13 35 1 0 0 1 2
## 64 978 45 89 47 12 17 2 38 1 0 0 1 0
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## 66 619 42 93 20 12 10 10 28 0 0 0 1 8
## 67 442 60 82 32 12 12 0 33 1 0 0 1 2
## 68 600 40 120 45 15 12 1 38 1 0 0 0 3
## 69 1366 38 122 38 12 15 15 32 1 0 0 1 1
## 70 1643 48 117 48 18 8 2 33 1 0 0 1 2
## 71 1455 40 109 44 16 8 13 36 1 0 0 1 1
## 72 2310 40 114 55 18 12 7 38 1 0 0 1 2
## 73 1682 45 126 40 16 10 11 31 1 0 0 1 1
## 74 1235 40 82 45 12 10 2 33 1 0 0 1 1
## 75 855 45 119 45 16 11 4 32 1 0 0 1 3
## 76 1072 42 104 37 16 6 8 35 1 0 0 1 2
## 77 1040 40 115 49 15 8 15 35 1 0 0 1 3
## 78 1000 50 97 45 12 17 15 36 1 0 0 1 1
## 79 675 40 105 27 15 8 2 31 1 0 0 0 3
## 80 1100 37 100 32 12 14 1 30 1 0 0 1 1
## 81 996 50 114 41 12 5 14 31 1 0 0 1 2
## 82 732 40 73 28 10 17 5 36 1 0 0 1 5
## 83 1200 40 96 48 11 15 3 37 1 0 0 1 4
## 84 1694 30 113 30 15 9 8 29 0 0 0 1 3
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## 90 672 61 96 38 12 13 3 34 1 0 0 1 2
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## 92 1076 45 105 24 12 5 8 31 0 0 0 1 1
## 93 750 40 94 18 12 16 3 37 1 1 0 1 7
## 94 1186 30 91 41 17 8 9 33 1 0 0 1 1
## 95 833 48 96 37 16 9 7 31 1 0 0 1 4
## 96 650 40 69 37 12 14 11 30 1 1 0 1 3
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## 101 1299 60 125 44 18 6 12 35 0 0 0 1 1
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## 648 2 8 NA 6.582025
## 649 3 10 11 6.173786
## 650 2 17 12 6.694562
## 651 1 12 12 7.273787
## 652 1 12 NA 6.994850
## 653 1 12 NA 5.857933
## 654 1 7 NA 6.214608
## 655 2 16 14 6.933423
## 656 2 13 16 6.357842
## 657 2 12 12 7.195187
## 658 NA 10 NA 6.818924
## 659 1 11 10 7.007601
## 660 NA 9 3 6.813445
## 661 7 1 4 6.907755
## 662 6 3 4 7.056175
## 663 8 3 4 6.908755
## 664 4 NA NA 6.569481
## 665 1 12 12 6.834109
## 666 1 6 2 5.991465
## 667 3 12 12 7.123673
## 668 1 12 NA 6.970730
## 669 NA 12 12 5.998937
## 670 2 NA NA 6.845880
## 671 2 8 3 6.699501
## 672 NA 6 10 6.551080
## 673 NA NA NA 6.354370
## 674 6 12 12 6.109248
## 675 3 8 6 6.431331
## 676 4 8 6 6.089045
## 677 1 NA NA 6.437752
## 678 2 6 NA 6.587550
## 679 6 6 NA 6.214608
## 680 1 17 18 6.907755
## 681 1 8 7 5.973810
## 682 2 9 12 6.396930
## 683 1 6 NA 6.869014
## 684 1 10 8 6.869014
## 685 4 0 10 6.762730
## 686 1 16 14 7.050990
## 687 1 4 14 7.234177
## 688 2 NA NA 6.595780
## 689 3 8 8 6.762730
## 690 6 8 4 6.551080
## 691 NA 5 4 6.882438
## 692 3 12 12 7.170120
## 693 1 12 12 6.802395
## 694 5 8 8 6.720220
## 695 NA 12 NA 6.907755
## 696 1 16 12 6.717805
## 697 NA 12 NA 6.214608
## 698 2 12 12 7.051856
## 699 NA 12 12 6.856462
## 700 5 8 NA 6.551080
## 701 1 12 NA 7.444249
## 702 1 11 12 6.278522
## 703 1 12 12 7.602901
## 704 2 7 7 6.791222
## 705 2 12 12 8.032035
## 706 2 12 16 7.338888
## 707 NA NA NA 6.230482
## 708 1 16 16 7.210818
## 709 1 12 12 7.041412
## 710 1 10 10 6.869014
## 711 1 12 12 7.130899
## 712 1 8 8 6.897705
## 713 5 12 12 6.807935
## 714 5 0 2 6.830874
## 715 4 8 8 7.351800
## 716 1 12 8 7.179308
## 717 1 7 NA 6.827629
## 718 2 13 13 6.778785
## 719 1 12 10 6.684612
## 720 3 12 10 6.955593
## 721 4 12 10 6.309918
## 722 1 12 14 7.081708
## 723 1 12 12 6.368187
## 724 1 6 7 7.090077
## 725 2 14 17 6.680855
## 726 2 16 12 7.223296
## 727 NA 12 8 5.886104
## 728 2 12 12 7.146772
## 729 2 8 12 7.513164
## 730 3 12 7 6.812345
## 731 3 12 12 7.465083
## 732 5 12 12 6.253829
## 733 6 12 12 6.694562
## 734 3 12 NA 7.667158
## 735 1 8 8 6.539586
## 736 2 12 12 6.836259
## 737 2 12 8 6.699501
## 738 1 12 12 6.763885
## 739 5 12 12 7.273787
## 740 2 11 6 6.214608
## 741 1 12 13 7.232733
## 742 5 12 12 6.269096
## 743 3 12 NA 6.781058
## 744 1 14 17 6.933423
## 745 NA 12 10 7.100027
## 746 2 10 NA 7.390182
## 747 2 12 NA 7.519150
## 748 2 12 12 7.379008
## 749 2 8 14 6.907755
## 750 4 10 NA 6.602588
## 751 3 12 12 7.437795
## 752 2 12 8 7.437795
## 753 3 NA 12 6.932448
## 754 3 11 12 7.009409
## 755 4 12 12 6.437752
## 756 2 11 11 6.579251
## 757 2 12 12 6.751101
## 758 2 12 12 7.130899
## 759 2 18 15 7.029973
## 760 NA 13 13 6.802395
## 761 1 4 NA 7.562161
## 762 1 12 16 6.869014
## 763 NA NA NA 7.535830
## 764 3 11 8 7.360740
## 765 2 12 10 6.845880
## 766 2 12 12 6.802395
## 767 3 8 10 6.782192
## 768 1 12 12 7.444249
## 769 2 9 8 7.138867
## 770 1 10 8 6.621406
## 771 1 12 11 7.000334
## 772 6 12 NA 6.908755
## 773 1 12 9 7.050990
## 774 4 12 8 6.907755
## 775 NA 6 2 7.130899
## 776 1 8 8 6.753438
## 777 2 16 12 7.099202
## 778 NA 16 12 6.842683
## 779 NA 12 NA 6.806829
## 780 1 12 18 6.770790
## 781 3 NA 8 7.090077
## 782 1 6 8 7.139660
## 783 1 8 2 6.856462
## 784 6 10 11 4.744932
## 785 5 16 12 6.511745
## 786 2 16 16 6.988413
## 787 1 15 9 6.964136
## 788 6 7 8 6.684612
## 789 4 NA NA 5.910797
## 790 1 12 NA 7.562161
## 791 1 7 6 6.751101
## 792 1 12 12 6.761573
## 793 4 13 13 6.995766
## 794 NA 10 12 6.932448
## 795 2 17 NA 6.745236
## 796 2 12 12 6.511745
## 797 2 10 12 6.684612
## 798 3 10 12 6.955593
## 799 2 14 9 6.784457
## 800 3 12 8 6.684612
## 801 1 8 8 7.090077
## 802 6 12 12 6.498282
## 803 3 NA NA 7.013016
## 804 2 NA NA 6.745236
## 805 2 8 12 6.907755
## 806 NA 14 14 6.774224
## 807 2 12 12 7.091742
## 808 3 12 12 6.501290
## 809 1 12 7 6.827629
## 810 5 10 3 6.669498
## 811 9 NA 6 6.856462
## 812 4 NA NA 7.273787
## 813 5 6 0 6.763885
## 814 7 4 NA 6.437752
## 815 1 6 7 6.522093
## 816 2 12 8 6.541030
## 817 9 6 NA 6.774224
## 818 1 12 13 7.744137
## 819 3 10 6 7.086738
## 820 NA 2 8 6.551080
## 821 2 12 4 6.580639
## 822 3 12 8 7.313221
## 823 1 16 9 6.956545
## 824 1 NA NA 6.685861
## 825 NA NA NA 6.461468
## 826 2 12 12 7.088409
## 827 NA 16 13 5.966147
## 828 2 9 12 6.790097
## 829 1 12 9 6.981006
## 830 2 14 12 7.027315
## 831 1 12 9 6.620073
## 832 1 NA 12 6.751101
## 833 1 10 12 6.263398
## 834 2 10 12 7.006695
## 835 2 8 9 6.315358
## 836 1 10 10 6.684612
## 837 1 13 14 7.232733
## 838 1 14 12 6.052089
## 839 6 2 10 6.234411
## 840 2 3 4 5.913503
## 841 8 2 8 6.390241
## 842 2 NA 10 5.996452
## 843 1 9 10 6.035481
## 844 1 12 NA 7.032624
## 845 NA 9 10 6.523562
## 846 4 10 6 6.856462
## 847 1 5 6 6.396930
## 848 2 NA 5 6.493754
## 849 2 6 NA 6.829794
## 850 1 5 5 6.309918
## 851 4 3 7 6.406880
## 852 4 3 7 6.052089
## 853 3 8 18 5.828946
## 854 2 7 5 6.539586
## 855 2 8 12 6.354370
## 856 1 8 6 6.347389
## 857 2 0 5 6.705639
## 858 2 12 12 6.894670
## 859 5 9 NA 6.423247
## 860 1 14 10 6.933423
## 861 2 8 7 6.694562
## 862 2 15 15 6.694562
## 863 1 12 12 6.669498
## 864 1 12 9 6.745236
## 865 NA 3 2 6.802395
## 866 3 8 8 7.257003
## 867 3 NA NA 5.560682
## 868 1 5 5 6.495265
## 869 1 12 11 6.331502
## 870 NA 12 12 6.331502
## 871 7 NA NA 5.877736
## 872 1 12 10 6.916715
## 873 1 9 12 7.273787
## 874 7 10 11 6.478509
## 875 NA 3 NA 6.620073
## 876 1 10 NA 6.625392
## 877 3 NA NA 6.551080
## 878 NA 9 9 6.220590
## 879 2 12 9 6.842683
## 880 3 10 6 6.436151
## 881 1 10 6 6.620073
## 882 2 10 6 6.802395
## 883 NA 10 7 6.291569
## 884 10 4 10 6.464588
## 885 NA 12 NA 5.991465
## 886 3 12 10 6.802395
## 887 2 12 10 6.240276
## 888 8 9 8 6.795706
## 889 2 12 18 7.156177
## 890 3 12 18 6.184149
## 891 2 10 13 5.783825
## 892 1 7 7 6.645091
## 893 8 7 4 6.426488
## 894 7 7 3 6.946976
## 895 3 8 3 6.621406
## 896 1 7 7 5.940171
## 897 1 3 NA 5.703783
## 898 2 12 7 6.624065
## 899 3 11 12 6.970730
## 900 2 14 12 6.975414
## 901 1 14 18 7.360740
## 902 1 12 9 6.476973
## 903 2 10 10 6.551080
## 904 NA NA NA 6.202536
## 905 1 NA NA 6.791222
## 906 1 6 NA 6.253829
## 907 5 12 9 6.792345
## 908 3 12 NA 6.345636
## 909 1 11 9 7.275172
## 910 3 NA NA 6.175867
## 911 NA 12 7 6.214608
## 912 2 12 14 7.295056
## 913 4 10 12 6.688354
## 914 1 12 12 6.869014
## 915 1 11 NA 6.907755
## 916 1 12 12 6.396930
## 917 3 8 8 6.109248
## 918 3 10 6 6.444131
## 919 NA 8 NA 6.198479
## 920 1 12 13 7.353722
## 921 2 13 16 5.877736
## 922 NA 10 NA 6.866933
## 923 2 12 12 6.338594
## 924 2 12 8 6.175867
## 925 2 8 8 7.273787
## 926 7 7 8 6.469250
## 927 NA 9 NA 6.669498
## 928 3 12 NA 6.467699
## 929 3 7 7 6.167517
## 930 4 16 16 6.498282
## 931 1 11 NA 6.253829
## 932 7 8 6 7.091742
## 933 NA 7 NA 6.287858
## 934 1 NA 11 6.771935
## 935 1 NA NA 6.907755mean(wage)## [1] 957.9455mean(IQ)## [1] 101.2824sd(IQ)## [1] 15.05264modelnew<-lm(wage~IQ)
summary(Datanew)## wage hours IQ KWW
## Min. : 115.0 Min. :20.00 Min. : 50.0 Min. :12.00
## 1st Qu.: 669.0 1st Qu.:40.00 1st Qu.: 92.0 1st Qu.:31.00
## Median : 905.0 Median :40.00 Median :102.0 Median :37.00
## Mean : 957.9 Mean :43.93 Mean :101.3 Mean :35.74
## 3rd Qu.:1160.0 3rd Qu.:48.00 3rd Qu.:112.0 3rd Qu.:41.00
## Max. :3078.0 Max. :80.00 Max. :145.0 Max. :56.00
##
## educ exper tenure age
## Min. : 9.00 Min. : 1.00 Min. : 0.000 Min. :28.00
## 1st Qu.:12.00 1st Qu.: 8.00 1st Qu.: 3.000 1st Qu.:30.00
## Median :12.00 Median :11.00 Median : 7.000 Median :33.00
## Mean :13.47 Mean :11.56 Mean : 7.234 Mean :33.08
## 3rd Qu.:16.00 3rd Qu.:15.00 3rd Qu.:11.000 3rd Qu.:36.00
## Max. :18.00 Max. :23.00 Max. :22.000 Max. :38.00
##
## married black south urban
## Min. :0.000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:1.000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :1.000 Median :0.0000 Median :0.0000 Median :1.0000
## Mean :0.893 Mean :0.1283 Mean :0.3412 Mean :0.7176
## 3rd Qu.:1.000 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.000 Max. :1.0000 Max. :1.0000 Max. :1.0000
##
## sibs brthord meduc feduc
## Min. : 0.000 Min. : 1.000 Min. : 0.00 Min. : 0.00
## 1st Qu.: 1.000 1st Qu.: 1.000 1st Qu.: 8.00 1st Qu.: 8.00
## Median : 2.000 Median : 2.000 Median :12.00 Median :10.00
## Mean : 2.941 Mean : 2.277 Mean :10.68 Mean :10.22
## 3rd Qu.: 4.000 3rd Qu.: 3.000 3rd Qu.:12.00 3rd Qu.:12.00
## Max. :14.000 Max. :10.000 Max. :18.00 Max. :18.00
## NA's :83 NA's :78 NA's :194
## lwage
## Min. :4.745
## 1st Qu.:6.506
## Median :6.808
## Mean :6.779
## 3rd Qu.:7.056
## Max. :8.032
## rm(Datanew, model1, modelnew)## Warning in rm(Datanew, model1, modelnew): object 'model1' not foundlibrary(wooldridge)
Data4<-wooldridge::wage2
attach(Data4)## The following objects are masked from Datanew:
##
## age, black, brthord, educ, exper, feduc, hours, IQ, KWW, lwage,
## married, meduc, sibs, south, tenure, urban, wagemean("wage")## Warning in mean.default("wage"): argument is not numeric or logical: returning
## NA## [1] NAmean("IQ")## Warning in mean.default("IQ"): argument is not numeric or logical: returning NA## [1] NAsd("IQ")## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
## na.rm): NAs introduced by coercion## [1] NAmodel<-lm(wage~IQ)
summary(model)##
## Call:
## lm(formula = wage ~ IQ)
##
## Residuals:
## Min 1Q Median 3Q Max
## -898.7 -256.5 -47.3 201.1 2072.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 116.9916 85.6415 1.366 0.172
## IQ 8.3031 0.8364 9.927 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 384.8 on 933 degrees of freedom
## Multiple R-squared: 0.09554, Adjusted R-squared: 0.09457
## F-statistic: 98.55 on 1 and 933 DF, p-value: < 2.2e-16