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Ra6 · ‎02-18-2019

yes, that's a part but the rest of the data for average snow depth is also constant

Ra6 · ‎02-18-2019

Hello good people, Recently, I have started using SAS for analyzing data and I did not have any prior experience regarding using SAS for data analysis. I will really appreciate if anyone please help me to solve the problem below. I want to show the effect of average snow depth on bulk density (with three depths 0-5, 5-10 and 10-20). Basically, I will have to find out that bulk density will increase or decrease for average snow depth. I have also attached the data file here. ᐧ data Data; Input plot $ BD_zero_to_five BD_five_to_ten BD_ten_to_twenty $ Average_snow_depth; datalines; 1 1.58 2.29 1.59 12 1 1.20 1.81 1.41 12 1 1.28 1.33 1.82 12 1 1.46 1.97 1.39 12 1 1.17 1.54 1.09 12 1 1.28 1.59 1.23 12 1 1.41 1.53 1.67 12 1 1.21 1.37 1.40 12 1 1.54 1.87 1.26 12 1 1.28 1.70 1.30 12 1 1.21 1.65 1.55 12 1 1.33 1.86 1.69 12 2 1.25 1.36 1.15 12 2 1.58 1.86 1.75 12 2 1.36 1.60 1.59 12 2 1.03 1.72 1.43 12 2 1.03 1.55 1.16 12 2 1.04 1.49 1.30 12 2 1.28 1.24 1.17 12 2 1.18 2.10 1.66 12 2 1.38 1.61 1.32 12 2 1.28 1.51 1.33 12 2 1.26 1.53 1.31 12 2 1.44 1.58 1.85 12 ;

Ra6 · ‎08-20-2018

I am attaching my sas file here. So, in my data set, experimental unit is plot which represents combination of treatments which are traffic and overstory and depending variables are BD_zero_to_five_cm BD_five_to_ten_cm BD_ten_to_twenty_cm. So, I had to run my sas file showing the effect of two treatments on BD_zero_to_five_cm BD_five_to_ten_cm BD_ten_to_twenty_cm. But now I have to add two more variable (slash volume, average snow depth) as covariates and find out effect of these two covariates (which will be actually covariates as a regression variable following ANCOVA https://onlinecourses.science.psu.edu/stat502/node/183/ ) on each depth BD_zero_to_five_cm BD_five_to_ten_cm BD_ten_to_twenty_cm. I would like to also mention that, slash volume and average snow depth are continuous data data New_data; Input plot $ traffic $ overstory_trmt $ BD_zero_to_five_cm BD_five_to_ten_cm BD_ten_to_twenty_cm $ slash_volume $ average_snow_depth; datalines; 1 Low Partial 1.323 1.896 1.410 0 12 1 Low Partial 1.019 1.518 1.260 0.0127 12 1 Low Partial 1.057 1.103 1.583 0.002 12 1 Low Partial 1.218 1.631 1.236 0.00712 12 1 Low Partial 0.990 1.294 0.982 0 12 1 Low Partial 1.081 1.348 1.098 0.0096 12 1 Low Partial 1.166 1.273 1.470 0.0081 12 1 Low Partial 1.017 1.150 1.251 0.0026 12 1 Low Partial 1.293 1.558 1.127 0.004 12 1 Low Partial 1.087 1.433 1.159 0 12 1 Low Partial 1.027 1.394 1.376 0.01 12 1 Low Partial 1.120 1.570 1.501 0 12 2 Low Partial 1.050 1.134 1.022 0 12 2 Low Partial 1.322 1.553 1.555 0 12 2 Low Partial 1.147 1.350 1.424 0 12 2 Low Partial 0.851 1.411 1.272 0 12 2 Low Partial 0.868 1.262 1.040 0 12 2 Low Partial 0.881 1.245 1.156 0 12 2 Low Partial 1.069 1.047 1.047 0 12 2 Low Partial 0.987 1.703 1.481 0 12 2 Low Partial 1.154 1.346 1.181 0 12 2 Low Partial 1.076 1.255 1.184 0 12 2 Low Partial 1.062 1.286 1.165 0.0078 12 2 Low Partial 1.194 1.320 1.627 0 12 3 Low Clearcut 0.966 1.062 0.938 0 15 3 Low Clearcut 1.001 1.085 0.940 0.027 15 3 Low Clearcut 1.217 1.136 1.133 0 15 3 Low Clearcut 0.800 1.193 1.259 0.0088 15 3 Low Clearcut 1.203 1.460 1.280 0.0096 15 3 Low Clearcut 1.162 1.485 1.276 0.0364 15 3 Low Clearcut 1.104 1.457 1.204 0.0042 15 3 Low Clearcut 1.297 1.301 1.578 0.0024 15 3 Low Clearcut 1.332 1.337 1.375 0.0488 15 3 Low Clearcut 0.560 1.434 1.069 0 15 3 Low Clearcut 1.017 1.154 1.258 0.0024 15 3 Low Clearcut 0.925 0.922 1.094 0 15 4 Medium Partial 1.200 1.399 1.089 0 3 4 Medium Partial 1.228 1.692 1.227 0 3 4 Medium Partial 1.058 1.410 1.166 0 3 4 Medium Partial 1.235 1.006 0.979 0 3 4 Medium Partial 1.589 1.485 1.283 0 3 4 Medium Partial 0.948 1.292 1.291 0 3 4 Medium Partial 1.067 0.947 1.190 0 3 4 Medium Partial 1.019 1.157 1.236 0 3 4 Medium Partial 1.344 1.340 1.522 0 3 4 Medium Partial 0.900 1.580 1.289 0 3 4 Medium Partial 1.463 1.589 1.560 0 3 4 Medium Partial 0.073 1.210 1.182 0 3 5 Medium Partial 1.117 1.317 0.986 0.153 1 5 Medium Partial 1.006 1.338 1.050 0 1 5 Medium Partial 0.958 1.008 1.029 0.044 1 5 Medium Partial 0.893 0.992 0.939 0.0096 1 5 Medium Partial 0.978 0.899 0.861 0.0056 1 5 Medium Partial 0.936 1.101 1.240 0.012 1 5 Medium Partial 0.879 0.818 0.926 0.014 1 5 Medium Partial 1.103 1.301 1.141 0.038 1 5 Medium Partial 0.582 1.013 1.046 0.0052 1 5 Medium Partial 1.167 1.301 0.965 0.0132 1 5 Medium Partial 0.895 0.874 1.015 0.0028 1 5 Medium Partial 1.196 1.202 1.076 0.0135 1 6 Medium Partial 0.982 1.452 2.161 0 8 6 Medium Partial 1.149 1.349 1.430 0 8 6 Medium Partial 1.193 2.184 1.569 0 8 6 Medium Partial 1.296 2.065 1.373 0 8 6 Medium Partial 0.921 1.178 1.373 0 8 6 Medium Partial 1.589 1.795 1.750 0 8 6 Medium Partial 0.862 1.169 1.518 0 8 6 Medium Partial 1.315 1.559 1.494 0 8 6 Medium Partial 1.176 1.206 1.537 0 8 6 Medium Partial 1.444 1.737 1.658 0 8 6 Medium Partial 1.462 2.359 0.898 0 8 6 Medium Partial 1.396 1.585 1.680 0 8 7 High Partial 1.174 1.005 1.251 0 3 7 High Partial 1.107 1.621 1.517 0 3 7 High Partial 1.028 1.160 1.028 0 3 7 High Partial 1.293 1.310 1.115 0 3 7 High Partial 0.944 1.181 0.896 0 3 7 High Partial 1.329 1.790 1.067 0 3 7 High Partial 0.932 1.283 0.912 0 3 7 High Partial 1.052 0.932 1.258 0 3 7 High Partial 0.682 0.850 1.277 0 3 7 High Partial 1.054 1.095 1.052 0 3 7 High Partial 1.100 1.102 1.193 0 3 7 High Partial 1.074 1.114 1.173 0 3 8 High Partial 0.984 1.129 1.089 0 3 8 High Partial 1.113 1.136 1.036 0 3 8 High Partial 0.866 1.484 1.113 0 3 8 High Partial 1.027 1.385 1.347 0 3 8 High Partial 0.743 1.005 1.355 0 3 8 High Partial 1.144 1.397 1.317 0 3 8 High Partial 0.831 1.350 1.198 0 3 8 High Partial 0.889 1.184 0.936 0 3 8 High Partial 0.954 1.566 1.161 0 3 8 High Partial 0.789 1.254 1.045 0 3 8 High Partial 1.199 1.396 1.072 0 3 8 High Partial 0.895 1.398 1.317 0 3 9 High Partial 1.472 1.683 1.520 0.0328 8 9 High Partial 1.052 1.406 1.311 0.0288 8 9 High Partial 1.341 1.404 1.382 0.0286 8 9 High Partial 1.114 0.116 1.350 0.0066 8 9 High Partial 1.211 1.294 1.280 0.0116 8 9 High Partial 1.126 1.052 1.488 0 8 9 High Partial 1.204 1.262 0.873 0.021 8 9 High Partial 1.218 1.223 1.155 0.0216 8 9 High Partial 1.265 1.346 1.248 0.0022 8 9 High Partial 1.218 1.105 1.180 0 8 9 High Partial 1.080 1.361 1.532 0.0036 8 9 High Partial 1.000 1.111 1.233 0 8 10 Medium Clearcut 1.054 1.554 1.666 0 9 10 Medium Clearcut 1.721 1.351 1.623 0.009 9 10 Medium Clearcut 1.364 1.624 1.746 0.0042 9 10 Medium Clearcut 0.955 1.153 1.348 0 9 10 Medium Clearcut 1.150 1.817 1.467 0.01 9 10 Medium Clearcut 1.820 1.666 1.545 0.0024 9 10 Medium Clearcut 1.021 1.558 1.673 0.0108 9 10 Medium Clearcut 0.650 1.131 1.304 0.0145 9 10 Medium Clearcut 1.580 1.400 1.544 0 9 10 Medium Clearcut 1.047 1.988 1.481 0 9 10 Medium Clearcut 1.270 1.784 1.693 0.0042 9 10 Medium Clearcut 0.808 1.088 1.895 0.0112 9 11 Medium Clearcut 0.851 1.295 1.905 0.0102 15 11 Medium Clearcut 1.535 1.269 1.279 0.012 15 11 Medium Clearcut 1.346 1.680 1.697 0.018 15 11 Medium Clearcut 1.084 1.546 1.907 0.0175 15 11 Medium Clearcut 0.869 1.229 1.387 0.0351 15 11 Medium Clearcut 1.865 0.964 1.309 0.0144 15 11 Medium Clearcut 1.312 1.257 1.463 0.0028 15 11 Medium Clearcut 1.091 1.169 1.191 0.0037 15 11 Medium Clearcut 1.403 1.394 1.635 0.0032 15 11 Medium Clearcut 0.735 1.031 1.283 0 15 11 Medium Clearcut 0.953 1.005 1.669 0 15 11 Medium Clearcut 0.704 0.963 1.277 0.0052 15 12 Medium Clearcut 0.896 1.427 1.678 0.0342 16 12 Medium Clearcut 0.969 1.900 1.806 0.0096 16 12 Medium Clearcut 0.607 1.090 1.397 0.0026 16 12 Medium Clearcut 1.804 1.645 1.609 0.0253 16 12 Medium Clearcut 0.683 1.315 1.525 0.048 16 12 Medium Clearcut 1.154 2.762 1.619 0 16 12 Medium Clearcut 0.778 1.495 1.830 0.008 16 12 Medium Clearcut 0.790 1.153 1.747 0.076 16 12 Medium Clearcut 1.393 1.995 2.008 0 16 12 Medium Clearcut 0.787 0.804 0.417 0.0084 16 12 Medium Clearcut 0.865 0.831 1.023 0.0132 16 12 Medium Clearcut 0.887 2.708 1.178 0.0261 16 13 Low Clearcut 1.087 1.462 1.034 0.0115 19 13 Low Clearcut 0.954 1.275 1.036 0.0096 19 13 Low Clearcut 1.002 1.430 1.234 0.016 19 13 Low Clearcut 1.491 1.175 1.295 0.0075 19 13 Low Clearcut 1.131 1.299 1.074 0.0357 19 13 Low Clearcut 0.991 0.969 1.022 0.0062 19 13 Low Clearcut 1.004 1.205 1.109 0.0301 19 13 Low Clearcut 1.201 1.058 1.029 0.014 19 13 Low Clearcut 1.073 0.981 1.038 0.0132 19 13 Low Clearcut 1.086 1.304 1.236 0.076 19 13 Low Clearcut 0.995 1.084 1.386 0.0144 19 13 Low Clearcut 0.715 0.791 1.081 0.0115 19 14 Low Partial 1.353 1.694 1.419 0 14 14 Low Partial 0.912 1.241 1.180 0 14 14 Low Partial 0.945 1.381 1.418 0.005 14 14 Low Partial 0.771 1.340 1.270 0.001 14 14 Low Partial 0.842 1.120 1.341 0.01 14 14 Low Partial 0.864 0.952 0.991 0 14 14 Low Partial 1.132 1.981 1.099 0 14 14 Low Partial 1.027 1.376 1.100 0.046 14 14 Low Partial 0.992 1.529 0.869 0 14 14 Low Partial 1.030 1.430 2.150 0.0014 14 14 Low Partial 0.983 1.607 0.834 0.016 14 14 Low Partial 1.093 3.007 0.531 0.004 14 15 Low Clearcut 0.779 1.457 1.329 0 12.5 15 Low Clearcut 1.500 1.058 2.075 0 12.5 15 Low Clearcut 1.006 1.282 1.668 0 12.5 15 Low Clearcut 0.542 0.758 1.461 0 12.5 15 Low Clearcut 1.296 0.786 1.216 0 12.5 15 Low Clearcut 0.916 1.125 1.097 0 12.5 15 Low Clearcut 0.857 1.105 1.333 0 12.5 15 Low Clearcut 0.923 1.014 1.035 0 12.5 15 Low Clearcut 1.003 0.942 1.021 0 12.5 15 Low Clearcut 0.905 1.064 1.047 0 12.5 15 Low Clearcut 0.904 0.988 1.291 0 12.5 15 Low Clearcut 1.167 1.315 1.385 0 12.5 16 High Clearcut 1.125 3.074 0.557 0.0052 8.5 16 High Clearcut 0.923 1.035 1.114 0.06 8.5 16 High Clearcut 0.865 1.262 1.315 0.0308 8.5 16 High Clearcut 1.540 1.049 0.803 0 8.5 16 High Clearcut 1.037 2.497 1.948 0.03 8.5 16 High Clearcut 1.332 2.287 1.951 0 8.5 16 High Clearcut 1.320 1.506 1.721 0.003 8.5 16 High Clearcut 1.143 1.401 1.314 0.018 8.5 16 High Clearcut 1.336 1.464 1.541 0.0161 8.5 16 High Clearcut 1.141 1.319 1.083 0 8.5 16 High Clearcut 0.904 1.279 1.077 0.04275 8.5 16 High Clearcut 0.906 1.175 1.167 0.0108 8.5 17 High Clearcut 0.827 0.950 1.440 0.0759 15 17 High Clearcut 1.054 1.078 1.171 0.0672 15 17 High Clearcut 0.883 1.070 0.961 0.0867 15 17 High Clearcut 0.863 1.110 1.209 0.035 15 17 High Clearcut 1.012 0.893 1.043 0.051 15 17 High Clearcut 0.829 1.047 1.704 0.1288 15 17 High Clearcut 0.842 2.687 1.570 0.0126 15 17 High Clearcut 1.116 1.246 1.121 0.0064 15 17 High Clearcut 1.069 1.115 1.296 0.0513 15 17 High Clearcut 0.968 1.009 1.455 0.066 15 17 High Clearcut 1.222 1.400 1.364 0.043 15 17 High Clearcut 1.069 1.547 1.530 0.0342 15 18 High Clearcut 0.805 1.502 1.701 0.016 15 18 High Clearcut 1.075 1.643 1.568 0.018 15 18 High Clearcut 0.959 1.325 1.275 0.0171 15 18 High Clearcut 1.110 1.475 1.502 0.008 15 18 High Clearcut 0.721 1.027 1.662 0.015 15 18 High Clearcut 0.979 1.998 1.390 0.017 15 18 High Clearcut 1.276 1.736 1.416 0.0123 15 18 High Clearcut 1.166 1.376 1.462 0.0012 15 18 High Clearcut 1.163 1.546 1.718 0.008 15 18 High Clearcut 1.391 1.756 1.820 0.0028 15 18 High Clearcut 1.372 1.252 1.932 0 15 18 High Clearcut 1.128 1.684 1.891 0 15 19 None Partial 1.093 1.088 1.211 0 0 19 None Partial 0.887 0.974 1.092 0 0 19 None Partial 1.071 1.220 1.579 0 0 19 None Partial 0.828 1.237 1.349 0 0 19 None Partial 1.083 1.461 1.498 0 0 19 None Partial 0.714 0.885 1.260 0 0 19 None Partial 0.795 1.304 1.201 0 0 19 None Partial 0.445 1.604 1.328 0 0 19 None Partial 0.807 0.721 1.128 0 0 19 None Partial 1.141 1.196 1.152 0 0 19 None Partial 0.797 1.106 1.102 0 0 19 None Partial 1.130 1.480 1.158 0 0 19 None Partial 1.162 1.711 1.168 0 0 19 None Partial 0.652 0.929 1.228 0 0 19 None Partial 0.923 1.162 1.399 0 0 19 None Partial 0.796 1.292 1.080 0 0 19 None Partial 0.847 1.080 0.745 0 0 19 None Partial 1.025 1.112 2.670 0 0 19 None Clearcut 1.060 1.425 1.219 0 0 19 None Partial 0.753 0.860 1.571 0 0 19 None Partial 1.290 1.146 1.146 0 0 19 None Partial 0.932 1.638 1.695 0 0 19 None Partial 0.956 0.975 0.867 0 0 19 None Partial 0.828 1.377 1.131 0 0 19 None Clearcut 1.003 1.753 1.527 0 0 19 None Clearcut 0.819 1.057 1.374 0 0 19 None Partial 0.949 1.220 0.891 0 0 19 None Clearcut 0.708 1.770 1.330 0 0 19 None Clearcut 0.877 1.109 1.402 0 0 19 None Clearcut 1.006 1.946 1.522 0 0 19 None Partial 0.827 2.489 1.396 0 0 ; PROC GLM; class traffic overstory_trmt plot; model BD_zero_to_five_cm = traffic overstory_trmt plot(traffic overstory_trmt); test h=traffic overstory_trmt e=plot(traffic overstory_trmt); means traffic overstory_trmt / waller e=plot(traffic overstory_trmt); LSMeans traffic / Adjust = Tukey; LSMeans overstory_trmt / Adjust = Tukey; run; PROC GLM; class traffic overstory_trmt plot; model BD_five_to_ten_cm = traffic overstory_trmt plot(traffic overstory_trmt); test h=traffic overstory_trmt e=plot(traffic overstory_trmt); means traffic overstory_trmt / waller e=plot(traffic overstory_trmt); LSMeans traffic / Adjust = Tukey; LSMeans overstory_trmt / Adjust = Tukey; run; PROC GLM; class traffic overstory_trmt plot; model BD_ten_to_twenty_cm = traffic overstory_trmt plot(traffic overstory_trmt); test h=traffic overstory_trmt e=plot(traffic overstory_trmt); means traffic overstory_trmt / waller e=plot(traffic overstory_trmt); LSMeans traffic / Adjust = Tukey; LSMeans overstory_trmt / Adjust = Tukey; run; #Effect of slash volume and average snow depth proc reg data=New_data; model BD_zero_to_five_cm = slash_volume average_snow_depth traffic overstory_trmt plot(traffic overstory_trmt); model BD_five_to_ten_cm = slash_volume average_snow_depth traffic overstory_trmt plot(traffic overstory_trmt); model BD_ten_to_twenty_cm = slash_volume average_snow_depth traffic overstory_trmt plot(traffic overstory_trmt);

Ra6 · ‎08-20-2018

Unfortunately, It's not working

Ra6 · ‎08-20-2018

Unfortunately, it's not working

Ra6 · ‎08-20-2018

Here is my code where I had to find out the effect of machine traffic and overstory treatment on BD_zero_to_five_cm, BD_five_to_ten_cm and BD_ten_to_twenty_cm separately. Now I added two more variables as covariates (slash volume and average snow depth) where covariates will be regression variable. Then what will be the code if I want to find out the effect of covariates (slash volume and average snow depth) on BD_zero_to_five_cm, BD_five_to_ten_cm and BD_ten_to_twenty_cm separately. I am using SAS 9.4. I hope that anyone can give me some suggestions on how to perform this. data New_data; Input plot $ traffic $ overstory_trmt $ BD_zero_to_five_cm BD_five_to_ten_cm BD_ten_to_twenty_cm $ slash_volume $ average_snow_depth; datalines; 1 Low Partial 1.323 1.896 1.410 0 12 1 Low Partial 1.019 1.518 1.260 0.0127 12 1 Low Partial 1.057 1.103 1.583 0.002 12 1 Low Partial 1.218 1.631 1.236 0.00712 12 1 Low Partial 0.990 1.294 0.982 0 12 1 Low Partial 1.081 1.348 1.098 0.0096 12 1 Low Partial 1.166 1.273 1.470 0.0081 12 1 Low Partial 1.017 1.150 1.251 0.0026 12 1 Low Partial 1.293 1.558 1.127 0.004 12 1 Low Partial 1.087 1.433 1.159 0 12 1 Low Partial 1.027 1.394 1.376 0.01 12 1 Low Partial 1.120 1.570 1.501 0 12 2 Low Partial 1.050 1.134 1.022 0 12 2 Low Partial 1.322 1.553 1.555 0 12 2 Low Partial 1.147 1.350 1.424 0 12 2 Low Partial 0.851 1.411 1.272 0 12 2 Low Partial 0.868 1.262 1.040 0 12 2 Low Partial 0.881 1.245 1.156 0 12 2 Low Partial 1.069 1.047 1.047 0 12 2 Low Partial 0.987 1.703 1.481 0 12 2 Low Partial 1.154 1.346 1.181 0 12 2 Low Partial 1.076 1.255 1.184 0 12 2 Low Partial 1.062 1.286 1.165 0.0078 12 2 Low Partial 1.194 1.320 1.627 0 12 3 Low Clearcut 0.966 1.062 0.938 0 15 3 Low Clearcut 1.001 1.085 0.940 0.027 15 3 Low Clearcut 1.217 1.136 1.133 0 15 3 Low Clearcut 0.800 1.193 1.259 0.0088 15 3 Low Clearcut 1.203 1.460 1.280 0.0096 15 3 Low Clearcut 1.162 1.485 1.276 0.0364 15 3 Low Clearcut 1.104 1.457 1.204 0.0042 15 3 Low Clearcut 1.297 1.301 1.578 0.0024 15 3 Low Clearcut 1.332 1.337 1.375 0.0488 15 3 Low Clearcut 0.560 1.434 1.069 0 15 3 Low Clearcut 1.017 1.154 1.258 0.0024 15 3 Low Clearcut 0.925 0.922 1.094 0 15 4 Medium Partial 1.200 1.399 1.089 0 3 4 Medium Partial 1.228 1.692 1.227 0 3 4 Medium Partial 1.058 1.410 1.166 0 3 4 Medium Partial 1.235 1.006 0.979 0 3 4 Medium Partial 1.589 1.485 1.283 0 3 4 Medium Partial 0.948 1.292 1.291 0 3 4 Medium Partial 1.067 0.947 1.190 0 3 4 Medium Partial 1.019 1.157 1.236 0 3 4 Medium Partial 1.344 1.340 1.522 0 3 4 Medium Partial 0.900 1.580 1.289 0 3 4 Medium Partial 1.463 1.589 1.560 0 3 4 Medium Partial 0.073 1.210 1.182 0 3 5 Medium Partial 1.117 1.317 0.986 0.153 1 5 Medium Partial 1.006 1.338 1.050 0 1 5 Medium Partial 0.958 1.008 1.029 0.044 1 5 Medium Partial 0.893 0.992 0.939 0.0096 1 5 Medium Partial 0.978 0.899 0.861 0.0056 1 5 Medium Partial 0.936 1.101 1.240 0.012 1 5 Medium Partial 0.879 0.818 0.926 0.014 1 5 Medium Partial 1.103 1.301 1.141 0.038 1 5 Medium Partial 0.582 1.013 1.046 0.0052 1 5 Medium Partial 1.167 1.301 0.965 0.0132 1 5 Medium Partial 0.895 0.874 1.015 0.0028 1 5 Medium Partial 1.196 1.202 1.076 0.0135 1 6 Medium Partial 0.982 1.452 2.161 0 8 6 Medium Partial 1.149 1.349 1.430 0 8 6 Medium Partial 1.193 2.184 1.569 0 8 6 Medium Partial 1.296 2.065 1.373 0 8 6 Medium Partial 0.921 1.178 1.373 0 8 6 Medium Partial 1.589 1.795 1.750 0 8 6 Medium Partial 0.862 1.169 1.518 0 8 6 Medium Partial 1.315 1.559 1.494 0 8 6 Medium Partial 1.176 1.206 1.537 0 8 6 Medium Partial 1.444 1.737 1.658 0 8 6 Medium Partial 1.462 2.359 0.898 0 8 6 Medium Partial 1.396 1.585 1.680 0 8 7 High Partial 1.174 1.005 1.251 0 3 7 High Partial 1.107 1.621 1.517 0 3 7 High Partial 1.028 1.160 1.028 0 3 7 High Partial 1.293 1.310 1.115 0 3 7 High Partial 0.944 1.181 0.896 0 3 7 High Partial 1.329 1.790 1.067 0 3 7 High Partial 0.932 1.283 0.912 0 3 7 High Partial 1.052 0.932 1.258 0 3 7 High Partial 0.682 0.850 1.277 0 3 7 High Partial 1.054 1.095 1.052 0 3 7 High Partial 1.100 1.102 1.193 0 3 7 High Partial 1.074 1.114 1.173 0 3 8 High Partial 0.984 1.129 1.089 0 3 8 High Partial 1.113 1.136 1.036 0 3 8 High Partial 0.866 1.484 1.113 0 3 8 High Partial 1.027 1.385 1.347 0 3 8 High Partial 0.743 1.005 1.355 0 3 8 High Partial 1.144 1.397 1.317 0 3 8 High Partial 0.831 1.350 1.198 0 3 8 High Partial 0.889 1.184 0.936 0 3 8 High Partial 0.954 1.566 1.161 0 3 8 High Partial 0.789 1.254 1.045 0 3 8 High Partial 1.199 1.396 1.072 0 3 8 High Partial 0.895 1.398 1.317 0 3 9 High Partial 1.472 1.683 1.520 0.0328 8 9 High Partial 1.052 1.406 1.311 0.0288 8 9 High Partial 1.341 1.404 1.382 0.0286 8 9 High Partial 1.114 0.116 1.350 0.0066 8 9 High Partial 1.211 1.294 1.280 0.0116 8 9 High Partial 1.126 1.052 1.488 0 8 9 High Partial 1.204 1.262 0.873 0.021 8 9 High Partial 1.218 1.223 1.155 0.0216 8 9 High Partial 1.265 1.346 1.248 0.0022 8 9 High Partial 1.218 1.105 1.180 0 8 9 High Partial 1.080 1.361 1.532 0.0036 8 9 High Partial 1.000 1.111 1.233 0 8 10 Medium Clearcut 1.054 1.554 1.666 0 9 10 Medium Clearcut 1.721 1.351 1.623 0.009 9 10 Medium Clearcut 1.364 1.624 1.746 0.0042 9 10 Medium Clearcut 0.955 1.153 1.348 0 9 10 Medium Clearcut 1.150 1.817 1.467 0.01 9 10 Medium Clearcut 1.820 1.666 1.545 0.0024 9 10 Medium Clearcut 1.021 1.558 1.673 0.0108 9 10 Medium Clearcut 0.650 1.131 1.304 0.0145 9 10 Medium Clearcut 1.580 1.400 1.544 0 9 10 Medium Clearcut 1.047 1.988 1.481 0 9 10 Medium Clearcut 1.270 1.784 1.693 0.0042 9 10 Medium Clearcut 0.808 1.088 1.895 0.0112 9 11 Medium Clearcut 0.851 1.295 1.905 0.0102 15 11 Medium Clearcut 1.535 1.269 1.279 0.012 15 11 Medium Clearcut 1.346 1.680 1.697 0.018 15 11 Medium Clearcut 1.084 1.546 1.907 0.0175 15 11 Medium Clearcut 0.869 1.229 1.387 0.0351 15 11 Medium Clearcut 1.865 0.964 1.309 0.0144 15 11 Medium Clearcut 1.312 1.257 1.463 0.0028 15 11 Medium Clearcut 1.091 1.169 1.191 0.0037 15 11 Medium Clearcut 1.403 1.394 1.635 0.0032 15 11 Medium Clearcut 0.735 1.031 1.283 0 15 11 Medium Clearcut 0.953 1.005 1.669 0 15 11 Medium Clearcut 0.704 0.963 1.277 0.0052 15 12 Medium Clearcut 0.896 1.427 1.678 0.0342 16 12 Medium Clearcut 0.969 1.900 1.806 0.0096 16 12 Medium Clearcut 0.607 1.090 1.397 0.0026 16 12 Medium Clearcut 1.804 1.645 1.609 0.0253 16 12 Medium Clearcut 0.683 1.315 1.525 0.048 16 12 Medium Clearcut 1.154 2.762 1.619 0 16 12 Medium Clearcut 0.778 1.495 1.830 0.008 16 12 Medium Clearcut 0.790 1.153 1.747 0.076 16 12 Medium Clearcut 1.393 1.995 2.008 0 16 12 Medium Clearcut 0.787 0.804 0.417 0.0084 16 12 Medium Clearcut 0.865 0.831 1.023 0.0132 16 12 Medium Clearcut 0.887 2.708 1.178 0.0261 16 13 Low Clearcut 1.087 1.462 1.034 0.0115 19 13 Low Clearcut 0.954 1.275 1.036 0.0096 19 13 Low Clearcut 1.002 1.430 1.234 0.016 19 13 Low Clearcut 1.491 1.175 1.295 0.0075 19 13 Low Clearcut 1.131 1.299 1.074 0.0357 19 13 Low Clearcut 0.991 0.969 1.022 0.0062 19 13 Low Clearcut 1.004 1.205 1.109 0.0301 19 13 Low Clearcut 1.201 1.058 1.029 0.014 19 13 Low Clearcut 1.073 0.981 1.038 0.0132 19 13 Low Clearcut 1.086 1.304 1.236 0.076 19 13 Low Clearcut 0.995 1.084 1.386 0.0144 19 13 Low Clearcut 0.715 0.791 1.081 0.0115 19 14 Low Partial 1.353 1.694 1.419 0 14 14 Low Partial 0.912 1.241 1.180 0 14 14 Low Partial 0.945 1.381 1.418 0.005 14 14 Low Partial 0.771 1.340 1.270 0.001 14 14 Low Partial 0.842 1.120 1.341 0.01 14 14 Low Partial 0.864 0.952 0.991 0 14 14 Low Partial 1.132 1.981 1.099 0 14 14 Low Partial 1.027 1.376 1.100 0.046 14 14 Low Partial 0.992 1.529 0.869 0 14 14 Low Partial 1.030 1.430 2.150 0.0014 14 14 Low Partial 0.983 1.607 0.834 0.016 14 14 Low Partial 1.093 3.007 0.531 0.004 14 15 Low Clearcut 0.779 1.457 1.329 0 12.5 15 Low Clearcut 1.500 1.058 2.075 0 12.5 15 Low Clearcut 1.006 1.282 1.668 0 12.5 15 Low Clearcut 0.542 0.758 1.461 0 12.5 15 Low Clearcut 1.296 0.786 1.216 0 12.5 15 Low Clearcut 0.916 1.125 1.097 0 12.5 15 Low Clearcut 0.857 1.105 1.333 0 12.5 15 Low Clearcut 0.923 1.014 1.035 0 12.5 15 Low Clearcut 1.003 0.942 1.021 0 12.5 15 Low Clearcut 0.905 1.064 1.047 0 12.5 15 Low Clearcut 0.904 0.988 1.291 0 12.5 15 Low Clearcut 1.167 1.315 1.385 0 12.5 16 High Clearcut 1.125 3.074 0.557 0.0052 8.5 16 High Clearcut 0.923 1.035 1.114 0.06 8.5 16 High Clearcut 0.865 1.262 1.315 0.0308 8.5 16 High Clearcut 1.540 1.049 0.803 0 8.5 16 High Clearcut 1.037 2.497 1.948 0.03 8.5 16 High Clearcut 1.332 2.287 1.951 0 8.5 16 High Clearcut 1.320 1.506 1.721 0.003 8.5 16 High Clearcut 1.143 1.401 1.314 0.018 8.5 16 High Clearcut 1.336 1.464 1.541 0.0161 8.5 16 High Clearcut 1.141 1.319 1.083 0 8.5 16 High Clearcut 0.904 1.279 1.077 0.04275 8.5 16 High Clearcut 0.906 1.175 1.167 0.0108 8.5 17 High Clearcut 0.827 0.950 1.440 0.0759 15 17 High Clearcut 1.054 1.078 1.171 0.0672 15 17 High Clearcut 0.883 1.070 0.961 0.0867 15 17 High Clearcut 0.863 1.110 1.209 0.035 15 17 High Clearcut 1.012 0.893 1.043 0.051 15 17 High Clearcut 0.829 1.047 1.704 0.1288 15 17 High Clearcut 0.842 2.687 1.570 0.0126 15 17 High Clearcut 1.116 1.246 1.121 0.0064 15 17 High Clearcut 1.069 1.115 1.296 0.0513 15 17 High Clearcut 0.968 1.009 1.455 0.066 15 17 High Clearcut 1.222 1.400 1.364 0.043 15 17 High Clearcut 1.069 1.547 1.530 0.0342 15 18 High Clearcut 0.805 1.502 1.701 0.016 15 18 High Clearcut 1.075 1.643 1.568 0.018 15 18 High Clearcut 0.959 1.325 1.275 0.0171 15 18 High Clearcut 1.110 1.475 1.502 0.008 15 18 High Clearcut 0.721 1.027 1.662 0.015 15 18 High Clearcut 0.979 1.998 1.390 0.017 15 18 High Clearcut 1.276 1.736 1.416 0.0123 15 18 High Clearcut 1.166 1.376 1.462 0.0012 15 18 High Clearcut 1.163 1.546 1.718 0.008 15 18 High Clearcut 1.391 1.756 1.820 0.0028 15 18 High Clearcut 1.372 1.252 1.932 0 15 18 High Clearcut 1.128 1.684 1.891 0 15 19 None Partial 1.093 1.088 1.211 0 0 19 None Partial 0.887 0.974 1.092 0 0 19 None Partial 1.071 1.220 1.579 0 0 19 None Partial 0.828 1.237 1.349 0 0 19 None Partial 1.083 1.461 1.498 0 0 19 None Partial 0.714 0.885 1.260 0 0 19 None Partial 0.795 1.304 1.201 0 0 19 None Partial 0.445 1.604 1.328 0 0 19 None Partial 0.807 0.721 1.128 0 0 19 None Partial 1.141 1.196 1.152 0 0 19 None Partial 0.797 1.106 1.102 0 0 19 None Partial 1.130 1.480 1.158 0 0 19 None Partial 1.162 1.711 1.168 0 0 19 None Partial 0.652 0.929 1.228 0 0 19 None Partial 0.923 1.162 1.399 0 0 19 None Partial 0.796 1.292 1.080 0 0 19 None Partial 0.847 1.080 0.745 0 0 19 None Partial 1.025 1.112 2.670 0 0 19 None Clearcut 1.060 1.425 1.219 0 0 19 None Partial 0.753 0.860 1.571 0 0 19 None Partial 1.290 1.146 1.146 0 0 19 None Partial 0.932 1.638 1.695 0 0 19 None Partial 0.956 0.975 0.867 0 0 19 None Partial 0.828 1.377 1.131 0 0 19 None Clearcut 1.003 1.753 1.527 0 0 19 None Clearcut 0.819 1.057 1.374 0 0 19 None Partial 0.949 1.220 0.891 0 0 19 None Clearcut 0.708 1.770 1.330 0 0 19 None Clearcut 0.877 1.109 1.402 0 0 19 None Clearcut 1.006 1.946 1.522 0 0 19 None Partial 0.827 2.489 1.396 0 0 ; PROC GLM; class traffic overstory_trmt plot; model BD_zero_to_five_cm = traffic overstory_trmt plot(traffic overstory_trmt); test h=traffic overstory_trmt e=plot(traffic overstory_trmt); means traffic overstory_trmt / waller e=plot(traffic overstory_trmt); LSMeans traffic / Adjust = Tukey; LSMeans overstory_trmt / Adjust = Tukey; run; PROC GLM; class traffic overstory_trmt plot; model BD_five_to_ten_cm = traffic overstory_trmt plot(traffic overstory_trmt); test h=traffic overstory_trmt e=plot(traffic overstory_trmt); means traffic overstory_trmt / waller e=plot(traffic overstory_trmt); LSMeans traffic / Adjust = Tukey; LSMeans overstory_trmt / Adjust = Tukey; run; PROC GLM; class traffic overstory_trmt plot; model BD_ten_to_twenty_cm = traffic overstory_trmt plot(traffic overstory_trmt); test h=traffic overstory_trmt e=plot(traffic overstory_trmt); means traffic overstory_trmt / waller e=plot(traffic overstory_trmt); LSMeans traffic / Adjust = Tukey; LSMeans overstory_trmt / Adjust = Tukey; run;

Ra6 · ‎07-27-2018

Thanks for your suggestion. I got it.

Ra6 · ‎07-27-2018

Hi, I am facing a problem to run my sas code. My advisor's advice was, "When you run your ANOVA, it will provide you with averages for each treatment classification. Essentially, you are using ANOVA to compare means among multiple treatment classifications to determine if the bulk densities are significantly different among the different treatments. The covariates being snow depth and slash volume. That will tell you if those two variables (snow depth and slash volume) had any significant effect on each depth of bulk density." I tried a lot but I could not solve. Here is my code: libname rafia 'C:\Users\rafiar\Documents\My SAS Files'; data New_data; Input Plot $ Sample $ percentage_of_rock_cover $ zero_to_five $ five_to_ten $ ten_to_twenty $ Slash_Volume $ Average_Snow_Depth; datalines; 1 1 0.5 1.323 1.896 1.410 0 12 1 2 1 1.019 1.518 1.260 0.0127 12 1 3 1 1.057 1.103 1.583 0.002 12 1 4 0.5 1.218 1.631 1.236 0.00712 12 1 5 0 0.990 1.294 0.982 0 12 1 6 0.5 1.081 1.348 1.098 0.0096 12 1 7 1 1.166 1.273 1.470 0.0081 12 1 8 17 1.017 1.150 1.251 0.0026 12 1 9 4 1.293 1.558 1.127 0.004 12 1 10 3 1.087 1.433 1.159 0 12 1 11 0.5 1.027 1.394 1.376 0.01 12 1 12 5 1.120 1.570 1.501 0 12 2 1 0.5 1.050 1.134 1.022 0 12 2 2 0 1.322 1.553 1.555 0 12 2 3 0 1.147 1.350 1.424 0 12 2 4 0 0.851 1.411 1.272 0 12 2 5 0.5 0.868 1.262 1.040 0 12 2 6 1 0.881 1.245 1.156 0 12 2 7 0 1.069 1.047 1.047 0 12 2 8 0 0.987 1.703 1.481 0 12 2 9 15 1.154 1.346 1.181 0 12 2 10 0 1.076 1.255 1.184 0 12 2 11 0 1.062 1.286 1.165 0.0078 12 2 12 0 1.194 1.320 1.627 0 12 3 1 1 0.966 1.062 0.938 0 15 3 2 2 1.001 1.085 0.940 0.027 15 3 3 1 1.217 1.136 1.133 0 15 3 4 3 0.800 1.193 1.259 0.0088 15 3 5 0 1.203 1.460 1.280 0.0096 15 3 6 0 1.162 1.485 1.276 0.0364 15 3 7 0 1.104 1.457 1.204 0.0042 15 3 8 0 1.297 1.301 1.578 0.0024 15 3 9 0 1.332 1.337 1.375 0.0488 15 3 10 0 0.560 1.434 1.069 0 15 3 11 0 1.017 1.154 1.258 0.0024 15 3 12 0.5 0.925 0.922 1.094 0 15 4 1 0 1.200 1.399 1.089 0 3 4 2 15 1.228 1.692 1.227 0 3 4 3 2 1.058 1.410 1.166 0 3 4 4 0 1.235 1.006 0.979 0 3 4 5 0.5 1.589 1.485 1.283 0 3 4 6 10 0.948 1.292 1.291 0 3 4 7 1 1.067 0.947 1.190 0 3 4 8 0.5 1.019 1.157 1.236 0 3 4 9 2 1.344 1.340 1.522 0 3 4 10 1 0.900 1.580 1.289 0 3 4 11 5 1.463 1.589 1.560 0 3 4 12 5 0.073 1.210 1.182 0 3 5 1 0 1.117 1.317 0.986 0.153 1 5 2 0 1.006 1.338 1.050 0 1 5 3 0 0.958 1.008 1.029 0.044 1 5 4 0 0.893 0.992 0.939 0.0096 1 5 5 0 0.978 0.899 0.861 0.0056 1 5 6 0 0.936 1.101 1.240 0.012 1 5 7 0 0.879 0.818 0.926 0.014 1 5 8 0 1.103 1.301 1.141 0.038 1 5 9 0 0.582 1.013 1.046 0.0052 1 5 10 0 1.167 1.301 0.965 0.0132 1 5 11 0 0.895 0.874 1.015 0.0028 1 5 12 0 1.196 1.202 1.076 0.0135 1 6 1 0 0.982 1.452 2.161 0 8 6 2 0.5 1.149 1.349 1.430 0 8 6 3 0.5 1.193 2.184 1.569 0 8 6 4 0 1.296 2.065 1.373 0 8 6 5 0 0.921 1.178 1.373 0 8 6 6 0.5 1.589 1.795 1.750 0 8 6 7 0 0.862 1.169 1.518 0 8 6 8 1 1.315 1.559 1.494 0 8 6 9 0.5 1.176 1.206 1.537 0 8 6 10 0 1.444 1.737 1.658 0 8 6 11 0 1.462 2.359 0.898 0 8 6 12 0 1.396 1.585 1.680 0 8 7 1 0 1.174 1.005 1.251 0 3 7 2 0 1.107 1.621 1.517 0 3 7 3 0 1.028 1.160 1.028 0 3 7 4 2 1.293 1.310 1.115 0 3 7 5 0.5 0.944 1.181 0.896 0 3 7 6 0 1.329 1.790 1.067 0 3 7 7 0 0.932 1.283 0.912 0 3 7 8 0 1.052 0.932 1.258 0 3 7 9 2 0.682 0.850 1.277 0 3 7 10 0.5 1.054 1.095 1.052 0 3 7 11 0.5 1.100 1.102 1.193 0 3 7 12 0 1.074 1.114 1.173 0 3 8 1 0 0.984 1.129 1.089 0 3 8 2 0 1.113 1.136 1.036 0 3 8 3 0 0.866 1.484 1.113 0 3 8 4 0 1.027 1.385 1.347 0 3 8 5 0 0.743 1.005 1.355 0 3 8 6 0 1.144 1.397 1.317 0 3 8 7 0 0.831 1.350 1.198 0 3 8 8 2 0.889 1.184 0.936 0 3 8 9 0 0.954 1.566 1.161 0 3 8 10 0 0.789 1.254 1.045 0 3 8 11 0 1.199 1.396 1.072 0 3 8 12 0.5 0.895 1.398 1.317 0 3 9 1 2 1.472 1.683 1.520 0.0328 8 9 2 0.5 1.052 1.406 1.311 0.0288 8 9 3 1 1.341 1.404 1.382 0.0286 8 9 4 0 1.114 0.116 1.350 0.0066 8 9 5 3 1.211 1.294 1.280 0.0116 8 9 6 0.5 1.126 1.052 1.488 0 8 9 7 10 1.204 1.262 0.873 0.021 8 9 8 0.5 1.218 1.223 1.155 0.0216 8 9 9 0 1.265 1.346 1.248 0.0022 8 9 10 0.5 1.218 1.105 1.180 0 8 9 11 0 1.080 1.361 1.532 0.0036 8 9 12 0 1.000 1.111 1.233 0 8 10 1 0 1.054 1.554 1.666 0 9 10 2 0 1.721 1.351 1.623 0.009 9 10 3 0.5 1.364 1.624 1.746 0.0042 9 10 4 0.5 0.955 1.153 1.348 0 9 10 5 0 1.150 1.817 1.467 0.01 9 10 6 0 1.820 1.666 1.545 0.0024 9 10 7 0 1.021 1.558 1.673 0.0108 9 10 8 0.5 0.650 1.131 1.304 0.0145 9 10 9 2 1.580 1.400 1.544 0 9 10 10 1 1.047 1.988 1.481 0 9 10 11 0 1.270 1.784 1.693 0.0042 9 10 12 1 0.808 1.088 1.895 0.0112 9 11 1 2 0.851 1.295 1.905 0.0102 15 11 2 0 1.535 1.269 1.279 0.012 15 11 3 0 1.346 1.680 1.697 0.018 15 11 4 0 1.084 1.546 1.907 0.0175 15 11 5 0 0.869 1.229 1.387 0.0351 15 11 6 0 1.865 0.964 1.309 0.0144 15 11 7 1 1.312 1.257 1.463 0.0028 15 11 8 0 1.091 1.169 1.191 0.0037 15 11 9 0 1.403 1.394 1.635 0.0032 15 11 10 0 0.735 1.031 1.283 0 15 11 11 0 0.953 1.005 1.669 0 15 11 12 0 0.704 0.963 1.277 0.0052 15 12 1 5 0.896 1.427 1.678 0.0342 16 12 2 3 0.969 1.900 1.806 0.0096 16 12 3 3 0.607 1.090 1.397 0.0026 16 12 4 0.5 1.804 1.645 1.609 0.0253 16 12 5 4 0.683 1.315 1.525 0.048 16 12 6 15 1.154 2.762 1.619 0 16 12 7 10 0.778 1.495 1.830 0.008 16 12 8 10 0.790 1.153 1.747 0.076 16 12 9 25 1.393 1.995 2.008 0 16 12 10 2 0.787 0.804 0.417 0.0084 16 12 11 1 0.865 0.831 1.023 0.0132 16 12 12 2 0.887 2.708 1.178 0.0261 16 13 1 0.5 1.087 1.462 1.034 0.0115 19 13 2 0 0.954 1.275 1.036 0.0096 19 13 3 0 1.002 1.430 1.234 0.016 19 13 4 0 1.491 1.175 1.295 0.0075 19 13 5 0 1.131 1.299 1.074 0.0357 19 13 6 0 0.991 0.969 1.022 0.0062 19 13 7 0 1.004 1.205 1.109 0.0301 19 13 8 0 1.201 1.058 1.029 0.014 19 13 9 0 1.073 0.981 1.038 0.0132 19 13 10 0 1.086 1.304 1.236 0.076 19 13 11 0 0.995 1.084 1.386 0.0144 19 13 12 0 0.715 0.791 1.081 0.0115 19 14 1 0.5 1.353 1.694 1.419 0 14 14 2 0 0.912 1.241 1.180 0 14 14 3 0 0.945 1.381 1.418 0.005 14 14 4 0 0.771 1.340 1.270 0.001 14 14 5 0 0.842 1.120 1.341 0.01 14 14 6 0 0.864 0.952 0.991 0 14 14 7 0 1.132 1.981 1.099 0 14 14 8 0 1.027 1.376 1.100 0.046 14 14 9 5 0.992 1.529 0.869 0 14 14 10 0 1.030 1.430 2.150 0.0014 14 14 11 0 0.983 1.607 0.834 0.016 14 14 12 0 1.093 3.007 0.531 0.004 14 15 1 0.5 0.779 1.457 1.329 0 12.5 15 2 2 1.500 1.058 2.075 0 12.5 15 3 0.5 1.006 1.282 1.668 0 12.5 15 4 0 0.542 0.758 1.461 0 12.5 15 5 0 1.296 0.786 1.216 0 12.5 15 6 0 0.916 1.125 1.097 0 12.5 15 7 0 0.857 1.105 1.333 0 12.5 15 8 1 0.923 1.014 1.035 0 12.5 15 9 0 1.003 0.942 1.021 0 12.5 15 10 0 0.905 1.064 1.047 0 12.5 15 11 0 0.904 0.988 1.291 0 12.5 15 12 1 1.167 1.315 1.385 0 12.5 16 1 0 1.125 3.074 0.557 0.0052 8.5 16 2 0 0.923 1.035 1.114 0.06 8.5 16 3 0 0.865 1.262 1.315 0.0308 8.5 16 4 0 1.540 1.049 0.803 0 8.5 16 5 0 1.037 2.497 1.948 0.03 8.5 16 6 26 1.332 2.287 1.951 0 8.5 16 7 0 1.320 1.506 1.721 0.003 8.5 16 8 0 1.143 1.401 1.314 0.018 8.5 16 9 1 1.336 1.464 1.541 0.0161 8.5 16 10 0 1.141 1.319 1.083 0 8.5 16 11 0 0.904 1.279 1.077 0.04275 8.5 16 12 0 0.906 1.175 1.167 0.0108 8.5 17 1 0 0.827 0.950 1.440 0.0759 15 17 2 0 1.054 1.078 1.171 0.0672 15 17 3 0 0.883 1.070 0.961 0.0867 15 17 4 0 0.863 1.110 1.209 0.035 15 17 5 0 1.012 0.893 1.043 0.051 15 17 6 0 0.829 1.047 1.704 0.1288 15 17 7 0.5 0.842 2.687 1.570 0.0126 15 17 8 0 1.116 1.246 1.121 0.0064 15 17 9 0 1.069 1.115 1.296 0.0513 15 17 10 0 0.968 1.009 1.455 0.066 15 17 11 0 1.222 1.400 1.364 0.043 15 17 12 0 1.069 1.547 1.530 0.0342 15 18 1 0 0.805 1.502 1.701 0.016 15 18 2 0 1.075 1.643 1.568 0.018 15 18 3 1 0.959 1.325 1.275 0.0171 15 18 4 2 1.110 1.475 1.502 0.008 15 18 5 0.5 0.721 1.027 1.662 0.015 15 18 6 0 0.979 1.998 1.390 0.017 15 18 7 0 1.276 1.736 1.416 0.0123 15 18 8 5 1.166 1.376 1.462 0.0012 15 18 9 0.5 1.163 1.546 1.718 0.008 15 18 10 0 1.391 1.756 1.820 0.0028 15 18 11 2 1.372 1.252 1.932 0 15 18 12 0.5 1.128 1.684 1.891 0 15 Rand 1 0 1.093 1.088 1.211 0 0 Rand 2 0 0.887 0.974 1.092 0 0 Rand 3 5 1.071 1.220 1.579 0 0 Rand 4 0 0.828 1.237 1.349 0 0 Rand 5 0 1.083 1.461 1.498 0 0 Rand 6 0 0.714 0.885 1.260 0 0 Rand 7 0 0.795 1.304 1.201 0 0 Rand 8 0 0.445 1.604 1.328 0 0 Rand 9 0 0.807 0.721 1.128 0 0 Rand 10 0.5 1.141 1.196 1.152 0 0 Rand 11 2 0.797 1.106 1.102 0 0 Rand 12 1 1.130 1.480 1.158 0 0 Rand 13 2 1.162 1.711 1.168 0 0 Rand 14 2 0.652 0.929 1.228 0 0 Rand 15 2 0.923 1.162 1.399 0 0 Rand 16 0 0.796 1.292 1.080 0 0 Rand 17 0 0.847 1.080 0.745 0 0 Rand 18 1 1.025 1.112 2.670 0 0 Rand 19 0 1.060 1.425 1.219 0 0 Rand 20 1 0.753 0.860 1.571 0 0 Rand 21 0 1.290 1.146 1.146 0 0 Rand 22 5 0.932 1.638 1.695 0 0 Rand 23 0.5 0.956 0.975 0.867 0 0 Rand 24 3 0.828 1.377 1.131 0 0 Rand 25 0 1.003 1.753 1.527 0 0 Rand 26 0.5 0.819 1.057 1.374 0 0 Rand 27 0 0.949 1.220 0.891 0 0 Rand 28 0 0.708 1.770 1.330 0 0 Rand 29 0.5 0.877 1.109 1.402 0 0 Rand 30 0 1.006 1.946 1.522 0 0 Rand 31 2 0.827 2.489 1.396 0 0 PROC ANOVA data=New_data; class plot; model zero_to_five five_to_ten ten_to_twenty = plot; manova h = plot; run; proc reg data = New_data; model zero_to_five = Slash_Volume Average_Snow_Depth; run; proc reg data = New_data; model five_to_ten = Slash_Volume Average_Snow_Depth; run; proc reg data = New_data; model ten_to_twenty= Slash_Volume Average_Snow_Depth; run; proc print; run; proc import datafile = '\\homedir.mtu.edu\home\My SAS Files\Soil.csv' out = work.Soil dbms = CSV ; run; I am also attaching the excel file here

Ra6 · ‎07-11-2018

Hi, Thanks for the reply. We will have to compare the variables. I am posting here an example and the solution for your convenience. Here is only one covariate with CRD factorial but we will have to work with two covariates according to my dataset. Example: Input Water$ FertTreat$ Plot Plant InitialHt Height; datalines; Hi Control 1 1 5 12.7 Hi Control 2 1 6.5 16.6 Hi Control 3 1 5.5 15.7 Hi Control 4 1 5.5 16.9 Hi Control 5 1 5.1 14.2 Hi Liquid 6 1 6.2 23.2 Hi Liquid 7 1 3.4 20.9 Hi Liquid 8 1 7.1 25 Hi Liquid 9 1 8.3 24.6 Hi Liquid 10 1 5.6 25.7 Hi Pellet 11 1 3.7 20.1 Hi Pellet 12 1 1.4 19.9 Hi Pellet 13 1 3.6 20.8 Hi Pellet 14 1 6.4 20.6 Hi Pellet 15 1 5.6 23 Hi SlowRele 16 1 6.9 21.7 Hi SlowRele 17 1 5.8 19.8 Hi SlowRele 18 1 4.8 18.4 Hi SlowRele 19 1 10.1 19.3 Hi SlowRele 20 1 3.9 21.9 Lo Control 21 1 4.5 14.8 Lo Control 22 1 6.8 16.5 Lo Control 23 1 4.9 12.7 Lo Control 24 1 3.6 13.4 Lo Control 25 1 4.1 10.6 Lo Liquid 26 1 7.4 27.7 Lo Liquid 27 1 10 28.1 Lo Liquid 27 1 9.8 27 Lo Liquid 28 1 7.4 23.9 Lo Liquid 30 1 7.1 29.1 Lo Pellet 31 1 5.9 21.5 Lo Pellet 32 1 9.6 20.5 Lo Pellet 33 1 2.6 19.5 Lo Pellet 34 1 5.3 18.9 Lo Pellet 35 1 4.8 20 Lo SlowRele 36 1 5.1 19.2 Lo SlowRele 37 1 4.6 18.3 Lo SlowRele 38 1 1.1 20.3 Lo SlowRele 39 1 6.3 21.2 Lo SlowRele 40 1 5.4 20.1 Hi Control 1 2 5 13.1 Hi Control 2 2 7 17.2 Hi Control 3 2 5.6 16.6 Hi Control 4 2 5.9 17.4 Hi Control 5 2 5.2 14.4 Hi Liquid 6 2 6.6 24.7 Hi Liquid 7 2 3.5 22.7 Hi Liquid 8 2 7.5 25.5 Hi Liquid 9 2 8.6 26.3 Hi Liquid 10 2 5.6 27 Hi Pellet 11 2 3.9 22 Hi Pellet 12 2 1.8 21.5 Hi Pellet 13 2 4 22.1 Hi Pellet 14 2 6.8 22.1 Hi Pellet 15 2 6 24.9 Hi SlowRele 16 2 7.2 22.4 Hi SlowRele 17 2 5.8 21.6 Hi SlowRele 18 2 5.2 18.7 Hi SlowRele 19 2 10.4 19.8 Hi SlowRele 20 2 4.3 22 Lo Control 21 2 5 15.2 Lo Control 22 2 6.8 17.1 Lo Control 23 2 5.1 13.2 Lo Control 24 2 4.1 14.6 Lo Control 25 2 4.2 12 Lo Liquid 26 2 7.8 29.6 Lo Liquid 27 2 10.2 28.6 Lo Liquid 27 2 9.9 28.2 Lo Liquid 28 2 7.5 24.8 Lo Liquid 30 2 7.3 30.2 Lo Pellet 31 2 6.2 22.5 Lo Pellet 32 2 9.8 21.2 Lo Pellet 33 2 2.7 20.6 Lo Pellet 34 2 5.6 19.9 Lo Pellet 35 2 5 20.5 Lo SlowRele 36 2 5.4 20.8 Lo SlowRele 37 2 4.6 18.6 Lo SlowRele 38 2 1.1 21.9 Lo SlowRele 39 2 6.5 22.5 Lo SlowRele 40 2 5.9 20.2 ; proc print; run; %INCLUDE '\\homedir.mtu.edu\home\Desktop\CRD'; %MMAOV (rahman,height, CLASS= plot FertTreat water, FIXED = FertTreat|water initialHt,RANDOM=plot(FertTreat*water) ); Solution: C:\Users\Rafia\Documents\Soil Data\SAS 2018\SAS Output_htm#IDX35.mht

Ra6 · ‎07-10-2018

Here is the code according to the attached excel file that I tried to run but I am getting error every time. How can I solve the problem? libname rafia 'C:\Users\rafiar\Documents\My SAS Files'; data rahman; Input Plot $ Sample $ Zero_to_Five_cm $ Five_to_Ten_cm $ Ten_to_Twenty_cm $ Slash_Volume $ Average_Snow_Depth; datalines; 1 1 1.323 1.896 1.410 0 12 1 2 1.019 1.518 1.260 0.0127 12 1 3 1.057 1.103 1.583 0.002 12 1 4 1.218 1.631 1.236 0.00712 12 1 5 0.990 1.294 0.982 0 12 1 6 1.081 1.348 1.098 0.0096 12 1 7 1.166 1.273 1.470 0.0081 12 1 8 1.017 1.150 1.251 0.0026 12 1 9 1.293 1.558 1.127 0.004 12 1 10 1.087 1.433 1.159 0 12 1 11 1.027 1.394 1.376 0.01 12 1 12 1.120 1.570 1.501 0 12 2 1 1.050 1.134 1.022 0 12 2 2 1.322 1.553 1.555 0 12 2 3 1.147 1.350 1.424 0 12 2 4 0.851 1.411 1.272 0 12 2 5 0.868 1.262 1.040 0 12 2 6 0.881 1.245 1.156 0 12 2 7 1.069 1.047 1.047 0 12 2 8 0.987 1.703 1.481 0 12 2 9 1.154 1.346 1.181 0 12 2 10 1.076 1.255 1.184 0 12 2 11 1.062 1.286 1.165 0.0078 12 2 12 1.194 1.320 1.627 0 12 3 1 0.966 1.062 0.938 0 15 3 2 1.001 1.085 0.940 0.027 15 3 3 1.217 1.136 1.133 0 15 3 4 0.800 1.193 1.259 0.0088 15 3 5 1.203 1.460 1.280 0.0096 15 3 6 1.162 1.485 1.276 0.0364 15 3 7 1.104 1.457 1.204 0.0042 15 3 8 1.297 1.301 1.578 0.0024 15 3 9 1.332 1.337 1.375 0.0488 15 3 10 0.560 1.434 1.069 0 15 3 11 1.017 1.154 1.258 0.0024 15 3 12 0.925 0.922 1.094 0 15 4 1 1.200 1.399 1.089 0 3 4 2 1.228 1.692 1.227 0 3 4 3 1.058 1.410 1.166 0 3 4 4 1.235 1.006 0.979 0 3 4 5 1.589 1.485 1.283 0 3 4 6 0.948 1.292 1.291 0 3 4 7 1.067 0.947 1.190 0 3 4 8 1.019 1.157 1.236 0 3 4 9 1.344 1.340 1.522 0 3 4 10 0.900 1.580 1.289 0 3 4 11 1.463 1.589 1.560 0 3 4 12 0.073 1.210 1.182 0 3 5 1 1.117 1.317 0.986 0.153 1 5 2 1.006 1.338 1.050 0 1 5 3 0.958 1.008 1.029 0.044 1 5 4 0.893 0.992 0.939 0.0096 1 5 5 0.978 0.899 0.861 0.0056 1 5 6 0.936 1.101 1.240 0.012 1 5 7 0.879 0.818 0.926 0.014 1 5 8 1.103 1.301 1.141 0.038 1 5 9 0.582 1.013 1.046 0.0052 1 5 10 1.167 1.301 0.965 0.0132 1 5 11 0.895 0.874 1.015 0.0028 1 5 12 1.196 1.202 1.076 0.0135 1 6 1 0.982 1.452 2.161 0 8 6 2 1.149 1.349 1.430 0 8 6 3 1.193 2.184 1.569 0 8 6 4 1.296 2.065 1.373 0 8 6 5 0.921 1.178 1.373 0 8 6 6 1.589 1.795 1.750 0 8 6 7 0.862 1.169 1.518 0 8 6 8 1.315 1.559 1.494 0 8 6 9 1.176 1.206 1.537 0 8 6 10 1.444 1.737 1.658 0 8 6 11 1.462 2.359 0.898 0 8 6 12 1.396 1.585 1.680 0 8 7 1 1.174 1.005 1.251 0 3 7 2 1.107 1.621 1.517 0 3 7 3 1.028 1.160 1.028 0 3 7 4 1.293 1.310 1.115 0 3 7 5 0.944 1.181 0.896 0 3 7 6 1.329 1.790 1.067 0 3 7 7 0.932 1.283 0.912 0 3 7 8 1.052 0.932 1.258 0 3 7 9 0.682 0.850 1.277 0 3 7 10 1.054 1.095 1.052 0 3 7 11 1.100 1.102 1.193 0 3 7 12 1.074 1.114 1.173 0 3 8 1 0.984 1.129 1.089 0 3 8 2 1.113 1.136 1.036 0 3 8 3 0.866 1.484 1.113 0 3 8 4 1.027 1.385 1.347 0 3 8 5 0.743 1.005 1.355 0 3 8 6 1.144 1.397 1.317 0 3 8 7 0.831 1.350 1.198 0 3 8 8 0.889 1.184 0.936 0 3 8 9 0.954 1.566 1.161 0 3 8 10 0.789 1.254 1.045 0 3 8 11 1.199 1.396 1.072 0 3 8 12 0.895 1.398 1.317 0 3 9 1 1.472 1.683 1.520 0.0328 8 9 2 1.052 1.406 1.311 0.0288 8 9 3 1.341 1.404 1.382 0.0286 8 9 4 1.114 0.116 1.350 0.0066 8 9 5 1.211 1.294 1.280 0.0116 8 9 6 1.126 1.052 1.488 0 8 9 7 1.204 1.262 0.873 0.021 8 9 8 1.218 1.223 1.155 0.0216 8 9 9 1.265 1.346 1.248 0.0022 8 9 10 1.218 1.105 1.180 0 8 9 11 1.080 1.361 1.532 0.0036 8 9 12 1.000 1.111 1.233 0 8 10 1 1.054 1.554 1.666 0 9 10 2 1.721 1.351 1.623 0.009 9 10 3 1.364 1.624 1.746 0.0042 9 10 4 0.955 1.153 1.348 0 9 10 5 1.150 1.817 1.467 0.01 9 10 6 1.820 1.666 1.545 0.0024 9 10 7 1.021 1.558 1.673 0.0108 9 10 8 0.650 1.131 1.304 0.0145 9 10 9 1.580 1.400 1.544 0 9 10 10 1.047 1.988 1.481 0 9 10 11 1.270 1.784 1.693 0.0042 9 10 12 0.808 1.088 1.895 0.0112 9 11 1 0.851 1.295 1.905 0.0102 15 11 2 1.535 1.269 1.279 0.012 15 11 3 1.346 1.680 1.697 0.018 15 11 4 1.084 1.546 1.907 0.0175 15 11 5 0.869 1.229 1.387 0.0351 15 11 6 1.865 0.964 1.309 0.0144 15 11 7 1.312 1.257 1.463 0.0028 15 11 8 1.091 1.169 1.191 0.0037 15 11 9 1.403 1.394 1.635 0.0032 15 11 10 0.735 1.031 1.283 0 15 11 11 0.953 1.005 1.669 0 15 11 12 0.704 0.963 1.277 0.0052 15 12 1 0.896 1.427 1.678 0.0342 16 12 2 0.969 1.900 1.806 0.0096 16 12 3 0.607 1.090 1.397 0.0026 16 12 4 1.804 1.645 1.609 0.0253 16 12 5 0.683 1.315 1.525 0.048 16 12 6 1.154 2.762 1.619 0 16 12 7 0.778 1.495 1.830 0.008 16 12 8 0.790 1.153 1.747 0.076 16 12 9 1.393 1.995 2.008 0 16 12 10 0.787 0.804 0.417 0.0084 16 12 11 0.865 0.831 1.023 0.0132 16 12 12 0.887 2.708 1.178 0.0261 16 13 1 1.087 1.462 1.034 0.0115 19 13 2 0.954 1.275 1.036 0.0096 19 13 3 1.002 1.430 1.234 0.016 19 13 4 1.491 1.175 1.295 0.0075 19 13 5 1.131 1.299 1.074 0.0357 19 13 6 0.991 0.969 1.022 0.0062 19 13 7 1.004 1.205 1.109 0.0301 19 13 8 1.201 1.058 1.029 0.014 19 13 9 1.073 0.981 1.038 0.0132 19 13 10 1.086 1.304 1.236 0.076 19 13 11 0.995 1.084 1.386 0.0144 19 13 12 0.715 0.791 1.081 0.0115 19 14 1 1.353 1.694 1.419 0 14 14 2 0.912 1.241 1.180 0 14 14 3 0.945 1.381 1.418 0.005 14 14 4 0.771 1.340 1.270 0.001 14 14 5 0.842 1.120 1.341 0.01 14 14 6 0.864 0.952 0.991 0 14 14 7 1.132 1.981 1.099 0 14 14 8 1.027 1.376 1.100 0.046 14 14 9 0.992 1.529 0.869 0 14 14 10 1.030 1.430 2.150 0.0014 14 14 11 0.983 1.607 0.834 0.016 14 14 12 1.093 3.007 0.531 0.004 14 15 1 0.779 1.457 1.329 0 12.5 15 2 1.500 1.058 2.075 0 12.5 15 3 1.006 1.282 1.668 0 12.5 15 4 0.542 0.758 1.461 0 12.5 15 5 1.296 0.786 1.216 0 12.5 15 6 0.916 1.125 1.097 0 12.5 15 7 0.857 1.105 1.333 0 12.5 15 8 0.923 1.014 1.035 0 12.5 15 9 1.003 0.942 1.021 0 12.5 15 10 0.905 1.064 1.047 0 12.5 15 11 0.904 0.988 1.291 0 12.5 15 12 1.167 1.315 1.385 0 12.5 16 1 1.125 3.074 0.557 0.0052 8.5 16 2 0.923 1.035 1.114 0.06 8.5 16 3 0.865 1.262 1.315 0.0308 8.5 16 4 1.540 1.049 0.803 0 8.5 16 5 1.037 2.497 1.948 0.03 8.5 16 6 1.332 2.287 1.951 0 8.5 16 7 1.320 1.506 1.721 0.003 8.5 16 8 1.143 1.401 1.314 0.018 8.5 16 9 1.336 1.464 1.541 0.0161 8.5 16 10 1.141 1.319 1.083 0 8.5 16 11 0.904 1.279 1.077 0.04275 8.5 16 12 0.906 1.175 1.167 0.0108 8.5 17 1 0.827 0.950 1.440 0.0759 15 17 2 1.054 1.078 1.171 0.0672 15 17 3 0.883 1.070 0.961 0.0867 15 17 4 0.863 1.110 1.209 0.035 15 17 5 1.012 0.893 1.043 0.051 15 17 6 0.829 1.047 1.704 0.1288 15 17 7 0.842 2.687 1.570 0.0126 15 17 8 1.116 1.246 1.121 0.0064 15 17 9 1.069 1.115 1.296 0.0513 15 17 10 0.968 1.009 1.455 0.066 15 17 11 1.222 1.400 1.364 0.043 15 17 12 1.069 1.547 1.530 0.0342 15 18 1 0.805 1.502 1.701 0.016 15 18 2 1.075 1.643 1.568 0.018 15 18 3 0.959 1.325 1.275 0.0171 15 18 4 1.110 1.475 1.502 0.008 15 18 5 0.721 1.027 1.662 0.015 15 18 6 0.979 1.998 1.390 0.017 15 18 7 1.276 1.736 1.416 0.0123 15 18 8 1.166 1.376 1.462 0.0012 15 18 9 1.163 1.546 1.718 0.008 15 18 10 1.391 1.756 1.820 0.0028 15 18 11 1.372 1.252 1.932 0 15 18 12 1.128 1.684 1.891 0 15 Rand 1 1.093 1.088 1.211 0 0 Rand 2 0.887 0.974 1.092 0 0 Rand 3 1.071 1.220 1.579 0 0 Rand 4 0.828 1.237 1.349 0 0 Rand 5 1.083 1.461 1.498 0 0 Rand 6 0.714 0.885 1.260 0 0 Rand 7 0.795 1.304 1.201 0 0 Rand 8 0.445 1.604 1.328 0 0 Rand 9 0.807 0.721 1.128 0 0 Rand 10 1.141 1.196 1.152 0 0 Rand 11 0.797 1.106 1.102 0 0 Rand 12 1.130 1.480 1.158 0 0 Rand 13 1.162 1.711 1.168 0 0 Rand 14 0.652 0.929 1.228 0 0 Rand 15 0.923 1.162 1.399 0 0 Rand 16 0.796 1.292 1.080 0 0 Rand 17 0.847 1.080 0.745 0 0 Rand 18 1.025 1.112 2.670 0 0 Rand 19 1.060 1.425 1.219 0 0 Rand 20 0.753 0.860 1.571 0 0 Rand 21 1.290 1.146 1.146 0 0 Rand 22 0.932 1.638 1.695 0 0 Rand 23 0.956 0.975 0.867 0 0 Rand 24 0.828 1.377 1.131 0 0 Rand 25 1.003 1.753 1.527 0 0 Rand 26 0.819 1.057 1.374 0 0 Rand 27 0.949 1.220 0.891 0 0 Rand 28 0.708 1.770 1.330 0 0 Rand 29 0.877 1.109 1.402 0 0 Rand 30 1.006 1.946 1.522 0 0 Rand 31 0.827 2.489 1.396 0 0 ; proc print; run; %INCLUDE ''\\homedir.mtu.edu\home\My SAS Files''; %MMAOV (rahman, Slash_Volume Average_Snow_Depth, CLASS= plot Zero_to_Five_cm Five_to_Ten_cm Ten_to_Twenty_cm, FIXED= Zero_to_Five_cm Five_to_Ten_cm Ten_to_Twenty_cm slash_volume average_snow_depth, RANDOM= plot sample Zero_to_Five_cm Five_to_Ten_cm Ten_to_Twenty_cm ;

Ra6 · ‎07-09-2018

Hi, Thank you for the reply. Actually, I am facing problem with the data of new excel file that I have already attached with the previous message. I tried several time to run the file in SAS, as one variable has 3 sub division that's why I could not put the table in SAS. I am again attaching here the excel file. This one is the new dataset, I will have to work on it.

Ra6 · ‎07-09-2018

Hi, I used a dataset including sampling and covariate. That time I followed the steps perfectly. But, the problem is, I have to use a different dataset now but all the steps will be same but I am getting error everytime. I am attaching here what I did before for convenience and the new dataset too. Now I have to work on new dataset using CRD factorial with two covariates (slash volume and average snow depth). I am using SAS version 9.4. The previous code is: libname rafia '\\homedir.mtu.edu\home\Desktop\CRD'; data rahman; Input Water$ FertTreat$ Plot Plant InitialHt Height; datalines; Hi Control 1 1 5 12.7 Hi Control 2 1 6.5 16.6 Hi Control 3 1 5.5 15.7 Hi Control 4 1 5.5 16.9 Hi Control 5 1 5.1 14.2 Hi Liquid 6 1 6.2 23.2 Hi Liquid 7 1 3.4 20.9 Hi Liquid 8 1 7.1 25 Hi Liquid 9 1 8.3 24.6 Hi Liquid 10 1 5.6 25.7 Hi Pellet 11 1 3.7 20.1 Hi Pellet 12 1 1.4 19.9 Hi Pellet 13 1 3.6 20.8 Hi Pellet 14 1 6.4 20.6 Hi Pellet 15 1 5.6 23 Hi SlowRele 16 1 6.9 21.7 Hi SlowRele 17 1 5.8 19.8 Hi SlowRele 18 1 4.8 18.4 Hi SlowRele 19 1 10.1 19.3 Hi SlowRele 20 1 3.9 21.9 Lo Control 21 1 4.5 14.8 Lo Control 22 1 6.8 16.5 Lo Control 23 1 4.9 12.7 Lo Control 24 1 3.6 13.4 Lo Control 25 1 4.1 10.6 Lo Liquid 26 1 7.4 27.7 Lo Liquid 27 1 10 28.1 Lo Liquid 27 1 9.8 27 Lo Liquid 28 1 7.4 23.9 Lo Liquid 30 1 7.1 29.1 Lo Pellet 31 1 5.9 21.5 Lo Pellet 32 1 9.6 20.5 Lo Pellet 33 1 2.6 19.5 Lo Pellet 34 1 5.3 18.9 Lo Pellet 35 1 4.8 20 Lo SlowRele 36 1 5.1 19.2 Lo SlowRele 37 1 4.6 18.3 Lo SlowRele 38 1 1.1 20.3 Lo SlowRele 39 1 6.3 21.2 Lo SlowRele 40 1 5.4 20.1 Hi Control 1 2 5 13.1 Hi Control 2 2 7 17.2 Hi Control 3 2 5.6 16.6 Hi Control 4 2 5.9 17.4 Hi Control 5 2 5.2 14.4 Hi Liquid 6 2 6.6 24.7 Hi Liquid 7 2 3.5 22.7 Hi Liquid 8 2 7.5 25.5 Hi Liquid 9 2 8.6 26.3 Hi Liquid 10 2 5.6 27 Hi Pellet 11 2 3.9 22 Hi Pellet 12 2 1.8 21.5 Hi Pellet 13 2 4 22.1 Hi Pellet 14 2 6.8 22.1 Hi Pellet 15 2 6 24.9 Hi SlowRele 16 2 7.2 22.4 Hi SlowRele 17 2 5.8 21.6 Hi SlowRele 18 2 5.2 18.7 Hi SlowRele 19 2 10.4 19.8 Hi SlowRele 20 2 4.3 22 Lo Control 21 2 5 15.2 Lo Control 22 2 6.8 17.1 Lo Control 23 2 5.1 13.2 Lo Control 24 2 4.1 14.6 Lo Control 25 2 4.2 12 Lo Liquid 26 2 7.8 29.6 Lo Liquid 27 2 10.2 28.6 Lo Liquid 27 2 9.9 28.2 Lo Liquid 28 2 7.5 24.8 Lo Liquid 30 2 7.3 30.2 Lo Pellet 31 2 6.2 22.5 Lo Pellet 32 2 9.8 21.2 Lo Pellet 33 2 2.7 20.6 Lo Pellet 34 2 5.6 19.9 Lo Pellet 35 2 5 20.5 Lo SlowRele 36 2 5.4 20.8 Lo SlowRele 37 2 4.6 18.6 Lo SlowRele 38 2 1.1 21.9 Lo SlowRele 39 2 6.5 22.5 Lo SlowRele 40 2 5.9 20.2 ; proc print; run; %INCLUDE '\\homedir.mtu.edu\home\Desktop\CRD'; %MMAOV (rahman,height, CLASS= plot FertTreat water, FIXED = FertTreat|water initialHt,RANDOM=plot(FertTreat*water) );

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