the raw data

employee wage hours IQ KW educ exper tenure age marriage gender urban sibs brthord meduc feduc 1 769 40 93 35 12 11 2 31 married Female Y 1 2 8 8 2 . 50 119 41 18 11 16 37 married Male Y 1 . 14 14 3 825 40 108 46 14 11 9 33 married Male Y 1 2 14 14 4 650 40 96 32 12 13 7 32 married Male Y 4 3 12 12 5 562 40 74 27 11 14 5 34 married Male Y 10 6 6 11 6 1400 40 116 43 16 14 2 35 married Male Y 1 2 8 . 7 600 40 91 24 10 13 0 30 single Male Y 1 2 8 8 8 1081 40 114 50 18 8 14 38 married Male Y 2 3 8 . 9 1154 45 111 37 15 13 1 36 married Male N 2 3 14 5 10 1000 40 95 44 12 16 16 36 married Male Y 1 1 12 11 11 930 43 132 44 18 8 13 38 married Male N 1 1 13 14 12 921 38 102 45 14 9 11 33 married Male N 1 2 16 . 13 900 45 125 40 . 4 3 30 single Male N 2 . 12 12 14 1318 38 119 24 16 7 2 28 married Male Y 3 1 10 10 15 1792 40 118 47 16 9 9 34 married Female Y 1 1 12 12 16 958 50 105 37 10 17 2 35 married Female Y 1 2 6 8 17 1360 45 109 39 15 6 9 36 married Female Y 3 3 12 10 18 850 40 72 36 11 19 10 38 married Male Y 2 3 10 8 19 830 44 105 29 14 4 7 29 married Male Y 3 1 12 . 20 471 . 101 34 12 13 7 31 married Female Y 3 2 8 . 21 1275 40 123 37 14 9 1 31 married Female Y 0 1 12 . 22 1615 50 113 49 16 10 4 36 married Male Y 2 1 12 12 23 873 65 95 36 12 14 3 38 married Female Y 5 1 10 10 24 2137 45 145 50 16 17 8 38 married Male Y 2 1 12 12 25 1053 38 114 35 16 12 7 32 married Male Y 0 1 11 11 26 1602 60 124 32 16 8 9 29 married Male Y 3 1 16 16 27 1188 40 93 40 13 16 5 35 married Male Y 2 1 12 8 28 800 40 115 39 18 11 1 35 married Male Y 0 1 8 8 29 1417 48 125 41 17 9 4 34 married Male Y 3 1 13 12 30 635 40 128 35 18 8 13 36 single Male Y 2 1 16 16 31 1000 40 . 40 12 17 2 34 married Female Y 1 1 8 . 32 1424 50 98 41 14 15 4 35 married Male Y 1 1 12 12 33 2668 75 108 41 13 12 2 32 married Female Y 1 2 12 12 34 666 75 129 40 18 8 12 38 married Male N 2 1 18 18 35 1779 40 132 42 17 8 9 32 married Female Y 1 1 17 14 36 782 40 92 24 13 9 10 31 married Male Y 3 3 8 . 37 1572 35 108 39 14 7 5 28 married Male Y 1 . 12 8 38 1274 40 106 35 13 11 12 31 married Female Y 0 1 15 12 39 714 35 105 37 16 9 2 29 married Male Y 1 2 13 12 40 1081 40 123 46 18 11 7 36 married Male N 1 1 12 12 41 692 40 108 48 12 21 11 38 married Male Y 3 1 8 7 42 1318 . 122 47 17 10 9 33 married Male Y 1 . 12 8 43 1239 45 109 44 12 16 4 38 married Male Y 3 3 12 . 44 1027 27 100 40 13 12 15 38 married Male Y 3 3 10 9 45 1748 55 125 50 18 12 12 34 married Female Y 1 2 14 15 46 981 40 122 41 14 9 15 35 single Male Y 3 2 12 . 47 770 40 105 34 17 7 11 30 married Male Y 3 1 12 12 48 1154 50 94 37 16 11 5 33 married Female Y 1 . 12 12 49 1155 40 102 36 12 16 5 34 married Male Y 1 2 12 . 50 808 60 109 47 13 14 12 38 married Male Y 2 2 12 10 51 1100 40 105 45 12 22 14 38 married Female Y 1 1 12 12 52 1154 40 134 46 18 10 10 37 married Female Y 2 1 12 12 53 1749 40 108 32 12 13 9 29 single Female Y 7 1 12 9 54 1000 50 104 36 16 11 8 30 married Female Y 1 1 12 11 55 462 40 112 35 12 11 1 29 married Female Y 8 3 12 . 56 769 55 120 49 18 8 12 32 single Female Y 1 1 11 8 57 875 50 124 40 16 9 3 31 single Female N 2 2 17 16 58 1375 40 103 34 12 12 7 29 married Female Y 4 2 12 10 59 1452 45 115 45 17 1 7 30 married Male Y 3 1 12 14 60 800 40 96 33 12 17 12 35 married Female Y 1 2 6 8 61 1748 55 123 48 17 10 7 36 married Female Y 1 1 12 12 62 1151 40 98 34 12 12 12 29 married Male Y 3 4 12 8 63 840 50 96 34 12 18 13 35 married Male Y 2 1 12 8 64 978 45 89 47 12 17 2 38 married Female Y 0 1 12 . 65 963 40 109 43 12 15 15 37 single Male N 5 . . 7 66 619 42 93 20 12 10 10 28 single Male Y 8 . 9 11 67 442 60 82 32 12 12 0 33 married Male Y 2 3 12 9 68 600 40 120 45 15 12 1 38 married Male N 3 4 12 . 69 1366 38 122 38 12 15 15 32 married Female Y 1 2 12 . 70 1643 48 117 48 18 8 2 33 married Male Y 2 2 12 18 71 1455 40 109 44 16 8 13 36 married Male Y 1 2 8 8 72 2310 40 114 55 18 12 7 38 married Male Y 2 . 12 16 73 1682 45 126 40 16 10 11 31 married Male Y 1 1 12 16 74 1235 40 82 45 12 10 2 33 married Female Y 1 1 11 11 75 855 45 119 45 16 11 4 32 married Female Y 3 1 12 9 76 1072 42 104 37 16 6 8 35 married Male Y 2 2 12 6 77 1040 40 115 49 15 8 15 35 married Female Y 3 4 . 11 78 1000 50 97 45 12 17 15 36 married Male Y 1 2 12 12 79 675 40 105 . 15 8 2 31 married Male N 3 . 14 12 80 1100 37 100 32 12 14 1 30 married Female Y 1 2 7 7 81 996 50 114 41 12 5 14 31 married Male Y 2 1 12 10 82 732 40 73 28 10 17 5 36 married Female Y 5 6 . . 83 1200 40 96 48 11 15 3 37 married Female Y 4 3 8 8 84 1694 30 113 30 15 9 8 29 single Male Y 3 1 12 16 85 686 55 106 38 10 15 3 36 married Female Y 4 5 14 10 86 754 40 104 37 13 9 5 32 married Male Y 1 2 8 . 87 857 35 80 39 10 14 15 33 married Female Y 2 1 . 12 88 832 50 104 30 13 4 2 30 married Female Y 1 2 12 8 89 579 44 122 36 13 8 1 30 married Female N 4 1 16 16 90 672 61 96 38 12 13 3 34 married Male Y 2 . 12 13 91 2500 40 95 51 15 12 9 36 married Male Y 3 1 16 16 92 1076 45 105 24 12 5 8 31 single Male Y 1 1 16 17 93 750 40 94 18 12 16 3 37 married Female Y 7 3 8 8 94 1186 30 91 41 17 8 9 33 married Male Y 1 1 12 14 95 833 48 96 37 16 9 7 31 married Female Y 4 4 12 14 96 650 40 69 37 12 14 11 30 married Female Y 3 4 10 9 97 1250 46 110 37 12 5 16 38 married Female N 1 1 . 12 98 1122 48 111 44 15 12 9 31 married Male Y 3 1 9 12 99 865 40 110 35 16 1 11 31 married Female Y 3 1 9 12 100 808 40 97 37 16 10 7 29 married Female Y 3 3 9 12 101 1299 60 125 44 18 6 12 35 single Female Y 1 2 8 8 102 903 40 91 39 12 6 12 36 married Female Y 5 4 9 10 103 900 40 86 28 12 17 15 37 married Male Y 5 3 8 10 104 625 40 110 32 12 18 0 35 married Female Y 4 1 9 8 105 1586 40 92 47 14 16 1 36 married Female Y 4 4 12 . 106 962 40 85 41 10 15 16 38 married Female Y 1 2 9 12 107 1539 45 120 36 17 7 8 34 married Male Y 1 1 8 8 108 1110 52 106 42 12 11 6 33 married Female N 5 2 12 12 109 1282 45 112 41 16 6 6 31 single Male Y 2 2 12 12 110 770 48 91 39 12 13 2 38 married Female Y 1 2 10 8 111 1000 45 90 42 12 16 0 38 married Male Y 6 3 10 12 112 895 40 86 39 9 17 16 38 married Male N 2 . . 12 113 1205 40 86 32 10 13 0 32 married Male Y 5 5 8 . 114 750 40 113 27 12 11 10 29 married Male N 1 . 12 12 115 654 50 111 51 13 5 16 38 married Female Y 3 3 12 9 116 601 48 111 30 12 15 3 33 married Male Y 0 1 12 12 117 600 40 106 32 13 10 11 28 married Female Y 6 . 8 7 118 433 40 98 28 12 11 10 30 single Male Y 1 1 12 11 119 1188 40 105 28 12 11 7 30 married Male Y 3 1 12 10 120 635 40 105 29 12 15 15 33 married Male N 2 2 10 . 121 1225 40 118 44 13 9 14 32 married Male Y 0 1 12 9 122 1151 45 90 38 12 19 19 36 married Male Y 1 1 12 8 123 865 40 95 34 12 15 11 32 single Female Y 3 1 9 8 124 1031 40 112 24 14 9 5 29 married Male Y 4 2 12 12 125 1049 55 120 39 12 9 11 32 married Female Y 3 2 12 10 126 1000 45 123 41 12 12 1 29 married Female Y 3 3 12 10 127 1105 40 103 32 13 10 11 30 married Female Y 1 1 16 15 128 1924 50 121 40 18 11 7 35 married Male Y 3 1 16 18 129 1346 40 90 44 16 9 16 38 married Male Y 1 1 12 12 130 809 65 125 32 12 11 10 29 married Male Y 0 1 12 12 131 1495 45 109 34 18 10 3 32 married Male Y 3 1 9 8 132 1346 50 128 46 16 10 1 33 single Female N 3 1 10 9 133 1200 40 97 35 12 10 4 31 married Male Y 3 1 12 12 134 500 40 96 35 12 6 0 33 married Female Y 4 4 12 12 135 1325 45 97 44 12 12 5 28 married Male Y 1 2 12 8 136 900 40 78 21 12 16 5 33 married Male Y 4 1 7 6 137 800 40 112 . 12 15 3 34 married Female Y 6 4 7 . 138 800 40 88 32 12 16 7 38 married Male Y 3 2 2 5 139 1034 40 97 39 12 15 16 36 married Female Y 14 10 14 11 140 980 40 101 41 12 4 12 36 married Female Y 1 . 6 9 141 884 40 106 42 10 19 12 34 married Male Y 2 1 7 10 142 480 40 59 28 10 11 5 34 single Male Y 0 1 8 6 143 923 40 105 54 18 7 14 34 married Female Y 1 1 12 12 144 513 45 119 30 17 8 5 31 single Male Y 1 1 12 8 145 1105 40 93 36 11 17 16 37 single Female Y 2 2 8 8 146 1193 50 82 38 12 19 10 36 married Female Y 0 1 12 . 147 2771 50 134 45 18 6 7 34 married Male Y 4 3 16 16 148 779 40 84 29 12 9 6 30 married Male Y 0 1 8 12 149 950 60 98 36 11 13 6 31 married Male Y 2 1 12 12 150 1394 40 118 41 16 11 8 30 married Female N 2 1 13 12 employee wage hours IQ KW educ exper tenure age marriage gender urban sibs brthord meduc feduc 1 769 40 93 35 12 11 2 31 married Female Y 1 2 8 8 2 . 50 119 41 18 11 16 37 married Male Y 1 . 14 14 3 825 40 108 46 14 11 9 33 married Male Y 1 2 14 14 4 650 40 96 32 12 13 7 32 married Male Y 4 3 12 12 5 562 40 74 27 11 14 5 34 married Male Y 10 6 6 11 6 1400 40 116 43 16 14 2 35 married Male Y 1 2 8 . 7 600 40 91 24 10 13 0 30 single Male Y 1 2 8 8 8 1081 40 114 50 18 8 14 38 married Male Y 2 3 8 . 9 1154 45 111 37 15 13 1 36 married Male N 2 3 14 5 10 1000 40 95 44 12 16 16 36 married Male Y 1 1 12 11 11 930 43 132 44 18 8 13 38 married Male N 1 1 13 14 12 921 38 102 45 14 9 11 33 married Male N 1 2 16 . 13 900 45 125 40 . 4 3 30 single Male N 2 . 12 12 14 1318 38 119 24 16 7 2 28 married Male Y 3 1 10 10 15 1792 40 118 47 16 9 9 34 married Female Y 1 1 12 12 16 958 50 105 37 10 17 2 35 married Female Y 1 2 6 8 17 1360 45 109 39 15 6 9 36 married Female Y 3 3 12 10 18 850 40 72 36 11 19 10 38 married Male Y 2 3 10 8 19 830 44 105 29 14 4 7 29 married Male Y 3 1 12 . 20 471 . 101 34 12 13 7 31 married Female Y 3 2 8 . 21 1275 40 123 37 14 9 1 31 married Female Y 0 1 12 . 22 1615 50 113 49 16 10 4 36 married Male Y 2 1 12 12 23 873 65 95 36 12 14 3 38 married Female Y 5 1 10 10 24 2137 45 145 50 16 17 8 38 married Male Y 2 1 12 12 25 1053 38 114 35 16 12 7 32 married Male Y 0 1 11 11 26 1602 60 124 32 16 8 9 29 married Male Y 3 1 16 16 27 1188 40 93 40 13 16 5 35 married Male Y 2 1 12 8 28 800 40 115 39 18 11 1 35 married Male Y 0 1 8 8 29 1417 48 125 41 17 9 4 34 married Male Y 3 1 13 12 30 635 40 128 35 18 8 13 36 single Male Y 2 1 16 16 31 1000 40 . 40 12 17 2 34 married Female Y 1 1 8 . 32 1424 50 98 41 14 15 4 35 married Male Y 1 1 12 12 33 2668 75 108 41 13 12 2 32 married Female Y 1 2 12 12 34 666 75 129 40 18 8 12 38 married Male N 2 1 18 18 35 1779 40 132 42 17 8 9 32 married Female Y 1 1 17 14 36 782 40 92 24 13 9 10 31 married Male Y 3 3 8 . 37 1572 35 108 39 14 7 5 28 married Male Y 1 . 12 8 38 1274 40 106 35 13 11 12 31 married Female Y 0 1 15 12 39 714 35 105 37 16 9 2 29 married Male Y 1 2 13 12 40 1081 40 123 46 18 11 7 36 married Male N 1 1 12 12 41 692 40 108 48 12 21 11 38 married Male Y 3 1 8 7 42 1318 . 122 47 17 10 9 33 married Male Y 1 . 12 8 43 1239 45 109 44 12 16 4 38 married Male Y 3 3 12 . 44 1027 27 100 40 13 12 15 38 married Male Y 3 3 10 9 45 1748 55 125 50 18 12 12 34 married Female Y 1 2 14 15 46 981 40 122 41 14 9 15 35 single Male Y 3 2 12 . 47 770 40 105 34 17 7 11 30 married Male Y 3 1 12 12 48 1154 50 94 37 16 11 5 33 married Female Y 1 . 12 12 49 1155 40 102 36 12 16 5 34 married Male Y 1 2 12 . 50 808 60 109 47 13 14 12 38 married Male Y 2 2 12 10 51 1100 40 105 45 12 22 14 38 married Female Y 1 1 12 12 52 1154 40 134 46 18 10 10 37 married Female Y 2 1 12 12 53 1749 40 108 32 12 13 9 29 single Female Y 7 1 12 9 54 1000 50 104 36 16 11 8 30 married Female Y 1 1 12 11 55 462 40 112 35 12 11 1 29 married Female Y 8 3 12 . 56 769 55 120 49 18 8 12 32 single Female Y 1 1 11 8 57 875 50 124 40 16 9 3 31 single Female N 2 2 17 16 58 1375 40 103 34 12 12 7 29 married Female Y 4 2 12 10 59 1452 45 115 45 17 1 7 30 married Male Y 3 1 12 14 60 800 40 96 33 12 17 12 35 married Female Y 1 2 6 8 61 1748 55 123 48 17 10 7 36 married Female Y 1 1 12 12 62 1151 40 98 34 12 12 12 29 married Male Y 3 4 12 8 63 840 50 96 34 12 18 13 35 married Male Y 2 1 12 8 64 978 45 89 47 12 17 2 38 married Female Y 0 1 12 . 65 963 40 109 43 12 15 15 37 single Male N 5 . . 7 66 619 42 93 20 12 10 10 28 single Male Y 8 . 9 11 67 442 60 82 32 12 12 0 33 married Male Y 2 3 12 9 68 600 40 120 45 15 12 1 38 married Male N 3 4 12 . 69 1366 38 122 38 12 15 15 32 married Female Y 1 2 12 . 70 1643 48 117 48 18 8 2 33 married Male Y 2 2 12 18 71 1455 40 109 44 16 8 13 36 married Male Y 1 2 8 8 72 2310 40 114 55 18 12 7 38 married Male Y 2 . 12 16 73 1682 45 126 40 16 10 11 31 married Male Y 1 1 12 16 74 1235 40 82 45 12 10 2 33 married Female Y 1 1 11 11 75 855 45 119 45 16 11 4 32 married Female Y 3 1 12 9 76 1072 42 104 37 16 6 8 35 married Male Y 2 2 12 6 77 1040 40 115 49 15 8 15 35 married Female Y 3 4 . 11 78 1000 50 97 45 12 17 15 36 married Male Y 1 2 12 12 79 675 40 105 . 15 8 2 31 married Male N 3 . 14 12 80 1100 37 100 32 12 14 1 30 married Female Y 1 2 7 7 81 996 50 114 41 12 5 14 31 married Male Y 2 1 12 10 82 732 40 73 28 10 17 5 36 married Female Y 5 6 . . 83 1200 40 96 48 11 15 3 37 married Female Y 4 3 8 8 84 1694 30 113 30 15 9 8 29 single Male Y 3 1 12 16 85 686 55 106 38 10 15 3 36 married Female Y 4 5 14 10 86 754 40 104 37 13 9 5 32 married Male Y 1 2 8 . 87 857 35 80 39 10 14 15 33 married Female Y 2 1 . 12 88 832 50 104 30 13 4 2 30 married Female Y 1 2 12 8 89 579 44 122 36 13 8 1 30 married Female N 4 1 16 16 90 672 61 96 38 12 13 3 34 married Male Y 2 . 12 13 91 2500 40 95 51 15 12 9 36 married Male Y 3 1 16 16 92 1076 45 105 24 12 5 8 31 single Male Y 1 1 16 17 93 750 40 94 18 12 16 3 37 married Female Y 7 3 8 8 94 1186 30 91 41 17 8 9 33 married Male Y 1 1 12 14 95 833 48 96 37 16 9 7 31 married Female Y 4 4 12 14 96 650 40 69 37 12 14 11 30 married Female Y 3 4 10 9 97 1250 46 110 37 12 5 16 38 married Female N 1 1 . 12 98 1122 48 111 44 15 12 9 31 married Male Y 3 1 9 12 99 865 40 110 35 16 1 11 31 married Female Y 3 1 9 12 100 808 40 97 37 16 10 7 29 married Female Y 3 3 9 12 101 1299 60 125 44 18 6 12 35 single Female Y 1 2 8 8 102 903 40 91 39 12 6 12 36 married Female Y 5 4 9 10 103 900 40 86 28 12 17 15 37 married Male Y 5 3 8 10 104 625 40 110 32 12 18 0 35 married Female Y 4 1 9 8 105 1586 40 92 47 14 16 1 36 married Female Y 4 4 12 . 106 962 40 85 41 10 15 16 38 married Female Y 1 2 9 12 107 1539 45 120 36 17 7 8 34 married Male Y 1 1 8 8 108 1110 52 106 42 12 11 6 33 married Female N 5 2 12 12 109 1282 45 112 41 16 6 6 31 single Male Y 2 2 12 12 110 770 48 91 39 12 13 2 38 married Female Y 1 2 10 8 111 1000 45 90 42 12 16 0 38 married Male Y 6 3 10 12 112 895 40 86 39 9 17 16 38 married Male N 2 . . 12 113 1205 40 86 32 10 13 0 32 married Male Y 5 5 8 . 114 750 40 113 27 12 11 10 29 married Male N 1 . 12 12 115 654 50 111 51 13 5 16 38 married Female Y 3 3 12 9 116 601 48 111 30 12 15 3 33 married Male Y 0 1 12 12 117 600 40 106 32 13 10 11 28 married Female Y 6 . 8 7 118 433 40 98 28 12 11 10 30 single Male Y 1 1 12 11 119 1188 40 105 28 12 11 7 30 married Male Y 3 1 12 10 120 635 40 105 29 12 15 15 33 married Male N 2 2 10 . 121 1225 40 118 44 13 9 14 32 married Male Y 0 1 12 9 122 1151 45 90 38 12 19 19 36 married Male Y 1 1 12 8 123 865 40 95 34 12 15 11 32 single Female Y 3 1 9 8 124 1031 40 112 24 14 9 5 29 married Male Y 4 2 12 12 125 1049 55 120 39 12 9 11 32 married Female Y 3 2 12 10 126 1000 45 123 41 12 12 1 29 married Female Y 3 3 12 10 127 1105 40 103 32 13 10 11 30 married Female Y 1 1 16 15 128 1924 50 121 40 18 11 7 35 married Male Y 3 1 16 18 129 1346 40 90 44 16 9 16 38 married Male Y 1 1 12 12 130 809 65 125 32 12 11 10 29 married Male Y 0 1 12 12 131 1495 45 109 34 18 10 3 32 married Male Y 3 1 9 8 132 1346 50 128 46 16 10 1 33 single Female N 3 1 10 9 133 1200 40 97 35 12 10 4 31 married Male Y 3 1 12 12 134 500 40 96 35 12 6 0 33 married Female Y 4 4 12 12 135 1325 45 97 44 12 12 5 28 married Male Y 1 2 12 8 136 900 40 78 21 12 16 5 33 married Male Y 4 1 7 6 137 800 40 112 . 12 15 3 34 married Female Y 6 4 7 . 138 800 40 88 32 12 16 7 38 married Male Y 3 2 2 5 139 1034 40 97 39 12 15 16 36 married Female Y 14 10 14 11 140 980 40 101 41 12 4 12 36 married Female Y 1 . 6 9 141 884 40 106 42 10 19 12 34 married Male Y 2 1 7 10 142 480 40 59 28 10 11 5 34 single Male Y 0 1 8 6 143 923 40 105 54 18 7 14 34 married Female Y 1 1 12 12 144 513 45 119 30 17 8 5 31 single Male Y 1 1 12 8 145 1105 40 93 36 11 17 16 37 single Female Y 2 2 8 8 146 1193 50 82 38 12 19 10 36 married Female Y 0 1 12 . 147 2771 50 134 45 18 6 7 34 married Male Y 4 3 16 16 148 779 40 84 29 12 9 6 30 married Male Y 0 1 8 12 149 950 60 98 36 11 13 6 31 married Male Y 2 1 12 12 150 1394 40 118 41 16 11 8 30 married Female N 2 1 13 12

DATA DESCRIPTION (A FICTITIOUS DATASET DESIGNED FOR THE ASSIGNMENT ONLY) A consulting firm randomly selected 150 young employees in Tasmania. These selected employees answered questions and undertook a standard IQ test and a KW test. The KW test examines respondents' knowledge about the duties in their workplaces and the knowledge about the Australian and Tasmanian labour markets. Respondents' answers are entered a spreadsheet where each column represents a variable. These variables include:

1. wage: monthly earnings in dollars

2. hours: average weekly working hours

3. IQ: IQ score

4. KW: knowledge of work score

5. educ: years of education

6. exper: years of work experience

7. tenure: years with the current employer

8. age: age in years

9. marriage: marriage status

10. gender: female or male

11. urban: =Y if lives in urban areas =N if lives in rural areas

12. sibs: the number of siblings

13. brthord: birth order, e.g. =2 means he/she is the second child in the family.

14. meduc: mother's education

15. feduc: father's education

The missing values are shown by a "." in the cells.

Questions: 1. Read the provided raw data carefully to check whether all respondents have provided information for each variable. Explain what you have done to manage the missing data. Clearly indicate the final number of observations (respondents) you will use in the following analysis. Submit an electronic copy of the Excel spreadsheet of the final dataset together with your assignment. All your following analysis should be based on this final dataset. [10 marks]

2. Pick up two numerical variables and two categorical variables and then describe each of them one by one. Use appropriate tables/graphs and numerical measures to help you describe the distribution of the variables. [10 marks]

3. It's often asked what factors relate to IQ score and KW score. Look through your data and first pick up one numerical variable that you think may relate to IQ score. Explain why you pick up this variable. Then use an appropriate graph and an appropriate numerical measure to discuss the empirical relationship between IQ score and this numerical variable. Repeat the same exercise for the relationship between KW score and a numerical variable to which you think KWmay relate. [15 marks]

4. You want to look at the relationship between gender and wages. However, you notice that gender is a categorical variable and wage is a numerical variable. One way to work on two different types of variables is to transform one variable to the type of the other. You decide to generate a categorical variable based on the level of wage, and this categorical variable has two values, "high" and "low". For example, you choose a threshold value for wage, and if a respondent's wage is no less than the threshold value, you enter "high" and enter "low" otherwise.

a. Describe in detail how you have decided the threshold value for generating the new categorical variable for the level of wage. Then use an appropriate graph to present this variable. (Hint: you may choose to use an appropriate numerical measure of wage as the threshold value). [6 marks]

b. Present these two categorical variables together using an appropriate graph, and then discuss what the graph shows. [4 marks]

c. Produce a contingency table to present these two categorical variables. Based on the contingency table, calculate the related (empirical) joint and marginal probabilities. You may find helpful to produce another contingency table to show your calculated probabilities. (Hint: you may need Excel skills -- e.g. use the commands such as "sort" or "countif" to count the relevant frequencies, or use PivotTable function) [8 marks] d. Based on the sample information, calculate the probability of either being a female or getting a low wage level, and calculate the probability of being a female conditional on getting a low wage level [5 marks]

e. Examine whether the statement "Males tend to receive high wages than females" is true, false or inconlusive based on the sample information. Explain your response. [5 marks] [Total Marks 28]

5. Suppose that the population average of (monthly) wage of young employees in Tasmania in the previous year before this survey was conducted was $900.

f. Conduct a hypothesis test that the population average wage of young employees in Tasmania during the year of survey remains the same as in the previous year. [7 marks]

g. Construct a 95% confidence estimate for the population average wage, and comment whether the population average wage in the year of survey remains the same as in the previous year. [5marks] [Total Marks 12]

6. You want to use the collected data to study what is the most important factor that affects young employees wage in Tasmania. Use simple regression analysis to answer the following questions. (For each regression you run, show the Excel regression output and report the regression equation. Partial marks from the following questions assign to your regression results.).

a. Do the years of education have significant impact on the wages? (You need to explain the choice of the null and the alternative hypotheses.) [5 marks]

b. Do the IQ scores have significant impact on the wages? (You need to explain the choice of the null and the alternative hypotheses.) [5 marks]

c. Which of the two variables is a better predictor for the wage, years of education or IQ scores? Explain why. [3 marks]

d. Do the years of work experience have significant impact on the wages? (You need to explain the choice of the null and the alternative hypotheses. [3 marks]

e. Do the KW scores have significant impact on the wages? (You need to explain the choice of the null and the alternative hypotheses.) [3 marks]

f. Which of the two variables is a better predictor for the wages, years of work experience or KW scores? Explain why. [3 marks]

g. Newspapers often criticize a weak link between wage and education comparing with the link between wage and work experience. Discuss if the criticism is consistent with our data. [3 marks] [Total Marks 25]

The end of question