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Discrete Vs Continous data

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  • #48508

    C Suresh
    Participant

    Is Fibonacci series discrete or continuous?  Why ? Can anyone clarify.

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    #163879

    Dr. Darth aka Darth
    Participant

    For those who don’t know what the Series is here is a quick description of this very simple concept developed around 1200.  The first two numbers in the series are one and one. To obtain each number of the series, you simply add the two numbers that came before it. In other words, each number of the series is the sum of the two numbers preceding it.  Great for mathematicians tired of masturbation who still need something to occupy themselves but not much use otherwise.  If we define continuous data as being infinitely divisable, the Series fails since all numbers in the series are whole and thus discrete.  While the series gets very large, it might be possible to justify a consideration of continuous behaving data although the underlying numbers are clearly discrete.  Hope that explains it.

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    #163883

    Mikel
    Member

    More than I ever wanted to know about some guy adding numbers. They did not teach the anecdote about mathematicans where I went to school.

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    #163886

    Ron
    Member

    Discrete

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    #163896

    Abel
    Participant

    What kind of discrete data is it: nominal or ordinal and why? The series of data appears to be continuous to me?

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    #163901

    Dr. Darth aka Darth
    Participant

    Appears to be is the key.  A pound is a continuous measurement since you can subdivide into half a pound or a quarter of a pound or an oz. and so forth.  Same for an hour, it can be divided into smaller and smaller units.  The Series are all whole numbers and no subdivision takes place, just more and more whole numbers.  Thus it remains discrete.  End of story.  Obviously you are not knowledgeable as to the difference between discrete and continuous nor the different types of discrete.  Otherwise you wouldn’t ask if it were nominal.  Do a little reading first and see if you can’t figure this out.  The answer will come to you quickly.

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    #163924

    Abel
    Participant

    How about you taking your own advice and do some reading.
    Attribute data is the same at Discrete and there are 3 types of Discrete 1. Binomial 2. Ordinal 3. Nominal
    Agreed; weight (such as a pound) is a continuous or variable data type as is time (such as an hour).
    Whole numbers have nothing to do with discrete or continuous. I can measure a series in smaller decimal units. Try supporting your response with mathematical support that series data is Discrete.
    Darth your respnse was very arrogant, try being more respectful when you respond to others. I just asked a very simple question and you did not answer it.
    I do not deserve to be spoken to as if i am some idiot. What comes around goes around

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    #163926

    What language game?
    Member

    Dr. Darth, help me think that through a little more, because the question is nevertheless puzzling. If I understand the series correctly, the series of numbers is non-referring. In other words, the series of numbers is self-referent (you call that masturbation) and not referring to attributes of objects in the empirical world. The assignment of numbers to attributes of objectives is what constitutes measurement. It is in this empirical context of measurement attributes of objects that the numbers are given their meaning as discrete or continuous based on the mathematical operation that are adequate for the type of measurement used to described the object (does exist/does not exist = 0/1, is 10,11, 12 yeards long, etc. When the numbers are not used in the context of  measurement as is the case in the series the terms discrete or continuous in the sense that we use them for measurement and statistical analysis are simply not applicable. It’s a different language game altogether.

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    #163928

    Dr. Scott
    Participant

    C Suresh,
    It is exponentially ordinal follow a taw or PHI distribution. It is ordinal because each succeeding value is larger than the prior. It is exponential because the succeeding values grow more in value than the previous (that is in a non linear fashion) because each succeeding value is dependent upon the value preceding it. The function is that equal (or similar) to a PHI type function.
    Sort of like bees or rats breeding assuming no incest is taking place.
    I would like to know what this data represents and how you plan to use it.
    Thanks,
    Dr. Scott

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    #163929

    Dr. Scott
    Participant

    Just to correct some typos:
    C Suresh,
    It is exponentially ordinal following a tau or PHI distribution. It is ordinal because each succeeding value is larger than the prior. It is exponential because the succeeding values grow more in value than the previous (that is in a non linear fashion) because each succeeding value is dependent upon the value preceding it. The function is that equal (or similar) to a PHI type function.
    Sort of like bees or rats breeding assuming no incest or death is taking place in the animals.
    I would like to know what this data represents and how you plan to use it.
    Thanks,
    Dr. Scott

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    #163949

    C Suresh
    Participant

    Thank you Dr. Scott and others for your valuable responses. It was just a query that came up when understanding Discrete and Continous Data.

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    #163958

    Dr. Darth aka Darth
    Participant

    Sorry Abel, I stopped reading your response after your first statement since it was incorrect.  Back to the drawing board for you.  Please read this definition posted on this site and reflect.
    http://finance.isixsigma.com/dictionary/Attribute_Data-95.htm

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    #163963

    Ordinal scales and transformat
    Participant
    #163967

    Queston for Dr. Scott
    Participant

    A question for Dr. Scott, In your opinion, are we truly dealing with a “measurement scale” here? It seems that the series itself is a series of numbers according to a rule, not the assignment of numbers to attributes of an objecti according to a rule. The series follows an exponential distribution, but they are not anchored in anything observable. Is this series “scalar” in terms of numers theory rather than measurement theory? Which, in my opinion, makes this question so odd and theoretical.

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    #163968

    Taylor
    Participant

    I learned way more than I wanted to on this subject today
    Cheers

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    #163970

    Abel
    Participant

     Dr. Scott, maybe you could take a look at the comments by Darth and arm him with some increased knowledge in this area, he seems to think that Attribute data and Discrete data are not the same and that Attribute is just binomial. Does not believe discrete cann be classified as ordinal.

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    #163971

    Abel
    Participant

    Thank you Ordinal scales and transformat and excellent link, perhaps Darth can read and understand my question was legit.

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    #163972

    Abel
    Participant

    The definitions on this site that you provided are not valid – I suggest you refer to a valid source as that provided by “Ordinal scales and transformat” or an everyday statistical book if you own one.

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    #163978

    Ken Feldman
    Participant

    I will concede that attribute/discrete and variable/continuous have been used interchangeably.  I will continue to argue that a distinction can be made between attribute and discrete although common usage does refer to them as the same.  As to the original question, the Series is not continuous data.

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    #163979

    Ken Feldman
    Participant

    Wow, a link to a book cover, now that’s definitive proof but seemed to satisfy Abel so guess that ends this discussion.

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    #163981

    Abel
    Participant

    Agreed, one one ever said it was continuous, just taht it appears as such. The data is Ordinal.

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    #163983

    Chris Seider
    Participant

    Darth,
    Where were you during the last discussion on discrete and attribute?  There was too much arguing during that session also.  I stated that discrete is typically numerical and attribute is word or letter based but both are categories.
    Of course, I wonder why some worry about these distinctions.  Let’s solve the business or personal problems and not get hung up on semantics.
    Good to see you posting more again.

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    #163985

    Abel
    Participant

    I’ll be honest with you, i skimmed it, but saw enough references to conclude it worthy

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    #163987

    Ordinal scale and transformat
    Participant

    Dr. Darth, this is a somewhat funny site. At times, clicking on the link will allow you to access the relevant pages (2 -7) at other times it will simply lead you to the cover page. The first seven pages of Cliff’s book provide an excellent overview of the issues and controversies surrounding the concept of “scaling” and “scaling levels” (with very well documented further references). The five pages cover both Steven’s theory (1951) as well as the subsequent development of his concepts by Luce and Tukey (1964) via conjoint measurement. It also covers Dr. Scott’s approach of determining the scale by fitting models which goes back to Gulliksen (1946). As always with these very conceptual issues a simple right or wrong answer is difficult to establish.

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    #163996

    Ken Feldman
    Participant

    Great to hear from you again Seider, a blast from the past. That is the point I was trying to make but it is splitting hairs.  I agree that solving personal issues on this site is much more productive than dealing with the technical crap :-).

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    #164001

    Ordinal scale and transformat
    Participant

    You really have to give the Six Sigma guys credit for always winning their game. They want to look academic, they put their doctor hat on and throw out big words. They get entangled in the complexity of their words, they pretend to pull up their sleeves. Suresh gets blasted for being too theoretical, Abel for being too unknowledgeable. In all cases, Darth and Seider win the game for appearing to be scientific pragmatists and pragmatic scientists all in one whithout ever solving the problem. That’s the witchcraft of modern day consultants. And it works and pays the bills!  

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    #164002

    Chris Seider
    Participant

    Darth,
    Wow, you may have just opened up a new string of posts!  Some people seem to use this site as a forum for cleansing….
    It was nice earlier this month to see the kind notes about the individual who passed away….kudos to those who spent the time to let others know the bad news of someone passing away.  I did not the individual, Reigle Stewart.

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    #164003

    Chris Seider
    Participant

    err… “I did not know the individual”.

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    #164005

    Ordinal scales and transformat
    Participant

    not to fear, Darth can handle it.

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    #164010

    Dr. Scott
    Participant

    There is a lot of merit to what you are saying. My only thought might be that as errors occur earlier in the process, then they exponentially lead to more errors. But I agree, it seems to be more a “prediction” of what the end product might be, rather than any specific measure of a quality.
     
    I still am not sure what this measure is being used for, but then I haven’t read the entire thread yet.
     
    Regards,
     
    Dr. Scott

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    #164011

    Dr. Scott
    Participant

    Abel,
    Bottom line is there are two large categories of data: discrete and continuous.
    Both can be ordinal in nature (in fact before data can be considered continuous it must be ordinal). However, discrete data can be ordinal as well, such as counts. 4 is larger than 3 which is larger than two if you are counting defects. Though ordinal, it is still discrete.
    The data that C Suresh is referencing is discrete and ordinal. It is discrete because it is ultimately based on counts. It is ordinal because one number can be said to be greater or lesser than another. Furthermore, it is exponential due to the function behind it (Fibonacci function). But it not continuous cause any value between each resulting number has no real meaning (like 1,000.435 bees). What would the .435th bee look like?
    Another type of discrete data is attribute in nature or binomial; does the “thing” have the attribute or not (e.g., 0 for no or 1 for yes), but it is not ordinal (only descriptive). That is, does the object have the attribute or not? Discrete data can also be categorical, that is have more than two options such as A, B, C, or D. An example of this world be race or hair color. Such a measure is discrete, categorical, but not ordinal.
    And finally, discrete data can be ordinal and in some cases assumed to be interval in nature. An example of this would be a 1-7 satisfaction scale. If the interval assumption can be accepted, then averages and comparisons can have much meaning and use. That is, though technically discrete, in certain cases it can be treated as continuous.
    Hope this helps all,
    Dr. Scott
     
     

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    #164012

    fake accrington alert
    Participant

    Where  is  the  smart “Andy Urguhart”?

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    #164014

    Ordinal scale and transformat
    Participant

    The categorization is an excellent review of the “received view”. The brutal relaity is that underlying the classification schemes of discrete/continuous and nominal/ordinal/interval/ratio are two historical strands of measurement theories: The classical Eucledian and the modern version of Steven. So far, all attempts at reconciling the two views have not resulted in universal consensus. But this is not a matter of Six Sigma, but of measurement theory.

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    #164025

    fake accrington alert
    Participant

    It  is  becoming  part  of  the  comprehensive  SS

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