Stata Computer Module


Stata is an easy to use statistical software package. Stata is command driven and is in some respects similar to SAS, although much easier to learn. This Computer Module is designed to introduce students to Stata and to allow them to begin to statistically analyze data.

Students have several choices when it comes to using Stata, they can use the interactive desktop version of Stata in one of the campus computer labs or they can purchase their own copy of Stata for around $100.

View Useful Stata Commands.

View ATS Stata Class Notes.

Part 1: My first Stata program

  • This program reads in a small dataset and sorts it by ID number. The program can be typed directly into the command window one line at a time or it can be entered into the do-file editor.

    input id x 
    13 34 
    17 21 
    14 25 
     9 33 
    18 40 
    12 33 
     4 44 
    11 41 
    17 21 
    end
    
    sort id
    
    list
    
         +---------+
         | id    x |
         |---------|
      1. | 13   34 |
      2. | 17   21 |
      3. | 14   25 |
      4. |  9   33 |
      5. | 18   40 |
         |---------|
      6. | 12   33 |
      7. |  4   44 |
      8. | 11   41 |
      9. | 17   21 |
         +---------+

    Part 2: Descriptive statistics and exploratory data analysis

    use http://www.philender.com/courses/data/hsb2, clear
    
    describe
    
    Contains data from http://www.gseis.ucla.edu/courses/data/hsb2.dta
      obs:           200                          highschool and beyond (200
                                                    cases)
     vars:            11                          21 Jun 2000 08:54
     size:         9,600 (98.9% of memory free)
    -------------------------------------------------------------------------------
                  storage  display     value
    variable name   type   format      label      variable label
    -------------------------------------------------------------------------------
    id              float  %9.0g                  
    female          float  %9.0g       fl         
    race            float  %12.0g      rl         
    ses             float  %9.0g       sl         
    schtyp          float  %9.0g       scl        type of school
    prog            float  %9.0g       sel        type of program
    read            float  %9.0g                  reading score
    write           float  %9.0g                  writing score
    math            float  %9.0g                  math score
    science         float  %9.0g                  science score
    socst           float  %9.0g                  social studies score
    -------------------------------------------------------------------------------
    
    list
    
    list
    
    Observation 1
    
              id           70      female         male        race        white
             ses          low      schtyp       public        prog      general
            read           57       write           52        math           41
         science           47       socst           57
    
    
    Observation 2
    
              id          121      female       female        race        white
             ses       middle      schtyp       public        prog     vocation
            read           68       write           59        math           53
         science           63       socst           61
    
    
    Observation 3
    
              id           86      female         male        race        white
             ses         high      schtyp       public        prog      general
            read           44       write           33        math           54
         science           58       socst           31
    
    
    Observation 4
    
              id          141      female         male        race        white
             ses         high      schtyp       public        prog     vocation
            read           63       write           44        math           47
         science           53       socst           56
    
    
    Observation 5
    
              id          172      female         male        race        white
             ses       middle      schtyp       public        prog     academic
            read           47       write           52        math           57
         science           53       socst           61
         
     ...
     
    list id female race ses prog read in 1/20
    
         +--------------------------------------------------------+
         |  id   female           race      ses       prog   read |
         |--------------------------------------------------------|
      1. |  70     male          white      low    general     57 |
      2. | 121   female          white   middle   vocation     68 |
      3. |  86     male          white     high    general     44 |
      4. | 141     male          white     high   vocation     63 |
      5. | 172     male          white   middle   academic     47 |
         |--------------------------------------------------------|
      6. | 113     male          white   middle   academic     44 |
      7. |  50     male   african-amer   middle    general     50 |
      8. |  11     male       hispanic   middle   academic     34 |
      9. |  84     male          white   middle    general     63 |
     10. |  48     male   african-amer   middle   academic     57 |
         |--------------------------------------------------------|
     11. |  75     male          white   middle   vocation     60 |
     12. |  60     male          white   middle   academic     57 |
     13. |  95     male          white     high   academic     73 |
     14. | 104     male          white     high   academic     54 |
     15. |  38     male   african-amer      low   academic     45 |
         |--------------------------------------------------------|
     16. | 115     male          white      low    general     42 |
     17. |  76     male          white     high   academic     47 |
     18. | 195     male          white   middle    general     57 |
     19. | 114     male          white     high   academic     68 |
     20. |  85     male          white   middle    general     55 |
         +--------------------------------------------------------+
    
    list id female race ses prog read in 1/20, clean
    
            id   female           race      ses       prog   read  
      1.    70     male          white      low    general     57  
      2.   121   female          white   middle   vocation     68  
      3.    86     male          white     high    general     44  
      4.   141     male          white     high   vocation     63  
      5.   172     male          white   middle   academic     47  
      6.   113     male          white   middle   academic     44  
      7.    50     male   african-amer   middle    general     50  
      8.    11     male       hispanic   middle   academic     34  
      9.    84     male          white   middle    general     63  
     10.    48     male   african-amer   middle   academic     57  
     11.    75     male          white   middle   vocation     60  
     12.    60     male          white   middle   academic     57  
     13.    95     male          white     high   academic     73  
     14.   104     male          white     high   academic     54  
     15.    38     male   african-amer      low   academic     45  
     16.   115     male          white      low    general     42  
     17.    76     male          white     high   academic     47  
     18.   195     male          white   middle    general     57  
     19.   114     male          white     high   academic     68  
     20.    85     male          white   middle    general     55 
     
    list id female race ses prog read in 1/20, clean nolabel
    
            id   female   race   ses   prog   read  
      1.    70        0      4     1      1     57  
      2.   121        1      4     2      3     68  
      3.    86        0      4     3      1     44  
      4.   141        0      4     3      3     63  
      5.   172        0      4     2      2     47  
      6.   113        0      4     2      2     44  
      7.    50        0      3     2      1     50  
      8.    11        0      1     2      2     34  
      9.    84        0      4     2      1     63  
     10.    48        0      3     2      2     57  
     11.    75        0      4     2      3     60  
     12.    60        0      4     2      2     57  
     13.    95        0      4     3      2     73  
     14.   104        0      4     3      2     54  
     15.    38        0      3     1      2     45  
     16.   115        0      4     1      1     42  
     17.    76        0      4     3      2     47  
     18.   195        0      4     2      1     57  
     19.   114        0      4     3      2     68  
     20.    85        0      4     2      1     55 
    
    summarize
    
        Variable |     Obs        Mean   Std. Dev.       Min        Max
    -------------+-----------------------------------------------------
              id |     200       100.5   57.87918          1        200
          female |     200        .545   .4992205          0          1
            race |     200        3.43   1.039472          1          4
             ses |     200       2.055   .7242914          1          3
          schtyp |     200        1.16    .367526          1          2
            prog |     200       2.025   .6904772          1          3
            read |     200       52.23   10.25294         28         76
           write |     200      52.775   9.478586         31         67
            math |     200      52.645   9.368448         33         75
         science |     200       51.85   9.900891         26         74
           socst |     200      52.405   10.73579         26         71
    
    
    summarize write
    
        Variable |     Obs        Mean   Std. Dev.       Min        Max
    -------------+-----------------------------------------------------
           write |     200      52.775   9.478586         31         67
           
    
    histogram write
    
    
    
    histogram write, start(30) width(5) normal
    
    
    
    tabulate write
    
        writing |
          score |      Freq.     Percent        Cum.
    ------------+-----------------------------------
             31 |          4        2.00        2.00
             33 |          4        2.00        4.00
             35 |          2        1.00        5.00
             36 |          2        1.00        6.00
             37 |          3        1.50        7.50
             38 |          1        0.50        8.00
             39 |          5        2.50       10.50
             40 |          3        1.50       12.00
             41 |         10        5.00       17.00
             42 |          2        1.00       18.00
             43 |          1        0.50       18.50
             44 |         12        6.00       24.50
             45 |          1        0.50       25.00
             46 |          9        4.50       29.50
             47 |          2        1.00       30.50
             49 |         11        5.50       36.00
             50 |          2        1.00       37.00
             52 |         15        7.50       44.50
             53 |          1        0.50       45.00
             54 |         17        8.50       53.50
             55 |          3        1.50       55.00
             57 |         12        6.00       61.00
             59 |         25       12.50       73.50
             60 |          4        2.00       75.50
             61 |          4        2.00       77.50
             62 |         18        9.00       86.50
             63 |          4        2.00       88.50
             65 |         16        8.00       96.50
             67 |          7        3.50      100.00
    ------------+-----------------------------------
          Total |        200      100.00
    
    sort prog
    by prog: summarize write
    
    _______________________________________________________________________________
    -> prog = general
    
        Variable |     Obs        Mean   Std. Dev.       Min        Max
    -------------+-----------------------------------------------------
           write |      45    51.33333   9.397775         31         67
    
    _______________________________________________________________________________
    -> prog = academic
    
        Variable |     Obs        Mean   Std. Dev.       Min        Max
    -------------+-----------------------------------------------------
           write |     105    56.25714   7.943343         33         67
    
    _______________________________________________________________________________
    -> prog = vocation
    
        Variable |     Obs        Mean   Std. Dev.       Min        Max
    -------------+-----------------------------------------------------
           write |      50       46.76   9.318754         31         67
    
    summarize write, detail
    
                            writing score
    -------------------------------------------------------------
          Percentiles      Smallest
     1%           31             31
     5%         35.5             31
    10%           39             31       Obs                 200
    25%         45.5             31       Sum of Wgt.         200
    
    50%           54                      Mean             52.775
                            Largest       Std. Dev.      9.478586
    75%           60             67
    90%           65             67       Variance       89.84359
    95%           65             67       Skewness      -.4784158
    99%           67             67       Kurtosis       2.238527
    
    stem write
    
    Stem-and-leaf plot for write (writing score)
    
      3* | 1111
      3t | 3333
      3f | 55
      3s | 66777
      3. | 899999
      4* | 0001111111111
      4t | 223
      4f | 4444444444445
      4s | 66666666677
      4. | 99999999999
      5* | 00
      5t | 2222222222222223
      5f | 44444444444444444555
      5s | 777777777777
      5. | 9999999999999999999999999
      6* | 00001111
      6t | 2222222222222222223333
      6f | 5555555555555555
      6s | 7777777
    
    graph box write
    
    
    
    graph box write, over(prog)
    
    

    Part 3: Selecting Cases

    Example 1
    use http://www.philender.com/courses/data/hsb2, clear
    
    tabulate prog
    
    
        type of |
        program |      Freq.     Percent        Cum.
    ------------+-----------------------------------
        general |         45       22.50       22.50
       academic |        105       52.50       75.00
       vocation |         50       25.00      100.00
    ------------+-----------------------------------
          Total |        200      100.00
    
    summarize write if prog==1
    
    
        Variable |       Obs        Mean    Std. Dev.       Min        Max
    -------------+--------------------------------------------------------
           write |        45    51.33333    9.397775         31         67

    Example 2

    You will have to clear and reload the data after this example.

    keep if prog==1
    (155 observations deleted)
    
    tabulate prog
    
        type of |
        program |      Freq.     Percent        Cum.
    ------------+-----------------------------------
        general |         45      100.00      100.00
    ------------+-----------------------------------
          Total |         45      100.00
    
    summarize write
    
        Variable |       Obs        Mean    Std. Dev.       Min        Max
    -------------+--------------------------------------------------------
           write |        45    51.33333    9.397775         31         67

    Part 4: Scatterplots, Correlation and Regression

    use http://www.philender.com/courses/data/hsb2
    
    scatter write read
    
    
    
    scatter write read, jitter(2)
    
    
    
    scatter write read, jitter(2)  msym(Oh)
    
    
    
    twoway (scatter write read, jitter(2)  msym(Oh))(lfit write read)
    
    
    
    correlate write read math female
    (obs=200)
    
                 |    write     read     math   female
    -------------+------------------------------------
           write |   1.0000
            read |   0.5968   1.0000
            math |   0.6174   0.6623   1.0000
          female |   0.2565  -0.0531  -0.0293   1.0000
    
    
    sort female
    by female: correlate write read math
    
    -------------------------------------------------------------------------------
    -> female = male
    (obs=91)
    
                 |    write     read     math
    -------------+---------------------------
           write |   1.0000
            read |   0.6485   1.0000
            math |   0.6268   0.6085   1.0000
    
    
    --------------------------------------------------------------------------------
    -> female = female
    (obs=109)
    
                 |    write     read     math
    -------------+---------------------------
           write |   1.0000
            read |   0.6209   1.0000
            math |   0.6749   0.7111   1.0000
    
    
    regress write read
    
          Source |       SS       df       MS              Number of obs =     200
    -------------+------------------------------           F(  1,   198) =  109.52
           Model |  6367.42127     1  6367.42127           Prob > F      =  0.0000
        Residual |  11511.4537   198  58.1386552           R-squared     =  0.3561
    -------------+------------------------------           Adj R-squared =  0.3529
           Total |   17878.875   199   89.843593           Root MSE      =  7.6249
    
    ------------------------------------------------------------------------------
           write |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
    -------------+----------------------------------------------------------------
            read |   .5517051   .0527178    10.47   0.000     .4477445    .6556656
           _cons |   23.95944   2.805744     8.54   0.000     18.42647    29.49242
    ------------------------------------------------------------------------------
    
    regress write read female
    
          Source |       SS       df       MS              Number of obs =     200
    -------------+------------------------------           F(  2,   197) =   77.21
           Model |  7856.32118     2  3928.16059           Prob > F      =  0.0000
        Residual |  10022.5538   197  50.8759077           R-squared     =  0.4394
    -------------+------------------------------           Adj R-squared =  0.4337
           Total |   17878.875   199   89.843593           Root MSE      =  7.1327
    
    ------------------------------------------------------------------------------
           write |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
    -------------+----------------------------------------------------------------
            read |   .5658869   .0493849    11.46   0.000      .468496    .6632778
          female |   5.486894   1.014261     5.41   0.000      3.48669    7.487098
           _cons |   20.22837   2.713756     7.45   0.000     14.87663    25.58011
    ------------------------------------------------------------------------------

    Part 5: Independent & Dependent t-tests

    Example 1: Independent t-test
    use http://www.philender.com/courses/data/hsb2, clear
    
    ttest write, by(female)
    
    Two-sample t test with equal variances
    ------------------------------------------------------------------------------
       Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
    ---------+--------------------------------------------------------------------
        male |      91    50.12088    1.080274    10.30516    47.97473    52.26703
      female |     109    54.99083    .7790686    8.133715    53.44658    56.53507
    ---------+--------------------------------------------------------------------
    combined |     200      52.775    .6702372    9.478586    51.45332    54.09668
    ---------+--------------------------------------------------------------------
        diff |           -4.869947    1.304191               -7.441835   -2.298059
    ------------------------------------------------------------------------------
        diff = mean(male) - mean(female)                              t =  -3.7341
    Ho: diff = 0                                     degrees of freedom =      198
    
        Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
     Pr(T < t) = 0.0001         Pr(|T| > |t|) = 0.0002          Pr(T > t) = 0.9999

    Example 2: Dependent t-test

    ttest write = read
    
    Paired t test
    ------------------------------------------------------------------------------
    Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
    ---------+--------------------------------------------------------------------
       write |     200      52.775    .6702372    9.478586    51.45332    54.09668
        read |     200       52.23    .7249921    10.25294    50.80035    53.65965
    ---------+--------------------------------------------------------------------
        diff |     200        .545    .6283822    8.886666   -.6941424    1.784142
    ------------------------------------------------------------------------------
         mean(diff) = mean(write - read)                              t =   0.8673
     Ho: mean(diff) = 0                              degrees of freedom =      199
    
     Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
     Pr(T < t) = 0.8066         Pr(|T| > |t|) = 0.3868          Pr(T > t) = 0.1934

    Part 6: Contingency Tables/Crosstabulation

    tabulate prog female, all
    
       type of |        female
       program |      male     female |     Total
    -----------+----------------------+----------
       general |        21         24 |        45 
      academic |        47         58 |       105 
      vocation |        23         27 |        50 
    -----------+----------------------+----------
         Total |        91        109 |       200 
    
              Pearson chi2(2) =   0.0528   Pr = 0.974
     likelihood-ratio chi2(2) =   0.0528   Pr = 0.974
                   Cramér's V =   0.0162
                        gamma =   0.0066  ASE = 0.122
              Kendall's tau-b =   0.0036  ASE = 0.067
    

    Part 7: Missing Data

    How to Indicate Missing Data

    In Stata, missing values are indicated by periods, ".".

    Example Dataset for Missing Data

    Consider the hypothetical midterm and final exam test scores for 15 students in an elementary statistics course. The maximum is possible score is 50 points on each. There are two missing midterm scores and one missing final exam score. Look a the sample size for each variable and analysis.
    use http://www.philender.com/courses/data/missing, clear
    
    describe
    
    Contains data from missing.dta
      obs:            15                          
     vars:             2                          14 Jul 2006 17:56
     size:           180 (99.9% of memory free)
    -------------------------------------------------------------------------------
                  storage  display     value
    variable name   type   format      label      variable label
    -------------------------------------------------------------------------------
    mt              float  %9.0g                  
    final           float  %9.0g                  
    -------------------------------------------------------------------------------
    Sorted by: 
    
    list, clean
    
           mt   final  
      1.   43      48  
      2.    .      41  
      3.   41      44  
      4.   40      44  
      5.   38      43  
      6.   46      42  
      7.   41      40  
      8.   48       .  
      9.   42      45  
     10.   41      40  
     11.   43      46  
     12.    .      45  
     13.   44      48  
     14.   39      42  
     15.   40      45 
    
    generate total = mt + final
    (3 missing values generated)
    
    list, clean
    
           mt   final   total  
      1.   43      48      91  
      2.    .      41       .  
      3.   41      44      85  
      4.   40      44      84  
      5.   38      43      81  
      6.   46      42      88  
      7.   41      40      81  
      8.   48       .       .  
      9.   42      45      87  
     10.   41      40      81  
     11.   43      46      89  
     12.    .      45       .  
     13.   44      48      92  
     14.   39      42      81  
     15.   40      45      85 
    
    summarize 
    
        Variable |       Obs        Mean    Std. Dev.       Min        Max
    -------------+--------------------------------------------------------
              mt |        13          42    2.798809         38         48
           final |        14    43.78571    2.607049         40         48
           total |        12    85.41667    4.010403         81         92
    
    correlate 
    (obs=12)
    
                 |       mt    final    total
    -------------+---------------------------
              mt |   1.0000
           final |   0.3263   1.0000
           total |   0.7755   0.8498   1.0000
    
    pwcorr, obs
    
                 |       mt    final    total
    -------------+---------------------------
              mt |   1.0000 
                 |       13
                 |
           final |   0.3263   1.0000 
                 |       12       14
                 |
           total |   0.7755   0.8498   1.0000 
                 |       12       12       12

    Part 8: Logging Your Output

    There will be many occassions wherre you will want to save all of the results from a Stata session. The output window only holds the last sewveral hundred lines and a session may include much more than that. To save your results you will need to create a log file of the session.

    log using mylog1.log
    
    [ a bunch of Stata commands ]
    
    log close
    
    type mylog1.log

    Part 9: Cleaning Datasets

    Overview of Data Cleaning

  • The purpose of data cleaning is to find and correct errors in data entry.
  • The primary tools used in data cleaning are summarize and tabulate.
  • Look for values that don't match up with what is given in the data codebook.
  • Also, look for values that are out of range or "impossible," such as, female = 3.

    Causes of Dirty Data

  • Typing Errors: Incorrect values are entered while typing in data.
  • Data Misalignment: During data entry, one or more values are skipped. For example, if a gender data value is skipped and the next variable is age, then the value for age is used by the computer as the value for gender.
  • File Corruption: This occurrs when there is a problem with data stored on a floppy disk or hard disk. This type of problem is much rarer than typing errors or data misalignment.

    Codebook for a Dataset

     Variable Name   Variable Label         Value Labels
     
     CASENUM         Case number            Possible range= 100 to 3000
     
     MATHTYPE        Level of math class    1-N/A
                                            2-Low
                                            3-Average
                                            4-High
                                            5-Algebra
                                            6-Honors Algebra
                                            
     LUNCH2          School lunch           1-Yes
                                            2-No
                                            
     TOTALC          Total accuracy score   Possible range= 0 to 25

    Stata Program

    use http://www.philender.com/courses/data/clean, clear
    
    describe
    
    Contains data from http://www.philender.com/courses/data/clean.dta
      obs:           199                          
     vars:             4                          14 Jul 2006 17:54
     size:         3,980 (99.9% of memory free)
    -------------------------------------------------------------------------------
                  storage  display     value
    variable name   type   format      label      variable label
    -------------------------------------------------------------------------------
    id              float  %9.0g                  
    mathtype        float  %9.0g                  
    lunch2          float  %9.0g                  
    totalc          float  %9.0g                  
    -------------------------------------------------------------------------------
    Sorted by: 
    
    list, clean
    
             id   mathtype   lunch2   totalc  
      1.    884          2        1        7  
      2.    885          2        1       11  
      3.    886          2        1       13  
      4.    887          2        1       14  
      5.    888          2        1        6  
    ...  
    195.    756          6        1       18  
    196.    757          6        1       14  
    197.    758          6        1       17  
    198.    761          6        1       15  
    199.    299          6        2       23 
    
    summarize id totalc, detail
    
                                 id
    -------------------------------------------------------------
          Percentiles      Smallest
     1%          106            102
     5%          148            106
    10%          176            108       Obs                 199
    25%          472            141       Sum of Wgt.         199
    
    50%          755                      Mean           819.4774
                            Largest       Std. Dev.      519.5533
    75%         1068           2126
    90%         1158           2133       Variance       269935.6
    95%         2037           3121       Skewness       1.481881
    99%         3121           3123       Kurtosis        6.65515
    
                               totalc
    -------------------------------------------------------------
          Percentiles      Smallest
     1%            1              0
     5%            3              1
    10%            5              1       Obs                 199
    25%            9              1       Sum of Wgt.         199
    
    50%           14                      Mean            14.1407
                            Largest       Std. Dev.      7.793438
    75%           19             24
    90%           22             25       Variance       60.73768
    95%           24             33       Skewness       2.549864
    99%           33             77       Kurtosis       22.51892
    
    tab1 mathtype lunch2 totalc
    
    -> tabulation of mathtype  
    
       mathtype |      Freq.     Percent        Cum.
    ------------+-----------------------------------
              2 |         44       22.11       22.11
              3 |         44       22.11       44.22
              4 |         44       22.11       66.33
              5 |         43       21.61       87.94
              6 |         21       10.55       98.49
              8 |          1        0.50       98.99
              9 |          2        1.01      100.00
    ------------+-----------------------------------
          Total |        199      100.00
    
    -> tabulation of lunch2  
    
         lunch2 |      Freq.     Percent        Cum.
    ------------+-----------------------------------
              0 |          1        0.50        0.50
              1 |        110       55.28       55.78
              2 |         87       43.72       99.50
              3 |          1        0.50      100.00
    ------------+-----------------------------------
          Total |        199      100.00
    
    -> tabulation of totalc  
    
         totalc |      Freq.     Percent        Cum.
    ------------+-----------------------------------
              0 |          1        0.50        0.50
              1 |          3        1.51        2.01
              2 |          2        1.01        3.02
              3 |          5        2.51        5.53
              4 |          3        1.51        7.04
              5 |          8        4.02       11.06
              6 |         10        5.03       16.08
              7 |          8        4.02       20.10
              8 |          7        3.52       23.62
              9 |          7        3.52       27.14
             10 |         11        5.53       32.66
             11 |         13        6.53       39.20
             12 |          6        3.02       42.21
             13 |          9        4.52       46.73
             14 |          8        4.02       50.75
             15 |         10        5.03       55.78
             16 |         13        6.53       62.31
             17 |         10        5.03       67.34
             18 |         10        5.03       72.36
             19 |         12        6.03       78.39
             20 |          9        4.52       82.91
             21 |          5        2.51       85.43
             22 |         10        5.03       90.45
             23 |          9        4.52       94.97
             24 |          7        3.52       98.49
             25 |          1        0.50       98.99
             33 |          1        0.50       99.50
             77 |          1        0.50      100.00
    ------------+-----------------------------------
          Total |        199      100.00
    
    sort id
    list if id == id[_n+1], clean
    
             id   mathtype   lunch2   totalc  
    155.   1101          4        1       16 
    
    list if id == 1101, clean
    
             id   mathtype   lunch2   totalc  
    155.   1101          4        1       16  
    156.   1101          4        2       12  
    
    list if id>3000 | id<1, clean
    
             id   mathtype   lunch2   totalc  
    198.   3121          5        2       21  
    199.   3123          5        2       22 

    Results

  • ID -- two cases with id number equal 1101 and two cases with id numbers greater than 3000.
  • MATHTYPE -- one case with value 8 and two cases with value 9
  • LUNCH2 -- one case with value 0 and one with value 3
  • TOTALC -- two cases with values greater than 25


    Phil Ender, 25Sep00