项目详情

• 项目名称：小作业数据处理，最好用SAS（其他语言也可以，数据见正文）
• 分类：数据分析
• 发布者： 瘴***
• 点击量：460
• 发布时间：2018-12-14 15:00
• 项目关键词：数据处理
• 项目描述：
  Homework #1 (Due: 10/20/2015):
  
  1.
  a. Consider the following data set. Copy and Save all the material from this data set to a text file and name it as "state.txt".
  
  01 Alabama e 6 112 -27 3893888 77 35.600 60.035 4801
  346 5894 26.12 1127
  02 Alaska w 9 100 -80 401851 1
  0.400 64.344 6410 858 10193 6.28 386
  04 Arizona w 8 127
  -40 2718215 24 53.300 83.832 7614 257 7041 14.49 1360
  05 Arkansas w 7 120 -29 2286435 44 46.900 51.589 3743 323
  5614 25.09 629
  06 California w 9 134 -45 23667902 151 33.100
  91.295 7592 241 8295 20.30 26387
  08 Colorado w 8 118 -60
  2889964 28 53.400 80.619 7189 273 7998 14.12 1007
  09 Connecticut e 1 105 -32 3107576 638 16.100 78.832 6552 218
  8511 30.95 5132
  10 Delaware e 5 110 -17 594338 308 53.500
  70.636 6644 330 7449 23.62 220
  11 DC e 5 . . 638333 10181 0.000 100.000 10692 . 8960 4.52 .
  12 Florida e 5 109 -2 9746324 180 38.400 84.261 8048 225 7270
  12.61 4650
  13 Georgia e 5 113 -17 5463105 94 37.000 62.402
  5536 296 6402 24.06 2449
  15 Hawaii w 9 100 14 964691
  150 48.300 86.514 6543 221 7740 7.93 639
  16 Idaho w
  8 118 -60 943935 12 28.100 53.998 4525 346 6248 13.93 49
  17 Illinois e 3 117 -35 11426518 205 83.300 83.298 4906
  301 8066 25.81 1535
  18 Indiana e 3 116 -35 5490224 153
  73.700 64.210 4726 416 7142 30.93 2117
  19 Iowa w 4 118
  -47 2913808 52 93.800 58.625 4657 319 7136 20.24 407
  20 Kansas w 4 121 -40 2363679 29 91.200 66.671 5394
  411 7350 19.23 1575
  21 Kentucky e 6 114 -34 3660777 92
  59.300 50.869 3286 320 5978 22.48 420
  22 Louisiana w 7 114
  -16 4205900 95 33.400 68.649 5042 710 6430 14.40 1484
  23 Maine e 1 105 -48 1124660 36 8.100 47.487 4244 261
  5768 27.28 405
  24 Maryland e 5 109 -40 4216975 429 42.900
  80.308 6561 257 8293 14.37 3540
  25 Massachusetts e 1 107 -34
  5737037 733 13.600 83.812 5495 213 7458 25.96 6328
  26 Michigan e 3 112 -51 9262078 163 31.500 70.735 6820 287
  7688 30.29 1782
  27 Minnesota w 4 114 -59 4075970 51 56.500
  66.860 4777 292 7451 20.19 1605
  28 Mississippi e 6 115 -19
  2520638 53 45.800 47.322 3506 312 5183 24.55 1840
  29 Missouri w 4 118 -40 4916686 71 69.900 68.127 5601 293
  6917 21.94 5638
  30 Montana w 8 117 -70 786690 5 66.800
  52.931 3733 421 6589 7.40 117
  31 Nebraska w 4 118 -47
  1569825 21 94.500 62.928 4252 310 6936 13.82 163
  32 Nevada w 8 122 -50 800493 7 14.900 85.316 8414
  327 8453 5.86 159
  33 New Hampshire e 1 106 -46 920610 102
  9.400 52.175 4235 200 6966 31.89 541
  34 New Jersey e 2 110
  -34 7364823 986 21.800 89.036 6183 282 8127 24.94 2640
  35 New Mexico w 8 116 -50 1302894 11 62.200 72.144 5960 316
  6119 7.43 463
  36 New York e 2 108 -52 17558072 371 32.400
  84.622 6691 190 7498 20.93 9635
  37 North Carolina e 5 109 -29
  5881766 120 36.300 47.993 4509 269 6133 32.77 786
  38 North Dakota w 4 121 -60 652717 9 94.800 48.767 2997 439 6417
  5.82 137
  39 Ohio e 3 113 -39 10797630 263 61.300 73.333
  5385 334 7285 30.12 3365
  40 Oklahoma w 7 120 -27 3025290
  44 78.000 67.269 4793 405 6858 16.68 612
  41 Oregon w
  9 119 -54 2633105 27 29.900 67.918 7019 299 7557 19.50 181
  42 Pennsylvania e 2 111 -42 11863895 264 30.400 69.293 3662
  276 7077 28.64 3329
  44 Rhode Island e 1 104 -23 947154 898
  11.100 86.998 5881 202 6897 32.47 381
  45 South Carolina e 5 111
  -20 3121820 103 32.700 54.111 5267 298 5886 32.58 400
  46 South Dakota w 4 120 -58 690768 9 91.600 46.438 3089 271
  5697 9.62 42
  47 Tennessee e 6 113 -32 4591120 112 49.700
  60.412 4307 344 6213 26.69 828
  48 Texas w 7 120 -23
  14229191 54 82.000 79.646 5826 545 7205 17.89 8229
  49 Utah w 8 116 -50 1461037 18 20.000 84.396 5362
  322 6305 15.80 722
  50 Vermont e 1 105 -50 511456 55
  29.600 33.773 3958 215 6178 23.86 180
  51 Virginia e 5 110
  -29 5346818 135 39.100 66.010 4626 280 7478 18.96 7072
  53 Washington w 9 118 -48 4132156 62 39.900 73.497 6579 382
  8073 19.50 3986
  54 West Virginia e 5 112 -37 1949644 81 25.100
  36.177 2580 379 6141 18.36 122
  55 Wisconsin e 3 114 -54
  4705767 87 51.900 64.192 4520 271 7243 28.49 775
  56 Wyoming w 8 114 -63 469557 5 54.200 62.748 5142
  688 7927 5.44 39
  
  
  Variables are:
  State code, State name, East or West of Mississippi river,
  Census Bureau district code, Highest recorded temp, Lowest recorded temp,
  1980 population, Population per square mile, % land devoted to agriculture,
  % urban population, crimes per 100,000, BTU's used per capita,
  per capita income, % labor force in manufacturing,
  per capita military contracts.
  
  b. Write a SAS program to read these data with respective variable names as:
  state_code state_name east_west CB_code High_temp Low_temp Pop80 pop_sqm p_land_ag p_urb_pop crime_rate BTU percapinc labor_force pcmc.
  Note that state_name and east_west are character variables and the rest are numeric. Also note that some states have two part names, e.g. North Carolina.
  
  c. In a new DATA step, Create two more variables:
  urban population = (% urban population*1980 population)/100 and temperature range=highest recorded temp - lowest recorded temp.
  d. Write your SAS program to print all the variables that you have read and created. Use appropriate Labels and Formats to print.
  e. Submit a copy of your program; make sure it RUNs correctly and there are no errors shown in your log file.
  
  2.
  The data given below correspond to a Pharmaceutical Stability Study.
  Six measurements (3rd to 8th entry) on the percentage of potency of the
  drug coming from a certain batch (the first entry) and of a specified age
  (the second entry) are taken.
  1 0 101.2 103.3 103.3 102.1 104.4 102.4
  1 1 98.8 99.4 99.7 99.5 . .
  1 3 98.4 99.0 97.3 99.8 . .
  1 6 101.5 100.2 101.7 102.7 . .
  1 9 96.3 97.2 97.2 96.3 . .
  1 12 97.3 97.9 96.8 97.7 97.7 96.7
  2 0 102.6 102.7 102.4 102.1 102.9 102.6
  2 1 99.1 99.0 99.9 100.6 . .
  2 3 105.7 103.3 103.4 104.0 . .
  2 6 101.3 101.5 100.9 101.4 . .
  2 9 94.1 96.5 97.2 95.6 . .
  2 12 93.1 92.8 95.4 92.5 92.2 93.0
  3 0 105.1 103.9 106.1 104.1 103.7 104.6
  3 1 102.2 102.0 100.8 99.8 . .
  3 3 101.2 101.8 100.8 102.6 . .
  3 6 101.1 102.0 100.1 100.2 . .
  3 9 100.9 99.5 102.5 100.8 . .
  3 12 97.8 98.3 96.9 98.4 96.9 96.5
  a. Write a SAS program to read these data in to eight variables: the batch number, the age, and six variables (say, Percent1 through Percent6), corresponding to six measurements on the percentage of potency. Thus your data set will have 8 (=2+6) variables and 18 observations.
  b. Use NMISS function to check the number of missing values for each observation.
  c. Next, write a SAS program to read these data differently as follows.
  The data now should be read into only three variables: the batch number, the age, and percent of potency of the drug. Note that for every value of batch and age listed on a row there are six values to be read for percent potency of the drug. [Hint: Use a program similar to that was used to read the ball bearing data set using an array and do loop.]
  d. Write a program to create a SAS data set containing data corresponding to the percent potency less than or equal to 97.
  e. Write a program to create a SAS data set of only the non-missing observations.
  
  3.
  The following data set is a score sheet in an elementary statistics class.
  The variables are: Student ID, score on test1 (out of 50), score on test2 (out of 50), score on final (out of 100), score on HW (out of 20), and score on a project (out of 10). The percentages assigned to each one of these respectively are 17.5%, 17.5%, 35%, 20%, and 10%.
  
  a. Write a program to read and write these data.
  
  b. Compute the final percentage score for each student. [Hint: Use SUM function with weights .35, .35, .35, 1, 1].
  
  c. Write your own Format to compute the letter grade with the following breakdown. A score of less than 60 is assigned F, between 60 and less than 67 a D, 67 to less than 69 a D+, 69 to less than 70 is C-, 70 to less than 77 is C, 77 to less than 79 is C+, 79 to less than 80 a B- and so forth. Do not assign any A+, but any score greater than or equal to 90 is assigned A.
  
  d. Use your format to write the grade for each student, but keep the original percent scores as well.
  
  Data set [multiple observations appear on one line]:
  
  0001 50 47.5 89 19 9 0002 45 41 52 8 8 0003 46 41 64 16 8.5 0004 45 27 58 19 8
  0005 43 34 69 20 9 0006 49 29 80 17 5 0007 46.5 38 85 20 10 0008 48 37 78 20 8.5
  0009 49 46.5 60 20 10 00010 47 51 91 20 9 00011 50 43 75 17 9.2
  00012 50 44 79 20 8.5 00013 42.5 34 15 14 0 00014 45.5 32 41 10 9.5
  00015 34.5 28 48 11 5 00016 47 27 87 6 7 00017 46.5 38.5 72 20 10
  00018 33 15 31 20 8.5 00019 41 28 41 16 9 00020 47.5 33 78 17 10
  00021 46 44 65 18 8.5 00022 49 47 91 14 8.5 00023 43.5 39 42 12 9
  00024 49 50 87 19 9.5 00025 44 30 79 11 8.5 00026 36 26 48 16 9
  00027 46 45 81 18 9 00028 35 23 41 4 0 00029 48.5 44.5 92 20 9.5
  00030 50 49.5 91 19 10 00031 39 28 65 18 8.5 00032 48 46 62 14 10
  00033 41.5 41 85 18 9 00034 47 48 91 20 9 00035 41.5 35 63 20 9
  00036 43 46 65 14 8.5 00037 50 50 90 20 10 00038 37 23 14 8 0
  00039 44 39 6 3 9 00040 50 38 78 19 8.5 00041 44 26 61 20 8
  00042 44 38 37 8 8 00043 42 45.5 86 20 8.9 00044 39.5 21 36 4 9.5
  00045 46.5 42 83 18 7.5 00046 49 44 80 20 9 00047 48 50.5 84 20 9
  00048 42 44 91 20 10 00049 49 40 82 16 8 00050 43 23 82 20 8.5
  00051 49 44.5 86 20 10 00052 44 40 85 17 8.5
  
  
  
  
• 项目附件： 承接人可见
• 项目预算： ￥ 100-500 元（ 未托管 ）
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