Hello all,   I have been trying to figure out how to test if two independent samples' medians are statistically significantly different from each other and Google directed me to the following link where the NPAR1WAY procedure was used: https://support.sas.com/documentation/cdl/en/statug/68162/HTML/default/viewer.htm#statug_npar1way_examples01.htm   Below are my data and the SAS code. My issue is about how to interpret the results. First of all there are two tables that reports two different statistics. The first one is the "Wilcoxon Two-Sample Test" and Pr < Z is 0.20 (i.e. not statistically significantly different). The second test is "Median Two-Sample Test" and Pr < Z is 0.175 (i.e. statistically significantly different). First, I don't know what these two tests measure and second why they have contradicting results (I'm guessing they measure different things and thus results are not contradicting but I don't know what).   I would really appreciate your advice and sorry for the lengthy data!   data los; input Treatment $ Length_of_stay Freq; datalines; No 1 475 No 2 304 No 3 364 No 4 364 No 5 334 No 6 320 No 7 277 No 8 256 No 9 208 No 10 192 No 11 173 No 12 152 No 13 114 No 14 123 No 15 95 No 16 96 No 17 94 No 18 81 No 19 69 No 20 75 No 21 50 No 22 60 No 23 53 No 24 49 No 25 45 No 26 40 No 27 46 No 28 37 No 29 36 No 30 31 No 31 33 No 32 25 No 33 26 No 34 21 No 35 33 No 36 23 No 37 21 No 38 27 No 39 16 No 40 31 No 41 24 No 42 27 No 43 16 No 44 17 No 45 13 No 46 11 No 47 20 No 48 15 No 49 13 No 50 16 No 51 8 No 52 12 No 53 17 No 54 10 No 55 6 No 56 9 No 57 11 No 58 8 No 59 10 No 60 8 No 61 7 No 62 10 No 63 8 No 64 11 No 65 6 No 66 4 No 67 9 No 68 6 No 69 4 No 70 10 No 71 5 No 72 1 No 73 7 No 74 4 No 75 4 No 76 4 No 77 6 No 78 9 No 79 2 No 80 2 No 81 2 No 82 4 No 83 2 No 84 3 No 85 4 No 86 3 No 87 3 No 88 2 No 89 3 No 90 3 No 91 3 No 92 2 No 93 3 No 94 1 No 95 2 No 96 2 No 97 1 No 98 2 No 102 3 No 104 1 No 108 2 No 113 2 No 114 1 No 115 3 No 117 1 No 119 2 No 120 1 No 121 2 No 122 1 No 123 2 No 124 1 No 125 2 No 126 2 No 128 2 No 130 2 No 133 1 No 135 1 No 141 1 No 143 1 No 144 2 No 145 1 No 148 1 No 149 1 No 151 1 No 152 1 No 153 1 No 162 1 No 168 1 No 170 1 No 176 2 No 177 1 No 180 1 No 181 1 No 187 1 No 192 1 No 194 1 No 201 1 No 212 1 No 215 1 No 227 1 No 260 1 No 261 1 No 313 1 No 326 1 No 370 1 No 383 1 No 387 1 No 395 1 No 402 1 No 409 1 No 431 1 Yes 1 617 Yes 2 471 Yes 3 489 Yes 4 460 Yes 5 447 Yes 6 398 Yes 7 393 Yes 8 289 Yes 9 290 Yes 10 280 Yes 11 269 Yes 12 204 Yes 13 209 Yes 14 138 Yes 15 142 Yes 16 148 Yes 17 135 Yes 18 129 Yes 19 113 Yes 20 98 Yes 21 91 Yes 22 80 Yes 23 70 Yes 24 85 Yes 25 66 Yes 26 59 Yes 27 57 Yes 28 54 Yes 29 56 Yes 30 36 Yes 31 45 Yes 32 46 Yes 33 52 Yes 34 49 Yes 35 32 Yes 36 31 Yes 37 41 Yes 38 30 Yes 39 24 Yes 40 35 Yes 41 20 Yes 42 14 Yes 43 24 Yes 44 20 Yes 45 30 Yes 46 18 Yes 47 14 Yes 48 24 Yes 49 22 Yes 50 21 Yes 51 21 Yes 52 9 Yes 53 17 Yes 54 18 Yes 55 8 Yes 56 13 Yes 57 12 Yes 58 15 Yes 59 14 Yes 60 12 Yes 61 13 Yes 62 9 Yes 63 7 Yes 64 4 Yes 65 10 Yes 66 12 Yes 67 11 Yes 68 7 Yes 69 13 Yes 70 7 Yes 71 10 Yes 72 3 Yes 73 6 Yes 74 11 Yes 75 8 Yes 76 2 Yes 77 1 Yes 78 3 Yes 79 2 Yes 80 5 Yes 81 1 Yes 82 2 Yes 83 2 Yes 84 6 Yes 85 3 Yes 86 2 Yes 87 4 Yes 88 3 Yes 89 2 Yes 90 6 Yes 91 2 Yes 92 2 Yes 93 4 Yes 94 2 Yes 95 1 Yes 97 1 Yes 98 2 Yes 99 1 Yes 100 3 Yes 102 3 Yes 104 1 Yes 105 1 Yes 106 3 Yes 108 1 Yes 109 3 Yes 111 2 Yes 114 3 Yes 115 4 Yes 116 2 Yes 117 2 Yes 119 1 Yes 121 5 Yes 122 1 Yes 123 4 Yes 124 2 Yes 125 1 Yes 127 1 Yes 128 1 Yes 131 1 Yes 132 1 Yes 133 1 Yes 137 1 Yes 140 1 Yes 141 1 Yes 144 1 Yes 145 1 Yes 146 1 Yes 148 1 Yes 149 1 Yes 151 1 Yes 153 2 Yes 158 1 Yes 160 1 Yes 165 1 Yes 166 1 Yes 168 1 Yes 173 2 Yes 175 1 Yes 176 1 Yes 182 1 Yes 184 1 Yes 185 1 Yes 187 1 Yes 195 2 Yes 199 1 Yes 220 1 Yes 224 1 Yes 228 2 Yes 232 2 Yes 242 1 Yes 247 1 Yes 300 1 Yes 316 1 Yes 322 1 Yes 336 1 Yes 340 1 Yes 375 1 Yes 576 1;  ... View more

Hello there, I'm trying to calculate the mean difference of a variable among two independent groups. I know that the variable is NOT normally distributed so I use the Wilxocon test below:   proc npar1way data = sample_data wilcoxon; class gender; var test_score; run;   The data "sample_data" is generated by multiple imputation with almost 40 imputations. The original data has about 10,000 records which brings the imputed data to 400,000 records (40x10,000). Since the sample size is artificially very big due to imputations the mean difference looks extremely statistically significant.   I was wondering how can I take into account that the data is constructed by 40 imputed data so that the significance of the mean difference can be calculated appropriately? Is there an option in the "proc npar1way" or do I need to use the estimators produced by PROC MIANALYZE in the next steps to calculate the mean difference of the two samples?   Thanks a lot in advance!  ... View more

Hello all,   I have a three dimensional data (year, source and mechanism) as in the example below and trying to display it via PROC SGPANEL but the code below stacks the source dimension on top of each other as opposed to what I want which is side by side. Lastly, I also would like to be able to show the labels for each three dimensions but could show only two (year and source). I was wondering if anyone can help?   Thanks a lot in advance!   Recep   PS: I'm not trying to create a 3-dimensional figure. Just trying to show my three-dimensional data in a usual 2-dimensional barchart by putting bars next to each other.   DATA cars1; length Mechanism $10. Year $9.; INPUT source $ Mechanism $ Year $ Count; CARDS; BCTR Scooter 2016/2017 17 BCTR Scooter 2017/2018 13 BCTR Scooter 2018/2019 17 BCTR Car 2016/2017 119 BCTR Car 2017/2018 133 BCTR Car 2018/2019 118 BCTR Motorcycle 2016/2017 47 BCTR Motorcycle 2017/2018 62 BCTR Motorcycle 2018/2019 63 BCTR Bicycle 2016/2017 82 BCTR Bicycle 2017/2018 90 BCTR Bicycle 2018/2019 85 BCTR Pedestrian 2016/2017 72 BCTR Pedestrian 2017/2018 82 BCTR Pedestrian 2018/2019 83 DAD Scooter 2016/2017 26 DAD Scooter 2017/2018 21 DAD Scooter 2018/2019 20 DAD Car 2016/2017 264 DAD Car 2017/2018 292 DAD Car 2018/2019 262 DAD Motorcycle 2016/2017 105 DAD Motorcycle 2017/2018 115 DAD Motorcycle 2018/2019 117 DAD Bicycle 2016/2017 314 DAD Bicycle 2017/2018 293 DAD Bicycle 2018/2019 321 DAD Pedestrian 2016/2017 176 DAD Pedestrian 2017/2018 154 DAD Pedestrian 2018/2019 150 ; RUN; proc sgpanel data=cars1; panelby year /layout=columnlattice onepanel colheaderpos=bottom rows=1 novarname noborder; vbar mechanism /group=source response=count; colaxis display=none; rowaxis grid; run;   ... View more

Hello all, I was wondering if anyone can tell how to manipulate the plots generated by PROC FREQ. The code below generates the  plot but I cannot change the title, axis names, etc. of the plot within the PROC FREQ procedure (neither the title nor the label statements make any difference). Thanks a lot in advance! Recep Gezer   ods graphics on; ods html style=statistical; proc freq data=master_blunt_penetrating order=data; tables site*ttl_surgeon*o_expired/ cmh alpha=0.1 relrisk plots=relriskplot(logbase=E); weight count; label ttl_surgeon= "Trauma Team Leader" o_expired="Mortality"; title 'Raw data (ignoring hospital) - Blunt and penetrating injuries'; run; ods graphics off; ods html close; ... View more