Is it 40% or 0.4%?












2












$begingroup$


A variable, which should contain percents, also contains some "ratio" values, for example:



0.61

41

54

.4

.39

20

52

0.7

12

70

82


The real distribution parameters are unknown but I guess it is unimodal with most (say over 70% of) values occurring between 50% and 80%, but it is also possible to see very low values (e.g., 0.1%).



Is there any formal or systematic approaches to determine the likely format in which each value is recorded (i.e., ratio or percent), assuming no other variables are available?






data-cleaning







share|cite|improve this question











$endgroup$








  • 1




    $begingroup$
    I'm voting to close this question as off-topic because it is impossible to definitively answer. If you don't know what the data mean, how will strangers on the internet know?
    $endgroup$
    – Sycorax
    5 hours ago










  • $begingroup$
    Read it again. It is not about asking strangers on the Internet to guess the data mean.
    $endgroup$
    – Orion
    5 hours ago






  • 2




    $begingroup$
    What the data mean != what is the (data) mean.
    $endgroup$
    – Nick Cox
    5 hours ago






  • 1




    $begingroup$
    You have 3 options: your big numbers are falsely big, and need a decimal in front; your small numbers are falsely small and need 100x multiplie; or your data is just fine. Why don't you plot the qqnorm of all three options?
    $endgroup$
    – EngrStudent
    5 hours ago






  • 1




    $begingroup$
    I'm guessing that "ask the people who collected the data" isn't a valid option, here?
    $endgroup$
    – nick012000
    26 mins ago
















2












$begingroup$


A variable, which should contain percents, also contains some "ratio" values, for example:



0.61

41

54

.4

.39

20

52

0.7

12

70

82


The real distribution parameters are unknown but I guess it is unimodal with most (say over 70% of) values occurring between 50% and 80%, but it is also possible to see very low values (e.g., 0.1%).



Is there any formal or systematic approaches to determine the likely format in which each value is recorded (i.e., ratio or percent), assuming no other variables are available?






data-cleaning







share|cite|improve this question











$endgroup$








  • 1




    $begingroup$
    I'm voting to close this question as off-topic because it is impossible to definitively answer. If you don't know what the data mean, how will strangers on the internet know?
    $endgroup$
    – Sycorax
    5 hours ago










  • $begingroup$
    Read it again. It is not about asking strangers on the Internet to guess the data mean.
    $endgroup$
    – Orion
    5 hours ago






  • 2




    $begingroup$
    What the data mean != what is the (data) mean.
    $endgroup$
    – Nick Cox
    5 hours ago






  • 1




    $begingroup$
    You have 3 options: your big numbers are falsely big, and need a decimal in front; your small numbers are falsely small and need 100x multiplie; or your data is just fine. Why don't you plot the qqnorm of all three options?
    $endgroup$
    – EngrStudent
    5 hours ago






  • 1




    $begingroup$
    I'm guessing that "ask the people who collected the data" isn't a valid option, here?
    $endgroup$
    – nick012000
    26 mins ago














2












2








2





$begingroup$


A variable, which should contain percents, also contains some "ratio" values, for example:



0.61

41

54

.4

.39

20

52

0.7

12

70

82


The real distribution parameters are unknown but I guess it is unimodal with most (say over 70% of) values occurring between 50% and 80%, but it is also possible to see very low values (e.g., 0.1%).



Is there any formal or systematic approaches to determine the likely format in which each value is recorded (i.e., ratio or percent), assuming no other variables are available?






data-cleaning







share|cite|improve this question











$endgroup$




A variable, which should contain percents, also contains some "ratio" values, for example:



0.61

41

54

.4

.39

20

52

0.7

12

70

82


The real distribution parameters are unknown but I guess it is unimodal with most (say over 70% of) values occurring between 50% and 80%, but it is also possible to see very low values (e.g., 0.1%).



Is there any formal or systematic approaches to determine the likely format in which each value is recorded (i.e., ratio or percent), assuming no other variables are available?






data-cleaning




data-cleaning






share|cite|improve this question















share|cite|improve this question













share|cite|improve this question




share|cite|improve this question








edited 5 hours ago







Orion

















asked 5 hours ago









OrionOrion

5312




5312








  • 1




    $begingroup$
    I'm voting to close this question as off-topic because it is impossible to definitively answer. If you don't know what the data mean, how will strangers on the internet know?
    $endgroup$
    – Sycorax
    5 hours ago










  • $begingroup$
    Read it again. It is not about asking strangers on the Internet to guess the data mean.
    $endgroup$
    – Orion
    5 hours ago






  • 2




    $begingroup$
    What the data mean != what is the (data) mean.
    $endgroup$
    – Nick Cox
    5 hours ago






  • 1




    $begingroup$
    You have 3 options: your big numbers are falsely big, and need a decimal in front; your small numbers are falsely small and need 100x multiplie; or your data is just fine. Why don't you plot the qqnorm of all three options?
    $endgroup$
    – EngrStudent
    5 hours ago






  • 1




    $begingroup$
    I'm guessing that "ask the people who collected the data" isn't a valid option, here?
    $endgroup$
    – nick012000
    26 mins ago














  • 1




    $begingroup$
    I'm voting to close this question as off-topic because it is impossible to definitively answer. If you don't know what the data mean, how will strangers on the internet know?
    $endgroup$
    – Sycorax
    5 hours ago










  • $begingroup$
    Read it again. It is not about asking strangers on the Internet to guess the data mean.
    $endgroup$
    – Orion
    5 hours ago






  • 2




    $begingroup$
    What the data mean != what is the (data) mean.
    $endgroup$
    – Nick Cox
    5 hours ago






  • 1




    $begingroup$
    You have 3 options: your big numbers are falsely big, and need a decimal in front; your small numbers are falsely small and need 100x multiplie; or your data is just fine. Why don't you plot the qqnorm of all three options?
    $endgroup$
    – EngrStudent
    5 hours ago






  • 1




    $begingroup$
    I'm guessing that "ask the people who collected the data" isn't a valid option, here?
    $endgroup$
    – nick012000
    26 mins ago








1




1




$begingroup$
I'm voting to close this question as off-topic because it is impossible to definitively answer. If you don't know what the data mean, how will strangers on the internet know?
$endgroup$
– Sycorax
5 hours ago




$begingroup$
I'm voting to close this question as off-topic because it is impossible to definitively answer. If you don't know what the data mean, how will strangers on the internet know?
$endgroup$
– Sycorax
5 hours ago












$begingroup$
Read it again. It is not about asking strangers on the Internet to guess the data mean.
$endgroup$
– Orion
5 hours ago




$begingroup$
Read it again. It is not about asking strangers on the Internet to guess the data mean.
$endgroup$
– Orion
5 hours ago




2




2




$begingroup$
What the data mean != what is the (data) mean.
$endgroup$
– Nick Cox
5 hours ago




$begingroup$
What the data mean != what is the (data) mean.
$endgroup$
– Nick Cox
5 hours ago




1




1




$begingroup$
You have 3 options: your big numbers are falsely big, and need a decimal in front; your small numbers are falsely small and need 100x multiplie; or your data is just fine. Why don't you plot the qqnorm of all three options?
$endgroup$
– EngrStudent
5 hours ago




$begingroup$
You have 3 options: your big numbers are falsely big, and need a decimal in front; your small numbers are falsely small and need 100x multiplie; or your data is just fine. Why don't you plot the qqnorm of all three options?
$endgroup$
– EngrStudent
5 hours ago




1




1




$begingroup$
I'm guessing that "ask the people who collected the data" isn't a valid option, here?
$endgroup$
– nick012000
26 mins ago




$begingroup$
I'm guessing that "ask the people who collected the data" isn't a valid option, here?
$endgroup$
– nick012000
26 mins ago










2 Answers
2






active

oldest

votes


















4












$begingroup$

Assuming




  • The only data you have is the percents/ratios (no other related explanatory variables)

  • Your percents comes from a unimodel distribution $P$ and the ratios come from the same unimodal distribution $P$, but squished by $100$ (call it $P_{100}$).

  • The percent/ratios are all between $0$ and $100$.


Then there's a single cutoff point $K$ (with $K < 1.0$ obviously) where everything under $K$ is more likely to be sampled from $P_{100}$ and everything over $K$ is more likely to be sampled from $P$.



You should be able to set up a maximum likelihood function with a binary parameter on each datapoint, plus any parameters of your chosen P.



Afterwards, find $K :=$ where $P$ and $P_{100}$ intersect and you can use that to clean your data.



In practice, just split your data 0-1 and 1-100, fit and plot both histograms and fiddle around with what you think $K$ is.






share|cite|improve this answer









$endgroup$





















    0












    $begingroup$

    Here's one method of determining whether your data are percents or proportions: if there are out-of-bounds values for a proportion (e.g. 52, 70, 82, 41, 54, to name a few) then they must be percents.



    Therefore, your data must be percents. You're welcome.






    share|cite|improve this answer









    $endgroup$









    • 3




      $begingroup$
      The issue is that the two are mixed together. It’s not all percents or all ratios/proportions. 49 is a percentage, but 0.49 could be either.
      $endgroup$
      – The Laconic
      5 hours ago






    • 3




      $begingroup$
      If you can't assume there is a unified format for all of the rows, then the question is obviously unanswerable. In the absence of any other information, it's anyone's guess whether the 0.4 is a proportion of a percentage. I chose to answer the only possible answerable interpretation of the question.
      $endgroup$
      – beta1_equals_beta2
      5 hours ago











    Your Answer





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    2 Answers
    2






    active

    oldest

    votes








    2 Answers
    2






    active

    oldest

    votes









    active

    oldest

    votes






    active

    oldest

    votes









    4












    $begingroup$

    Assuming




    • The only data you have is the percents/ratios (no other related explanatory variables)

    • Your percents comes from a unimodel distribution $P$ and the ratios come from the same unimodal distribution $P$, but squished by $100$ (call it $P_{100}$).

    • The percent/ratios are all between $0$ and $100$.


    Then there's a single cutoff point $K$ (with $K < 1.0$ obviously) where everything under $K$ is more likely to be sampled from $P_{100}$ and everything over $K$ is more likely to be sampled from $P$.



    You should be able to set up a maximum likelihood function with a binary parameter on each datapoint, plus any parameters of your chosen P.



    Afterwards, find $K :=$ where $P$ and $P_{100}$ intersect and you can use that to clean your data.



    In practice, just split your data 0-1 and 1-100, fit and plot both histograms and fiddle around with what you think $K$ is.






    share|cite|improve this answer









    $endgroup$


















      4












      $begingroup$

      Assuming




      • The only data you have is the percents/ratios (no other related explanatory variables)

      • Your percents comes from a unimodel distribution $P$ and the ratios come from the same unimodal distribution $P$, but squished by $100$ (call it $P_{100}$).

      • The percent/ratios are all between $0$ and $100$.


      Then there's a single cutoff point $K$ (with $K < 1.0$ obviously) where everything under $K$ is more likely to be sampled from $P_{100}$ and everything over $K$ is more likely to be sampled from $P$.



      You should be able to set up a maximum likelihood function with a binary parameter on each datapoint, plus any parameters of your chosen P.



      Afterwards, find $K :=$ where $P$ and $P_{100}$ intersect and you can use that to clean your data.



      In practice, just split your data 0-1 and 1-100, fit and plot both histograms and fiddle around with what you think $K$ is.






      share|cite|improve this answer









      $endgroup$
















        4












        4








        4





        $begingroup$

        Assuming




        • The only data you have is the percents/ratios (no other related explanatory variables)

        • Your percents comes from a unimodel distribution $P$ and the ratios come from the same unimodal distribution $P$, but squished by $100$ (call it $P_{100}$).

        • The percent/ratios are all between $0$ and $100$.


        Then there's a single cutoff point $K$ (with $K < 1.0$ obviously) where everything under $K$ is more likely to be sampled from $P_{100}$ and everything over $K$ is more likely to be sampled from $P$.



        You should be able to set up a maximum likelihood function with a binary parameter on each datapoint, plus any parameters of your chosen P.



        Afterwards, find $K :=$ where $P$ and $P_{100}$ intersect and you can use that to clean your data.



        In practice, just split your data 0-1 and 1-100, fit and plot both histograms and fiddle around with what you think $K$ is.






        share|cite|improve this answer









        $endgroup$



        Assuming




        • The only data you have is the percents/ratios (no other related explanatory variables)

        • Your percents comes from a unimodel distribution $P$ and the ratios come from the same unimodal distribution $P$, but squished by $100$ (call it $P_{100}$).

        • The percent/ratios are all between $0$ and $100$.


        Then there's a single cutoff point $K$ (with $K < 1.0$ obviously) where everything under $K$ is more likely to be sampled from $P_{100}$ and everything over $K$ is more likely to be sampled from $P$.



        You should be able to set up a maximum likelihood function with a binary parameter on each datapoint, plus any parameters of your chosen P.



        Afterwards, find $K :=$ where $P$ and $P_{100}$ intersect and you can use that to clean your data.



        In practice, just split your data 0-1 and 1-100, fit and plot both histograms and fiddle around with what you think $K$ is.







        share|cite|improve this answer












        share|cite|improve this answer



        share|cite|improve this answer










        answered 4 hours ago









        djmadjma

        64947




        64947

























            0












            $begingroup$

            Here's one method of determining whether your data are percents or proportions: if there are out-of-bounds values for a proportion (e.g. 52, 70, 82, 41, 54, to name a few) then they must be percents.



            Therefore, your data must be percents. You're welcome.






            share|cite|improve this answer









            $endgroup$









            • 3




              $begingroup$
              The issue is that the two are mixed together. It’s not all percents or all ratios/proportions. 49 is a percentage, but 0.49 could be either.
              $endgroup$
              – The Laconic
              5 hours ago






            • 3




              $begingroup$
              If you can't assume there is a unified format for all of the rows, then the question is obviously unanswerable. In the absence of any other information, it's anyone's guess whether the 0.4 is a proportion of a percentage. I chose to answer the only possible answerable interpretation of the question.
              $endgroup$
              – beta1_equals_beta2
              5 hours ago
















            0












            $begingroup$

            Here's one method of determining whether your data are percents or proportions: if there are out-of-bounds values for a proportion (e.g. 52, 70, 82, 41, 54, to name a few) then they must be percents.



            Therefore, your data must be percents. You're welcome.






            share|cite|improve this answer









            $endgroup$









            • 3




              $begingroup$
              The issue is that the two are mixed together. It’s not all percents or all ratios/proportions. 49 is a percentage, but 0.49 could be either.
              $endgroup$
              – The Laconic
              5 hours ago






            • 3




              $begingroup$
              If you can't assume there is a unified format for all of the rows, then the question is obviously unanswerable. In the absence of any other information, it's anyone's guess whether the 0.4 is a proportion of a percentage. I chose to answer the only possible answerable interpretation of the question.
              $endgroup$
              – beta1_equals_beta2
              5 hours ago














            0












            0








            0





            $begingroup$

            Here's one method of determining whether your data are percents or proportions: if there are out-of-bounds values for a proportion (e.g. 52, 70, 82, 41, 54, to name a few) then they must be percents.



            Therefore, your data must be percents. You're welcome.






            share|cite|improve this answer









            $endgroup$



            Here's one method of determining whether your data are percents or proportions: if there are out-of-bounds values for a proportion (e.g. 52, 70, 82, 41, 54, to name a few) then they must be percents.



            Therefore, your data must be percents. You're welcome.







            share|cite|improve this answer












            share|cite|improve this answer



            share|cite|improve this answer










            answered 5 hours ago









            beta1_equals_beta2beta1_equals_beta2

            412




            412








            • 3




              $begingroup$
              The issue is that the two are mixed together. It’s not all percents or all ratios/proportions. 49 is a percentage, but 0.49 could be either.
              $endgroup$
              – The Laconic
              5 hours ago






            • 3




              $begingroup$
              If you can't assume there is a unified format for all of the rows, then the question is obviously unanswerable. In the absence of any other information, it's anyone's guess whether the 0.4 is a proportion of a percentage. I chose to answer the only possible answerable interpretation of the question.
              $endgroup$
              – beta1_equals_beta2
              5 hours ago














            • 3




              $begingroup$
              The issue is that the two are mixed together. It’s not all percents or all ratios/proportions. 49 is a percentage, but 0.49 could be either.
              $endgroup$
              – The Laconic
              5 hours ago






            • 3




              $begingroup$
              If you can't assume there is a unified format for all of the rows, then the question is obviously unanswerable. In the absence of any other information, it's anyone's guess whether the 0.4 is a proportion of a percentage. I chose to answer the only possible answerable interpretation of the question.
              $endgroup$
              – beta1_equals_beta2
              5 hours ago








            3




            3




            $begingroup$
            The issue is that the two are mixed together. It’s not all percents or all ratios/proportions. 49 is a percentage, but 0.49 could be either.
            $endgroup$
            – The Laconic
            5 hours ago




            $begingroup$
            The issue is that the two are mixed together. It’s not all percents or all ratios/proportions. 49 is a percentage, but 0.49 could be either.
            $endgroup$
            – The Laconic
            5 hours ago




            3




            3




            $begingroup$
            If you can't assume there is a unified format for all of the rows, then the question is obviously unanswerable. In the absence of any other information, it's anyone's guess whether the 0.4 is a proportion of a percentage. I chose to answer the only possible answerable interpretation of the question.
            $endgroup$
            – beta1_equals_beta2
            5 hours ago




            $begingroup$
            If you can't assume there is a unified format for all of the rows, then the question is obviously unanswerable. In the absence of any other information, it's anyone's guess whether the 0.4 is a proportion of a percentage. I chose to answer the only possible answerable interpretation of the question.
            $endgroup$
            – beta1_equals_beta2
            5 hours ago


















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