Dear Friends,
I have a child nutrition data for 3 points of year and 6 regions. my dependent variable is underweight of children. this variable includes 4 categories like normal, moderate , mild and severe undernurished. so i have to run a regression against underweight with all independent variables. so please can you able to suggest which proc i need to use?
because i have to do regression for region wise or can i append all regions then run? please clarify on this i'm in a trouble your help is highly appreciated.
hanks and regards,
Anil
Please provide your sample data.
Hi Ravi,
Here i didn't find the attachement option so i copied and paste here please convert this to excel.
thanks
Anil
under_weight | HHID | HV003 | HV004 | HV005 | HV006 | HV007 | HV008 | HV009 | HV010 | HV011 | HV012 | HV013 | HV014 | HV015 | HV016 | HV017 | HV019 | HV020 | HV023 | HV024 | wealth index |
4 | 10 8 100 | 2 | 11 | 1107121 | 11 | 92 | 1115 | 7 | 1 | 0 | 7 | 5 | 2 | 1 | 18 | 1 | 3 | 1 | 0 | 10 | 3 |
3 | 10 15 100 | 2 | 18 | 1107121 | 11 | 92 | 1115 | 7 | 1 | 0 | 7 | 7 | 2 | 1 | 5 | 2 | 3 | 1 | 0 | 10 | 2 |
2 | 10 17 100 | 2 | 20 | 1107121 | 11 | 92 | 1115 | 3 | 1 | 0 | 3 | 3 | 0 | 1 | 7 | 3 | 2 | 1 | 0 | 10 | 3 |
2 | 10 76 100 | 2 | 79 | 1107121 | 2 | 93 | 1118 | 2 | 1 | 0 | 2 | 2 | 0 | 1 | 5 | 1 | 2 | 1 | 0 | 10 | 3 |
2 | 10 89 100 | 2 | 92 | 1107121 | 1 | 93 | 1117 | 6 | 1 | 0 | 5 | 6 | 1 | 1 | 8 | 1 | 1 | 1 | 0 | 10 | 5 |
2 | 10 91 100 | 2 | 94 | 1107121 | 2 | 93 | 1118 | 5 | 1 | 0 | 5 | 3 | 1 | 1 | 14 | 1 | 3 | 1 | 0 | 10 | 4 |
2 | 10105 100 | 2 | 108 | 1107121 | 2 | 93 | 1118 | 4 | 1 | 0 | 4 | 4 | 0 | 1 | 13 | 1 | 3 | 1 | 0 | 10 | 5 |
2 | 10126 100 | 2 | 129 | 1107121 | 11 | 92 | 1115 | 6 | 0 | 0 | 6 | 6 | 0 | 1 | 17 | 1 | 1 | 1 | 0 | 10 | 4 |
2 | 10126 100 | 2 | 129 | 1107121 | 11 | 92 | 1115 | 6 | 0 | 0 | 6 | 6 | 0 | 1 | 17 | 1 | 1 | 1 | 0 | 10 | 4 |
2 | 10126 100 | 2 | 129 | 1107121 | 11 | 92 | 1115 | 6 | 0 | 0 | 6 | 6 | 0 | 1 | 17 | 1 | 1 | 1 | 0 | 10 | 4 |
1 | 10 24 101 | 2 | 27 | 1107121 | 12 | 92 | 1116 | 5 | 1 | 0 | 5 | 5 | 0 | 1 | 9 | 1 | 3 | 1 | 0 | 10 | 4 |
2 | 10 25 101 | 2 | 28 | 1107121 | 12 | 92 | 1116 | 7 | 2 | 0 | 6 | 7 | 0 | 1 | 11 | 1 | 3 | 1 | 0 | 10 | 3 |
2 | 10 37 101 | 2 | 40 | 1107121 | 1 | 93 | 1117 | 6 | 1 | 0 | 6 | 6 | 1 | 1 | 5 | 1 | 2 | 1 | 0 | 10 | 2 |
2 | 10 49 101 | 2 | 52 | 1107121 | 1 | 93 | 1117 | 8 | 1 | 0 | 8 | 8 | 0 | 1 | 25 | 1 | 1 | 1 | 0 | 10 | 4 |
2 | 10 51 101 | 2 | 54 | 1107121 | 1 | 93 | 1117 | 5 | 1 | 0 | 5 | 5 | 0 | 1 | 30 | 1 | 1 | 1 | 0 | 10 | 4 |
2 | 10 59 101 | 2 | 62 | 1107121 | 1 | 93 | 1117 | 4 | 1 | 0 | 4 | 4 | 0 | 1 | 9 | 1 | 1 | 1 | 0 | 10 | 3 |
2 | 10 87 10 | 12 | 90 | 1107121 | 2 | 93 | 1118 | 13 | 2 | 0 | 13 | 13 | 3 | 1 | 23 | 1 | 2 | 1 | 0 | 10 | 4 |
2 | 10 87 10 | 12 | 90 | 1107121 | 2 | 93 | 1118 | 13 | 2 | 0 | 13 | 13 | 3 | 1 | 23 | 1 | 2 | 1 | 0 | 10 | 4 |
2 | 10 87 10 | 12 | 90 | 1107121 | 2 | 93 | 1118 | 13 | 2 | 0 | 13 | 13 | 3 | 1 | 23 | 1 | 2 | 1 | 0 | 10 | 4 |
2 | 10 87 10 | 12 | 90 | 1107121 | 2 | 93 | 1118 | 13 | 2 | 0 | 13 | 13 | 3 | 1 | 23 | 1 | 2 | 1 | 0 | 10 | 4 |
4 | 10 19 10 | 2 | 22 | 1107121 | 11 | 92 | 1115 | 4 | 1 | 0 | 4 | 4 | 0 | 1 | 18 | 3 | 1 | 1 | 0 | 10 | 4 |
4 | 10 48 10 | 2 | 51 | 1107121 | 1 | 93 | 1117 | 7 | 2 | 0 | 7 | 7 | 2 | 1 | 28 | 1 | 2 | 1 | 0 | 10 | 3 |
3 | 10 49 10 | 2 | 52 | 1107121 | 1 | 93 | 1117 | 7 | 1 | 0 | 7 | 7 | 2 | 1 | 25 | 1 | 1 | 1 | 0 | 10 | 3 |
3 | 10 74 10 | 2 | 77 | 1107121 | 1 | 93 | 1117 | 7 | 1 | 0 | 7 | 7 | 2 | 1 | 26 | 1 | 1 | 1 | 0 | 10 | 1 |
4 | 10 93 10 | 2 | 96 | 1107121 | 2 | 93 | 1118 | 8 | 1 | 0 | 8 | 8 | 2 | 1 | 20 | 1 | 3 | 1 | 0 | 10 | 4 |
2 | 10126 10 | 2 | 129 | 1107121 | 11 | 92 | 1115 | 8 | 1 | 0 | 8 | 8 | 3 | 1 | 14 | 1 | 1 | 1 | 0 | 10 | 4 |
4 | 10138 10 | 2 | 141 | 1107121 | 1 | 93 | 1117 | 6 | 1 | 0 | 6 | 6 | 0 | 1 | 20 | 1 | 1 | 1 | 0 | 10 | 5 |
4 | 10138 10 | 2 | 141 | 1107121 | 1 | 93 | 1117 | 6 | 1 | 0 | 6 | 6 | 0 | 1 | 20 | 1 | 1 | 1 | 0 | 10 | 5 |
4 | 10138 10 | 2 | 141 | 1107121 | 1 | 93 | 1117 | 6 | 1 | 0 | 6 | 6 | 0 | 1 | 20 | 1 | 1 | 1 | 0 | 10 | 5 |
4 | 10138 10 | 2 | 141 | 1107121 | 1 | 93 | 1117 | 6 | 1 | 0 | 6 | 6 | 0 | 1 | 20 | 1 | 1 | 1 | 0 | 10 | 5 |
4 | 10138 10 | 2 | 141 | 1107121 | 1 | 93 | 1117 | 6 | 1 | 0 | 6 | 6 | 0 | 1 | 20 | 1 | 1 | 1 | 0 | 10 | 5 |
2 | 10 56 102 | 2 | 59 | 1107121 | 11 | 92 | 1115 | 5 | 1 | 0 | 5 | 5 | 0 | 1 | 26 | 1 | 1 | 1 | 0 | 10 | 1 |
3 | 10 67 102 | 2 | 70 | 1107121 | 12 | 92 | 1116 | 3 | 1 | 0 | 3 | 3 | 1 | 1 | 17 | 1 | 1 | 1 | 0 | 10 | 2 |
2 | 10 87 102 | 2 | 90 | 1107121 | 2 | 93 | 1118 | 6 | 1 | 0 | 6 | 6 | 0 | 1 | 23 | 1 | 2 | 1 | 0 | 10 | 5 |
3 | 10143 102 | 2 | 146 | 1107121 | 1 | 93 | 1117 | 4 | 1 | 0 | 4 | 4 | 2 | 1 | 16 | 1 | 1 | 1 | 0 | 10 | 5 |
3 | 10143 102 | 2 | 146 | 1107121 | 1 | 93 | 1117 | 4 | 1 | 0 | 4 | 4 | 2 | 1 | 16 | 1 | 1 | 1 | 0 | 10 | 5 |
3 | 10143 102 | 2 | 146 | 1107121 | 1 | 93 | 1117 | 4 | 1 | 0 | 4 | 4 | 2 | 1 | 16 | 1 | 1 | 1 | 0 | 10 | 5 |
3 | 10143 102 | 2 | 146 | 1107121 | 1 | 93 | 1117 | 4 | 1 | 0 | 4 | 4 | 2 | 1 | 16 | 1 | 1 | 1 | 0 | 10 | 5 |
3 | 10143 102 | 2 | 146 | 1107121 | 1 | 93 | 1117 | 4 | 1 | 0 | 4 | 4 | 2 | 1 | 16 | 1 | 1 | 1 | 0 | 10 | 5 |
3 | 10 28 102 | 9 | 31 | 1107121 | 12 | 92 | 1116 | 12 | 2 | 0 | 12 | 12 | 1 | 1 | 2 | 1 | 2 | 1 | 0 | 10 | 4 |
4 | 10 3 10 | 3 | 6 | 1107121 | 11 | 92 | 1115 | 5 | 2 | 0 | 5 | 5 | 0 | 1 | 24 | 1 | 4 | 1 | 0 | 10 | 2 |
3 | 10125 10 | 3 | 128 | 1107121 | 11 | 92 | 1115 | 4 | 1 | 0 | 2 | 4 | 0 | 1 | 13 | 1 | 1 | 1 | 0 | 10 | 5 |
4 | 10 6 103 | 2 | 9 | 1107121 | 11 | 92 | 1115 | 7 | 3 | 0 | 7 | 7 | 2 | 1 | 28 | 1 | 1 | 1 | 0 | 10 | 1 |
1 | 10 81 103 | 4 | 84 | 1107121 | 1 | 93 | 1117 | 5 | 1 | 0 | 5 | 3 | 0 | 1 | 29 | 1 | 1 | 1 | 0 | 10 | 4 |
3 | 10 81 103 | 4 | 84 | 1107121 | 1 | 93 | 1117 | 5 | 1 | 0 | 5 | 3 | 0 | 1 | 29 | 1 | 1 | 1 | 0 | 10 | 4 |
2 | 10116 10 | 4 | 119 | 1107121 | 11 | 92 | 1115 | 6 | 1 | 0 | 6 | 6 | 2 | 1 | 7 | 1 | 2 | 1 | 0 | 10 | 3 |
2 | 10141 10 | 4 | 144 | 1107121 | 2 | 93 | 1118 | 11 | 2 | 0 | 11 | 11 | 1 | 1 | 4 | 1 | 2 | 1 | 0 | 10 | 5 |
2 | 10141 10 | 4 | 144 | 1107121 | 2 | 93 | 1118 | 11 | 2 | 0 | 11 | 11 | 1 | 1 | 4 | 1 | 2 | 1 | 0 | 10 | 5 |
2 | 10141 10 | 4 | 144 | 1107121 | 2 | 93 | 1118 | 11 | 2 | 0 | 11 | 11 | 1 | 1 | 4 | 1 | 2 | 1 | 0 | 10 | 5 |
2 | 10 18 104 | 1 | 21 | 1107121 | 12 | 92 | 1116 | 3 | 1 | 0 | 3 | 3 | 0 | 1 | 6 | 1 | 2 | 1 | 0 | 10 | 3 |
2 | 10 71 104 | 1 | 74 | 1107121 | 1 | 93 | 1117 | 5 | 1 | 0 | 5 | 5 | 3 | 1 | 2 | 1 | 1 | 1 | 0 | 10 | 2 |
2 | 10 78 104 | 1 | 81 | 1107121 | 1 | 93 | 1117 | 4 | 1 | 0 | 4 | 4 | 1 | 1 | 28 | 1 | 1 | 1 | 0 | 10 | 2 |
2 | 10 90 104 | 1 | 93 | 1107121 | 2 | 93 | 1118 | 6 | 1 | 0 | 6 | 4 | 0 | 1 | 7 | 1 | 2 | 1 | 0 | 10 | 5 |
2 | 10128 104 | 1 | 131 | 1107121 | 11 | 92 | 1115 | 4 | 0 | 0 | 4 | 4 | 0 | 1 | 10 | 1 | 2 | 1 | 0 | 10 | 3 |
2 | 10137 104 | 1 | 140 | 1107121 | 1 | 93 | 1117 | 3 | 1 | 0 | 3 | 3 | 1 | 1 | 19 | 1 | 2 | 1 | 0 | 10 | 4 |
3 | 10 27 104 | 2 | 30 | 1107121 | 11 | 92 | 1115 | 7 | 2 | 0 | 7 | 7 | 2 | 1 | 27 | 1 | 5 | 1 | 0 | 10 | 3 |
2 | 10 33 104 | 2 | 36 | 1107121 | 12 | 92 | 1116 | 6 | 2 | 0 | 6 | 6 | 2 | 1 | 30 | 1 | 1 | 1 | 0 | 10 | 3 |
4 | 10 34 104 | 2 | 37 | 1107121 | 1 | 93 | 1117 | 5 | 1 | 0 | 5 | 3 | 2 | 1 | 7 | 1 | 1 | 1 | 0 | 10 | 4 |
3 | 10 39 104 | 2 | 42 | 1107121 | 12 | 92 | 1116 | 5 | 1 | 0 | 5 | 3 | 0 | 1 | 21 | 1 | 3 | 1 | 0 | 10 | 4 |
3 | 10 53 104 | 2 | 56 | 1107121 | 1 | 93 | 1117 | 3 | 1 | 0 | 3 | 3 | 0 | 1 | 28 | 1 | 2 | 1 | 0 | 10 | 4 |
3 | 10 66 104 | 2 | 69 | 1107121 | 12 | 92 | 1116 | 5 | 1 | 0 | 5 | 5 | 1 | 1 | 26 | 1 | 1 | 1 | 0 | 10 | 2 |
3 | 10 80 104 | 2 | 83 | 1107121 | 2 | 93 | 1118 | 8 | 1 | 0 | 6 | 8 | 1 | 1 | 9 | 1 | 3 | 1 | 0 | 10 | 1 |
3 | 10 97 104 | 2 | 100 | 1107121 | 2 | 93 | 1118 | 4 | 1 | 0 | 4 | 4 | 1 | 1 | 23 | 1 | 2 | 1 | 0 | 10 | 5 |
4 | 10105 104 | 2 | 108 | 1107121 | 2 | 93 | 1118 | 4 | 1 | 0 | 4 | 4 | 1 | 1 | 14 | 1 | 3 | 1 | 0 | 10 | 4 |
4 | 10119 104 | 2 | 122 | 1107121 | 12 | 92 | 1116 | 5 | 1 | 0 | 5 | 5 | 0 | 1 | 29 | 1 | 3 | 1 | 0 | 10 | 5 |
4 | 10122 104 | 2 | 125 | 1107121 | 12 | 92 | 1116 | 3 | 1 | 0 | 3 | 3 | 1 | 1 | 4 | 1 | 2 | 1 | 0 | 10 | 5 |
4 | 10142 104 | 2 | 145 | 1107121 | 2 | 93 | 1118 | 5 | 1 | 0 | 5 | 5 | 2 | 1 | 3 | 1 | 2 | 1 | 0 | 10 | 4 |
4 | 10144 104 | 2 | 147 | 1107121 | 1 | 93 | 1117 | 7 | 1 | 0 | 7 | 7 | 0 | 1 | 16 | 1 | 1 | 1 | 0 | 10 | 4 |
4 | 10147 104 | 2 | 150 | 1107121 | 1 | 93 | 1117 | 4 | 0 | 0 | 4 | 2 | 2 | 1 | 16 | 1 | 2 | 1 | 0 | 10 | 3 |
4 | 10148 104 | 2 | 151 | 1107121 | 1 | 93 | 1117 | 6 | 1 | 0 | 6 | 6 | 0 | 1 | 18 | 1 | 2 | 1 | 0 | 10 | 5 |
4 | 10148 104 | 2 | 151 | 1107121 | 1 | 93 | 1117 | 6 | 1 | 0 | 6 | 6 | 0 | 1 | 18 | 1 | 2 | 1 | 0 | 10 | 5 |
2 | 10 2 104 | 3 | 5 | 1107121 | 11 | 92 | 1115 | 4 | 2 | 0 | 2 | 4 | 0 | 1 | 5 | 1 | 3 | 1 | 0 | 10 | 2 |
2 | 10 2 104 | 3 | 5 | 1107121 | 11 | 92 | 1115 | 4 | 2 | 0 | 2 | 4 | 0 | 1 | 5 | 1 | 3 | 1 | 0 | 10 | 2 |
2 | 10 52 104 | 4 | 55 | 1107121 | 1 | 93 | 1117 | 10 | 1 | 0 | 10 | 8 | 1 | 1 | 23 | 1 | 5 | 1 | 0 | 10 | 4 |
4 | 10 52 104 | 4 | 55 | 1107121 | 1 | 93 | 1117 | 10 | 1 | 0 | 10 | 8 | 1 | 1 | 23 | 1 | 5 | 1 | 0 | 10 | 4 |
4 | 10 52 104 | 4 | 55 | 1107121 | 1 | 93 | 1117 | 10 | 1 | 0 | 10 | 8 | 1 | 1 | 23 | 1 | 5 | 1 | 0 | 10 | 4 |
2 | 10 55 105 | 2 | 58 | 1107121 | 11 | 92 | 1115 | 6 | 1 | 0 | 6 | 6 | 0 | 1 | 28 | 1 | 1 | 1 | 0 | 10 | 1 |
1 | 10 79 105 | 2 | 82 | 1107121 | 1 | 93 | 1117 | 6 | 1 | 0 | 6 | 5 | 1 | 1 | 29 | 1 | 1 | 1 | 0 | 10 | 1 |
1 | 10 84 105 | 2 | 87 | 1107121 | 2 | 93 | 1118 | 4 | 1 | 0 | 4 | 4 | 0 | 1 | 10 | 1 | 3 | 1 | 0 | 10 | 3 |
1 | 10 94 105 | 2 | 97 | 1107121 | 2 | 93 | 1118 | 3 | 1 | 0 | 3 | 3 | 1 | 1 | 17 | 1 | 2 | 1 | 0 | 10 | 3 |
1 | 10126 105 | 2 | 129 | 1107121 | 11 | 92 | 1115 | 8 | 1 | 0 | 8 | 5 | 0 | 1 | 17 | 1 | 1 | 1 | 0 | 10 | 4 |
1 | 10133 105 | 2 | 136 | 1107121 | 12 | 92 | 1116 | 3 | 1 | 0 | 3 | 2 | 0 | 1 | 4 | 3 | 3 | 1 | 0 | 10 | 5 |
1 | 10138 105 | 2 | 141 | 1107121 | 1 | 93 | 1117 | 4 | 1 | 0 | 4 | 4 | 0 | 1 | 22 | 1 | 1 | 1 | 0 | 10 | 5 |
1 | 10138 105 | 2 | 141 | 1107121 | 1 | 93 | 1117 | 4 | 1 | 0 | 4 | 4 | 0 | 1 | 22 | 1 | 1 | 1 | 0 | 10 | 5 |
1 | 10138 105 | 2 | 141 | 1107121 | 1 | 93 | 1117 | 4 | 1 | 0 | 4 | 4 | 0 | 1 | 22 | 1 | 1 | 1 | 0 | 10 | 5 |
1 | 10138 105 | 2 | 141 | 1107121 | 1 | 93 | 1117 | 4 | 1 | 0 | 4 | 4 | 0 | 1 | 22 | 1 | 1 | 1 | 0 | 10 | 5 |
2 | 10 8 106 | 2 | 11 | 1107121 | 11 | 92 | 1115 | 5 | 1 | 0 | 5 | 5 | 0 | 1 | 19 | 1 | 3 | 1 | 0 | 10 | 1 |
1 | 10 22 106 | 2 | 25 | 1107121 | 11 | 92 | 1115 | 5 | 0 | 0 | 5 | 5 | 1 | 1 | 23 | 1 | 1 | 1 | 0 | 10 | 2 |
1 | 10 87 106 | 4 | 90 | 1107121 | 2 | 93 | 1118 | 14 | 2 | 0 | 14 | 14 | 2 | 1 | 24 | 1 | 2 | 1 | 0 | 10 | 5 |
2 | 10 1 107 | 3 | 4 | 1107121 | 11 | 92 | 1115 | 11 | 1 | 0 | 10 | 9 | 0 | 1 | 4 | 1 | 1 | 1 | 0 | 10 | 4 |
1 | 10 1 107 | 3 | 4 | 1107121 | 11 | 92 | 1115 | 11 | 1 | 0 | 10 | 9 | 0 | 1 | 4 | 1 | 1 | 1 | 0 | 10 | 4 |
1 | 10 1 107 | 3 | 4 | 1107121 | 11 | 92 | 1115 | 11 | 1 | 0 | 10 | 9 | 0 | 1 | 4 | 1 | 1 | 1 | 0 | 10 | 4 |
3 | 10 5 108 | 2 | 8 | 1107121 | 11 | 92 | 1115 | 7 | 1 | 0 | 7 | 7 | 2 | 1 | 17 | 1 | 3 | 1 | 0 | 10 | 1 |
2 | 10 47 108 | 2 | 50 | 1107121 | 2 | 93 | 1118 | 8 | 1 | 0 | 8 | 8 | 0 | 1 | 2 | 1 | 3 | 1 | 0 | 10 | 1 |
4 | 10 50 108 | 2 | 53 | 1107121 | 2 | 93 | 1118 | 6 | 1 | 0 | 6 | 6 | 0 | 1 | 24 | 1 | 3 | 1 | 0 | 10 | 1 |
2 | 10124 108 | 2 | 127 | 1107121 | 1 | 93 | 1117 | 5 | 1 | 0 | 5 | 5 | 2 | 1 | 21 | 1 | 1 | 1 | 0 | 10 | 5 |
2 | 10137 108 | 2 | 140 | 1107121 | 1 | 93 | 1117 | 2 | 1 | 0 | 2 | 2 | 0 | 1 | 20 | 1 | 2 | 1 | 0 | 10 | 4 |
2 | 10137 108 | 2 | 140 | 1107121 | 1 | 93 | 1117 | 2 | 1 | 0 | 2 | 2 | 0 | 1 | 20 | 1 | 2 | 1 | 0 | 10 | 4 |
2 | 10137 108 | 2 | 140 | 1107121 | 1 | 93 | 1117 | 2 | 1 | 0 | 2 | 2 | 0 | 1 | 20 | 1 | 2 | 1 | 0 | 10 | 4 |
Since it is a statistical problem. Post is at SAS Statistical Procedures
Steve will give some good advice .
As My opinion, I will use proc glm ,since there are some categorize variables.
Xia Keshan
You can attach by clicking the very faint advanced editor in the right upper corner of your Reply post.
I would suggest attaching the data. You also need to explain it a bit, you mention a single categorical variable but your data is clearly more than that.
How many data points do you have in total?
Hi Keshan,
Ad your opinion you told that i can use Proc GLM. suppose my dependent variable is 4 catergories and independent variables also many of them are class variables how to go about it.
in proc glm can we use class as a region? because there are 2-3 regions are there i have to do same analysis for all the regions.
Please give me some idea on this.
here i attached sample data for your reference
Regards,
Anil
Hi. I indeed am not a expert statistician . post it at
Steve , Rick , lvm ........ they are all specialist about it .
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