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





    StackExchange.ifUsing("editor", function () {
    return StackExchange.using("mathjaxEditing", function () {
    StackExchange.MarkdownEditor.creationCallbacks.add(function (editor, postfix) {
    StackExchange.mathjaxEditing.prepareWmdForMathJax(editor, postfix, [["$", "$"], ["\\(","\\)"]]);
    });
    });
    }, "mathjax-editing");

    StackExchange.ready(function() {
    var channelOptions = {
    tags: "".split(" "),
    id: "65"
    };
    initTagRenderer("".split(" "), "".split(" "), channelOptions);

    StackExchange.using("externalEditor", function() {
    // Have to fire editor after snippets, if snippets enabled
    if (StackExchange.settings.snippets.snippetsEnabled) {
    StackExchange.using("snippets", function() {
    createEditor();
    });
    }
    else {
    createEditor();
    }
    });

    function createEditor() {
    StackExchange.prepareEditor({
    heartbeatType: 'answer',
    autoActivateHeartbeat: false,
    convertImagesToLinks: false,
    noModals: true,
    showLowRepImageUploadWarning: true,
    reputationToPostImages: null,
    bindNavPrevention: true,
    postfix: "",
    imageUploader: {
    brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
    contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
    allowUrls: true
    },
    onDemand: true,
    discardSelector: ".discard-answer"
    ,immediatelyShowMarkdownHelp:true
    });


    }
    });














    draft saved

    draft discarded


















    StackExchange.ready(
    function () {
    StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstats.stackexchange.com%2fquestions%2f395626%2fis-it-40-or-0-4%23new-answer', 'question_page');
    }
    );

    Post as a guest















    Required, but never shown

























    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


















            draft saved

            draft discarded




















































            Thanks for contributing an answer to Cross Validated!


            • Please be sure to answer the question. Provide details and share your research!

            But avoid …



            • Asking for help, clarification, or responding to other answers.

            • Making statements based on opinion; back them up with references or personal experience.


            Use MathJax to format equations. MathJax reference.


            To learn more, see our tips on writing great answers.




            draft saved


            draft discarded














            StackExchange.ready(
            function () {
            StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstats.stackexchange.com%2fquestions%2f395626%2fis-it-40-or-0-4%23new-answer', 'question_page');
            }
            );

            Post as a guest















            Required, but never shown





















































            Required, but never shown














            Required, but never shown












            Required, but never shown







            Required, but never shown

































            Required, but never shown














            Required, but never shown












            Required, but never shown







            Required, but never shown







            -

            Popular posts from this blog

            A CLEAN and SIMPLE way to add appendices to Table of Contents and bookmarks

            Image
            0 I found a lot of questions abount appendices and ToC. Many users want appendices to be grouped in an Appendix part, however some problems arise with ToC, hyperref, PDF viewer bookmarks, and so on. There are different solutions which require extra packages, command patching and other extra code, however none of them satisfies me. I almost found an easy way to accomplish a good result, where appendices are added to bookmarks in the right way and hyperref links point to the right page. However, the number of the "Appendix" part page is wrong (it's the number of appendix A). Is there any EASY way to fix that? This is a MWE: documentclass{book} usepackage[nottoc,notlot,notlof]{tocbibind} usepackage{hyperref} begin{document} frontmatter tableofcontents mainmatter part{First} chapter{...
            Read more

            Calculate evaluation metrics using cross_val_predict sklearn

            Image
            1 In the sklearn.model_selection.cross_val_predict page it is stated: Generate cross-validated estimates for each input data point. It is not appropriate to pass these predictions into an evaluation metric. Can someone explain what does it mean? If this gives estimate of Y (y prediction) for every Y (true Y), why can't I calculate metrics such as RMSE or coefficient of determination using these results? python scikit-learn cross-validation share | improve this question edited Nov 28 '18 at 17:52 desertnaut 20.3k 7 43 79 ...
            Read more

            Insert data from modal to MySQL (multiple modal on website)

            Image
            0 I have problem with inserting data to MySQL from modal. My modal: <a href="#" class="badge badge-pill badge-success">6 komentarzy</a> <a href="#" class="badge badge-pill badge-danger">brak komentarzy</a> <a data-toggle="modal" href="#add_desk_comm_{$desk_['desk_id']}" data-target="#add_desk_comm_{$desk_['desk_id']}" class="ediiit">(dodaj)</a> <div class="modal fade" id="add_desk_comm_{$desk_['desk_id']}" tabindex="-1" role="dialog" aria-labelledby="edit_printer" aria-hidden="true"> <div class="modal-dialog" role="document"> ...
            Read more