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Re: st: RE: match variable across two tables


From   Rongrong Zhang <r05zhang@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: RE: match variable across two tables
Date   Sun, 22 Dec 2013 17:30:26 -0500

Dear Robert,
i used "reshape long naics, i(ionumber) j(code) string", it worked. My
read is j(code) takes the value of the dimension of NAICS (i.e. 8).

thanks,

-Rochelle

On Sun, Dec 22, 2013 at 1:20 PM, Rongrong Zhang <r05zhang@gmail.com> wrote:
> Dear Robert,
>
> You are correct. I eliminated 10 observations that have invalid NAICS
> (i.e. ones with letters embedded). I used your tostring to convert
> NAICS1-8.
>
> before I can use "reshape long naics, i(ionumber) j(code) string", I
> think I need to generate variable code, which in your original post ,
> was the maximum count of NAICS, I have
> 1113A0 Fruit farming               11131 11132 111331 111332 111333
> 111334 111336 111339
>
> I did :
> gen code=8
> reshape long naics, i(ionumber) j(code) string
>
> error message"code already defined -- data already long"
>
> but my data looks like
> ionumber ioname naics1 naics2 naics3 naics4 naics5 naics6 naics7 naics8
> 113A0 Fruit farming               11131 11132 111331 111332 111333
> 111334 111336 111339
>
> what did I do wrong?
>
> thanks,
>
> -Rochelle
>
> On Sat, Dec 21, 2013 at 2:54 PM, Robert Picard <picard@netbox.com> wrote:
>> Well "331A" is not a valid NAISC code so you have to decide what to do
>> about that. The sample code I provided earlier requires that
>> naics1-naics8 be string. This can easily be done using
>>
>> tostring naics*, replace
>>
>> Robert
>>
>> On Sat, Dec 21, 2013 at 1:56 PM, Rongrong Zhang <r05zhang@gmail.com> wrote:
>>> Thank you Sarah.
>>>
>>> NAICS1 does not contain all the naics code from the original data.
>>>
>>> I found out why stata import naics1 as str, because there are a few
>>> observations have letters embeded in NAICS1, e.g. 331A as a value of
>>> NAICS1, NAICS2-8 are only numeric .
>>>
>>> I am not proficient in writing a import program, that is why I use
>>> import wizard to import the txt file.
>>>
>>>
>>>
>>> On Sat, Dec 21, 2013 at 12:32 PM, Sarah Edgington <sedging@ucla.edu> wrote:
>>>> Rochelle,
>>>> At this point to determine what to do next you're actually going to have to
>>>> look carefully at your data, all of it, not just the first observation.
>>>> Then you'll have to make some decisions about how to get from the data you
>>>> have to the data you want.
>>>>
>>>> Are naics2-naics8 missing for ALL observations.  Stata doesn't make
>>>> decisions about what format to import variables based on only the first
>>>> observation so looking at the first observation is not going to be enough
>>>> information to tell you what happened.
>>>>
>>>> Then you'll want to look at naics1.  Does it contain all the naics codes
>>>> from your original table?  If naics1 contains all your values, separated by
>>>> spaces, and the rest of the naics variables are ALWAYS missing then you can,
>>>> as I suggested previously, just get rid of the extraneous naics variables
>>>> and use -split- as Robert suggested previously.
>>>>
>>>> If naic2-naics8 contain data for some of your observations then you'll have
>>>> to think harder about your next steps.
>>>>
>>>> For -reshape- to work you need a series of numbered variables that all have
>>>> the same storage format.
>>>> You should have all the tools you need to get to that point.  You just have
>>>> to look carefully at your data and figure out what steps you need to take.
>>>>
>>>> -Sarah
>>>>
>>>>
>>>> At 05:33 AM 12/21/2013, you wrote:
>>>>>
>>>>> Hi Sarah,
>>>>>
>>>>> after importing, naics1 was set to str, naics2-8 were set to long, as
>>>>> I said previously, I used File-Import-ASCII data created by
>>>>> spreadsheet, then stata imported my txt file for me, my first
>>>>> observation has non-missing data for naics1 and all missing for
>>>>> naics2-8, I guess that is why stata assigned different types to
>>>>> them.and the log shows command insheet was used.
>>>>>  insheet using "C:\Users\Questions\Stata list\I-O table__Cleaned.txt"
>>>>>
>>>>> Best,
>>>>> Rochelle
>>>>>
>>>>> On Fri, Dec 20, 2013 at 6:05 PM, Sarah Edgington <sedging@ucla.edu> wrote:
>>>>> > Rochelle,
>>>>> > The error message isn't because the naics variables are missing, it's
>>>>> > because naics2 (and presumably all of naics2-naics8?) are a different
>>>>> > variable type than naics1.  However, reshaping when all but 1 of the
>>>>> > variables being reshaped contain all missing values isn't going to get you
>>>>> > what you want.
>>>>> >
>>>>> > It sounds like something is going awry with your import process.  If I
>>>>> > understand you correctly you're saying that naics2-naics8 are missing for
>>>>> > all observations not just the first two that you show, right?
>>>>> > Are the codes all being read into the naics1 variable?  That is, is
>>>>> > naics1 a string variable containing multiple codes separated by spaces?  If
>>>>> > that's the case you'll want to drop naics2-naics8 and separate naics1 into
>>>>> > multiple variables before reshaping.
>>>>> > -Sarah
>>>>> >
>>>>> > -----Original Message-----
>>>>> > From: owner-statalist@hsphsun2.harvard.edu
>>>>> > [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Rongrong Zhang
>>>>> > Sent: Friday, December 20, 2013 2:06 PM
>>>>> > To: statalist@hsphsun2.harvard.edu
>>>>> > Subject: Re: st: RE: match variable across two tables
>>>>> >
>>>>> > THANKS! I use import wizard and get the data into stata .
>>>>> >
>>>>> > data looks like:
>>>>> > ionumber ioname naics1 naics2 naics3 naics4 naics5 naics6 naics7 naics8
>>>>> > 1110  Crop production 111 . . . . . . .
>>>>> > 1111A0 Oilseed farming    11111 . . . . . . .
>>>>> >
>>>>> >
>>>>> > that is missing for naics2 ~8.
>>>>> >
>>>>> >  insheet using "C:\Users\Questions\Stata list\I-O table__Cleaned.txt"
>>>>> > (10 vars, 564 obs)
>>>>> >
>>>>> > I got an error here:
>>>>> >
>>>>> > reshape long naics, i(ionumber) j(code) string
>>>>> > (note: j = 1 2 3 4 5 6 7 8)
>>>>> > naics2 type mismatch with other naics variables
>>>>> >
>>>>> > I did not have this error when I use your entire program, that is , when
>>>>> > I use your input, then split codelist,
>>>>> >
>>>>> > I wonder if my error is caused by missing values in naics2
>>>>> >
>>>>> > On Fri, Dec 20, 2013 at 4:40 PM, Robert Picard <picard@netbox.com>
>>>>> > wrote:
>>>>> >> I added double quotes so that your few lines of data could be read
>>>>> >> inline using -input- (since Statalist does not allow attachments). You
>>>>> >> most certainly do not need to input your data into Stata using the
>>>>> >> same command. See -help import- to find better ways to do it.
>>>>> >>
>>>>> >> Robert
>>>>> >>
>>>>> >> On Fri, Dec 20, 2013 at 4:31 PM, Rongrong Zhang <r05zhang@gmail.com>
>>>>> >> wrote:
>>>>> >>> Dear Roberts,
>>>>> >>>
>>>>> >>> Please excuse my late response.  Thanks so very much for your code !!!
>>>>> >>> Words can't express my gratitude.
>>>>> >>>
>>>>> >>> my original data has over 600 rows (the I-O table), I posted only a
>>>>> >>> few lines to save space. My question - to add quotes like in your
>>>>> >>> program
>>>>> >>>
>>>>> >>> "1110" "Crop production"
>>>>> >>>
>>>>> >>> is there a stata tool that does it automatically or do I need to
>>>>> >>> insert it manually for all 600 rows?
>>>>> >>>
>>>>> >>> Merry Christmas!
>>>>> >>>
>>>>> >>> Rochelle
>>>>> >>>
>>>>> >>> On Thu, Dec 19, 2013 at 12:22 PM, Robert Picard <picard@netbox.com>
>>>>> >>> wrote:
>>>>> >>>> No need to talk about "fuzzy" matching as NAISC codes are defined
>>>>> >>>> hierarchically. If you do not match at the 6-digit level, you can
>>>>> >>>> try again using 5-digit codes, and so on.
>>>>> >>>>
>>>>> >>>> Your first problem is to reshape Table 1 data from wide to long
>>>>> >>>> format. Your "I-O number codes" are clearly not valid NAISC codes so
>>>>> >>>> the target becomes creating a crosswalk between valid NAICS to "I-O
>>>>> >>>> number codes".
>>>>> >>>>
>>>>> >>>> Once you have the crosswalk, you can do an exact match using -merge-.
>>>>> >>>> For all NAICS code that did not find an exact match, you can do an
>>>>> >>>> update merge to find matching "I-O numbers" using 5-digit NAISC
>>>>> >>>> codes.
>>>>> >>>> You can then repeat down to 2-digit NAICS if you want to.
>>>>> >>>>
>>>>> >>>> Robert
>>>>> >>>>
>>>>> >>>> * ----------------- begin example ------------------------ clear
>>>>> >>>> input str6 ionumber str244 ioname str244 codelist "1110" "Crop
>>>>> >>>> production"
>>>>> >>>> "1111A0" "Oilseed farming" "11111 11112"
>>>>> >>>> "1111B0" "Grain farming" "11113 11114 11115 11116 11119"
>>>>> >>>> "111200" "Vegetable and melon farming" "1112"
>>>>> >>>> "111400" "Greenhouse and nursery production" "1114"
>>>>> >>>> "111910" "Tobacco farming" "11191"
>>>>> >>>> "111920" "Cotton farming" "11192"
>>>>> >>>> "1119A0" "Sugarcane and sugar beet" "11193 111991"
>>>>> >>>> "1119B0" "All other crop farming" "11194 111992 111998"
>>>>> >>>> end
>>>>> >>>> compress
>>>>> >>>>
>>>>> >>>> * split into separate codes and reshape long split codelist,
>>>>> >>>> gen(naics) reshape long naics, i(ionumber) j(code) string
>>>>> >>>>
>>>>> >>>> * drop obs with missing codes
>>>>> >>>> bysort ionumber (code): drop if mi(naics) & _n > 1 replace naics =
>>>>> >>>> ionumber if mi(naics)
>>>>> >>>>
>>>>> >>>> * remove trailing zeros
>>>>> >>>> replace naics = regexr(naics,"0+$","")
>>>>> >>>>
>>>>> >>>> * save naics to ionumber crosswalk
>>>>> >>>> isid naics, sort
>>>>> >>>> list, noobs sepby(ionumber)
>>>>> >>>> tempfile table1
>>>>> >>>> save "`table1'"
>>>>> >>>>
>>>>> >>>> clear
>>>>> >>>> input str6 naics
>>>>> >>>> "111"
>>>>> >>>> "1111"
>>>>> >>>> "111150"
>>>>> >>>> "111199"
>>>>> >>>> "111219"
>>>>> >>>> "111310"
>>>>> >>>> "111320"
>>>>> >>>> "111332"
>>>>> >>>> "111334"
>>>>> >>>> "111335"
>>>>> >>>> "111339"
>>>>> >>>> "1114"
>>>>> >>>> "111411"
>>>>> >>>> "111419"
>>>>> >>>> "111421"
>>>>> >>>> "111422"
>>>>> >>>> "111920"
>>>>> >>>> "111930"
>>>>> >>>> "111940"
>>>>> >>>> "111998"
>>>>> >>>> end
>>>>> >>>> gen table2id = _n
>>>>> >>>> replace naics = regexr(naics,"0+$","")
>>>>> >>>>
>>>>> >>>> * do an exact match using the crosswalk merge 1:1 naics using
>>>>> >>>> "`table1'", keepusing(ionumber) /// keep(master match) nogen
>>>>> >>>>
>>>>> >>>> * for obs that did not match, try again using 5 digits.
>>>>> >>>> clonevar naics6 = naics
>>>>> >>>> replace naics = substr(naics6,1,5)
>>>>> >>>> merge m:1 naics using "`table1'", keepusing(ionumber) /// update
>>>>> >>>> gen(merge5) drop if merge5 == 2
>>>>> >>>>
>>>>> >>>> * repeat for 4-digit naics
>>>>> >>>> replace naics = substr(naics6,1,4)
>>>>> >>>> merge m:1 naics using "`table1'", keepusing(ionumber) /// update
>>>>> >>>> gen(merge4) drop if merge4 == 2
>>>>> >>>>
>>>>> >>>> * repeat for 3-digit naics
>>>>> >>>> replace naics = substr(naics6,1,3)
>>>>> >>>> merge m:1 naics using "`table1'", keepusing(ionumber) /// update
>>>>> >>>> gen(merge3) drop if merge3 == 2
>>>>> >>>> * --------------------------- end example ---------------
>>>>> >>>>
>>>>> >>>>
>>>>> >>>> On Wed, Dec 18, 2013 at 9:52 PM, Rongrong Zhang <r05zhang@gmail.com>
>>>>> >>>> wrote:
>>>>> >>>>> Hi Sarah,
>>>>> >>>>>
>>>>> >>>>> Thanks so much for your questions.  Let me try to answer them in
>>>>> >>>>> the order they were posted.
>>>>> >>>>>
>>>>> >>>>> Yes, I plan to drop trailing zeros and take all the nonzero digits
>>>>> >>>>> as match criteria. In this case, you are correct in terms of - I
>>>>> >>>>> need processing the data first. - should I use trim ()?
>>>>> >>>>>
>>>>> >>>>> your next question: the structure of data in table 1: do I have a
>>>>> >>>>> single variable that has multiple codes in it. I assume you are
>>>>> >>>>> asking:
>>>>> >>>>>
>>>>> >>>>> e.g 1111B0    Grain farming    corresponds to 5 different NAICS code
>>>>> >>>>>  and they are    11113      11114      11115 11116      11119.
>>>>> >>>>>
>>>>> >>>>> suppose all these 5 NAICS codes are present in my Table 2, I would
>>>>> >>>>> like to have 5 rows in my final output table like this:
>>>>> >>>>>
>>>>> >>>>> 1111B0   11113
>>>>> >>>>> 1111B0   11114
>>>>> >>>>> 1111B0   11115
>>>>> >>>>> 1111B0   11116
>>>>> >>>>> 1111B0   11119
>>>>> >>>>>
>>>>> >>>>> next question : the rule that make an entry a match. If I require 5
>>>>> >>>>> or
>>>>> >>>>> 6 digit match, then these two tables may not produce many matches.
>>>>> >>>>> that is why I thought of 4 digit matches. Ideally I would like to
>>>>> >>>>> do both exact and "fuzzy" match e.g. using 4 digit, so I have the
>>>>> >>>>> flexibility to control my sample size.
>>>>> >>>>>
>>>>> >>>>> If you or others have questions or suggestions, please let me know.
>>>>> >>>>>
>>>>> >>>>> thanks,
>>>>> >>>>>
>>>>> >>>>> On Wed, Dec 18, 2013 at 3:05 PM, Sarah Edgington <sedging@ucla.edu>
>>>>> >>>>> wrote:
>>>>> >>>>>> Rochelle,
>>>>> >>>>>> This looks like it may be a pretty complicated problem.  I don't
>>>>> >>>>>> immediately have any suggestions because I'm not sure I understand either
>>>>> >>>>>> the exact structure of your data or the matching rules you want to follow.
>>>>> >>>>>>
>>>>> >>>>>> You say that if you use exact matching that you want I-O number
>>>>> >>>>>> 1111B0 to match with NAICS code 111150.  I take it that is an "exact match"
>>>>> >>>>>> because you want to drop the trailing zero in the NAICS code.  So, since
>>>>> >>>>>> 11115 appears in the list of NAICS codes for 1111B0, it would match to
>>>>> >>>>>> 111150 in table 2.  This is not to my mind an "exact match" because it
>>>>> >>>>>> requires first modifying the NAICS code in table 2 before you can match.  To
>>>>> >>>>>> do that successfully you need to be very clear about what the rule for
>>>>> >>>>>> modification is.
>>>>> >>>>>> Is the rule that if the NAICS code in table 2 has a zero at the end
>>>>> >>>>>> you always drop it?  Does it matter how many digits appear before the zero?
>>>>> >>>>>>
>>>>> >>>>>> The next question I have is about the structure of table 1 as it
>>>>> >>>>>> appears in Stata.  Do you have a single variable that has multiple codes in
>>>>> >>>>>> it?  If so, you're probably going to have to do some additional processing
>>>>> >>>>>> to that variable before trying to match the two tables.
>>>>> >>>>>>
>>>>> >>>>>> The final thing I was unclear on was what you want the final
>>>>> >>>>>> structure of your data to be after matching.  How do you want to deal with
>>>>> >>>>>> entries in table 1 that have multiple matches in table 2?  Do you want the
>>>>> >>>>>> resulting data to contain multiple observations, one for each of the NAICS
>>>>> >>>>>> codes that the I-O number matches to?
>>>>> >>>>>>
>>>>> >>>>>> Again for the four digit match, you'll want to be very clear on the
>>>>> >>>>>> rules that make an entry a match.  I'm not sure if you're asking for a match
>>>>> >>>>>> of the first four digits of the NAICS code in table 1 to only the codes in
>>>>> >>>>>> table 2 that are four digits long.  Alternatively perhaps you're looking to
>>>>> >>>>>> match observation in table 1 to ALL the entries in table 2 that share the
>>>>> >>>>>> same first four digits.
>>>>> >>>>>>
>>>>> >>>>>> If you can more precisely describe the structure of your data as it
>>>>> >>>>>> currently exists, the matching rules you want to follow, and the structure
>>>>> >>>>>> you want your final data to be in, you'll increase your chances of getting a
>>>>> >>>>>> helpful answer from the list.
>>>>> >>>>>>
>>>>> >>>>>> -S
>>>>> >>>>>>
>>>>> >>>>>> -----Original Message-----
>>>>> >>>>>> From: owner-statalist@hsphsun2.harvard.edu
>>>>> >>>>>> [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of
>>>>> >>>>>> Rongrong Zhang
>>>>> >>>>>> Sent: Wednesday, December 18, 2013 11:15 AM
>>>>> >>>>>> To: statalist@hsphsun2.harvard.edu
>>>>> >>>>>> Subject: st: match variable across two tables
>>>>> >>>>>>
>>>>> >>>>>> Dear STATALISTER,
>>>>> >>>>>>
>>>>> >>>>>> I have two tables:
>>>>> >>>>>>
>>>>> >>>>>> Table 1 has 3 variables  I-O number, I-O Name ,   Related 1997
>>>>> >>>>>> NAICS codes.
>>>>> >>>>>>
>>>>> >>>>>> Table 2 has 1 variable 1997 NAICS codes.
>>>>> >>>>>>
>>>>> >>>>>> I want to link these two tables based on NAICS code. However, the
>>>>> >>>>>> level of details on NAICS code does not match one-to-one because
>>>>> >>>>>> the tables come from different data source. My goal is to know
>>>>> >>>>>> which NAICS code correspond to which I-O number. I can’t use Table
>>>>> >>>>>> 1 only, because TABLE 2 is produced from Wharton Research Database
>>>>> >>>>>> which has company level financial data ­ I will use later on.
>>>>> >>>>>>
>>>>> >>>>>> By different details I mean : e.g.
>>>>> >>>>>>
>>>>> >>>>>> table 1:
>>>>> >>>>>>
>>>>> >>>>>> I-O number  I-O Name                  1997 NAICS codes
>>>>> >>>>>>
>>>>> >>>>>> 1110        Crop production
>>>>> >>>>>>
>>>>> >>>>>> 1111A0    Oilseed farming
>>>>> >>>>>>          11111      11112
>>>>> >>>>>>
>>>>> >>>>>> 1111B0    Grain farming             11113      11114      11115
>>>>> >>>>>> 11116      11119
>>>>> >>>>>>
>>>>> >>>>>> 111200    Vegetable and melon farming
>>>>> >>>>>>                    1112
>>>>> >>>>>> 111400 Greenhouse and nursery production
>>>>> >>>>>>                 1114
>>>>> >>>>>> 111910 Tobacco farming
>>>>> >>>>>>                 11191
>>>>> >>>>>> 111920 Cotton farming
>>>>> >>>>>>                 11192
>>>>> >>>>>> 1119A0 Sugarcane and sugar beet
>>>>> >>>>>>       11193 111991
>>>>> >>>>>> 1119B0 All other crop farming
>>>>> >>>>>> 11194 111992 111998
>>>>> >>>>>>
>>>>> >>>>>> in the above example, I present industry 1110 and its
>>>>> >>>>>> subindustries 1111A0, 1111B0, 111200, each of the subindustries
>>>>> >>>>>> correspond to a few (or a single) NAICS code (north america
>>>>> >>>>>> industry classification system).
>>>>> >>>>>>
>>>>> >>>>>> table 2:
>>>>> >>>>>> NAICS CODE
>>>>> >>>>>> 111
>>>>> >>>>>> 1111
>>>>> >>>>>> 111150
>>>>> >>>>>> 111199
>>>>> >>>>>> 111219
>>>>> >>>>>> 111310
>>>>> >>>>>> 111320
>>>>> >>>>>> 111332
>>>>> >>>>>> 111334
>>>>> >>>>>> 111335
>>>>> >>>>>> 111339
>>>>> >>>>>> 1114
>>>>> >>>>>> 111411
>>>>> >>>>>> 111419
>>>>> >>>>>> 111421
>>>>> >>>>>> 111422
>>>>> >>>>>> 111920
>>>>> >>>>>> 111930
>>>>> >>>>>> 111940
>>>>> >>>>>> 111998
>>>>> >>>>>>
>>>>> >>>>>> if I enforce exact match, then table 2  111150 matches with table 1
>>>>> >>>>>> 1111B0,    table 2 1112l9 may be matched with  111200 table 1 I-O.
>>>>> >>>>>>
>>>>> >>>>>> My question :
>>>>> >>>>>> 1.could you give a sample code/function to do exact match? note,
>>>>> >>>>>> if first 5digit match, and drop last 0 (naics), we consider that a
>>>>> >>>>>> match 2. if I want to increase match, how could I change the
>>>>> >>>>>> program to do 4 digit match
>>>>> >>>>>>
>>>>> >>>>>> thanks a bunch,
>>>>> >>>>>>
>>>>> >>>>>> --
>>>>> >>>>>> Best,
>>>>> >>>>>> Rochelle
>>>>> >>>>>>
>>>>> >>>>>> *
>>>>> >>>>>> *   For searches and help try:
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>>>>> >>>>>>
>>>>> >>>>>>
>>>>> >>>>>> *
>>>>> >>>>>> *   For searches and help try:
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>>>>> >>>>>
>>>>> >>>>>
>>>>> >>>>>
>>>>> >>>>> --
>>>>> >>>>> -Best,
>>>>> >>>>> R
>>>>> >>>>>
>>>>> >>>>> *
>>>>> >>>>> *   For searches and help try:
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>>>>> >>>>
>>>>> >>>> *
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>>>>> >>>
>>>>> >>>
>>>>> >>>
>>>>> >>> --
>>>>> >>> -Best,
>>>>> >>> R
>>>>> >>>
>>>>> >>> *
>>>>> >>> *   For searches and help try:
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>>>>> >>
>>>>> >> *
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>>>>> >
>>>>> >
>>>>> >
>>>>> > --
>>>>> > -Best,
>>>>> > R
>>>>> >
>>>>> > *
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>>>>> >
>>>>> >
>>>>> > *
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>>>>>
>>>>>
>>>>>
>>>>> --
>>>>> -Best,
>>>>> R
>>>>>
>>>>> *
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>>>>
>>>>
>>>>
>>>> *
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>>>> *   http://www.ats.ucla.edu/stat/stata/
>>>
>>> *
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>>> *   http://www.ats.ucla.edu/stat/stata/
>>
>> *
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