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Hypothesis test

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  • #48803

    itl190370
    Participant

    Can anyone help. I am currently monitoring 2 machines. We are recording defects per unit produced on both machines (blow holes in a weld). Which hypothesis test would I use to campare. ANOVA?

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    #165529

    Mikel
    Member

    F test and two sample t.
    There are much better ways if you are looking to quantify goodness of welds. In the time it will take to get enough data to make DPU a meaningful stat, you could have both machines working better.

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    #165530

    itl190370
    Participant

    Thanks for the response. What are your recommendations for assessing the differing performance in machines?

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    #165532

    AJ
    Participant

    You have to keep in mind that if you use ANOVA or a 2 sample t-test or any other test requiring assumptions of normality, you have to make sure that your data is independent and normal at the least.Β  If your data is not normal, then you may have to transform the data or use a non parametric test.Β  Sampling the data from your population withΒ n size greater than 25Β will also most likely get you normal data to work with.
    Β 

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    #165535

    Mikel
    Member

    AJ,
    With all due respect, your advice is just plain ignorant.
    This guy is using DPU. If his DPU is 1, your sample size advice will give him an average of 25 defects per sample – no problem. If his DPU is .01, most samples of 25 will have no defects, some will have 1 – big problem. You need to know what defect level you are dealing with to give any advice on sample size. It is clear you are parroting some bad training and not talking from experience.
    The other thing that is wrong with what is being done is who cares if he can show a difference in two welders? Take a big enough sample over time, one will emerge statistically better than the other one. Big deal. If his parts are designed right, welding is a critical operation. Welding is known to be one of those processes that can be taken to six sigma capability. Instead of wasting time showing a difference, the time should be spent improving both welders. We should be talking process maps with inputs and outputs, measurement capability including the ability to do cross sections and pull tests, and DOE. DOE’s for welding come down to how to hold, how clean the surfaces are, time and temperature. Done – go make the welders better.

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    #165552

    Ron
    Member

    You need continuous data to use anova calcualtions. You have attribute data

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    #165561

    Mikel
    Member

    Ron,DPU when given proper sample size can be treated as continuous.

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    #165793

    Badu
    Participant

    Since you have a discrete data For comparison of two machines for defect you use two sample propotion proportion test.

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    #165803

    Jenn
    Participant

    I agree with Amin.Β  The 2P test should give you what you want and is easy to do in Minitab, if that’s what you’re using.Β  Good luck!

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    #165835

    itl190370
    Participant

    I have tried using the 2 proportion test but on occasion the defect rate ie blow holes in the weld can be greater than the number of units welded, ie 1 unit can have more than one defect. In this case the failures are greater than the number of units produced and in this case how can I use a proportion failure as the defects is greater than the number of units produced?

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    #165838

    Krishnam Raju PV
    Participant

    Can you send data to me (Excel file)Β so that I will try to analize and get back to you.
    [email protected]
    Regards,
    Krishnam Raju PV
    Β 

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    #165842

    Anand
    Participant

    As you are testing number of defects, it is a discrete data. Hence, you should be using 2 proportion test or Chi Square test. 2 Sample t test can be used when you have Y as a continuous variable but that is not the case here. Your ‘X’ is a discrete variable and so is your ‘Y’.
    Β 

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    #165843

    Anand
    Participant

    I had posted my earlier message without reading the entire thread, first you need to be sure if you are concerned about defects or defectives. In my opinion you should go by the defectives and your defectives will never be greater than the number of units processed and thereby you can use 2 Proportion test.

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    #165846

    Harish Goyal
    Participant

    Hi
    To make comparison of defects on two machines you can make use of Chi Square Testing.
    Regards
    Harish

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    #165882

    AJ
    Participant

    I am going to attempt to add something here even though Stan thinks I’m ignorant and inexperienced.
    I agree with harish, I think since that this is considered analyzing two sets or proportions (% defectives) p1 vs p2, a chi squared test would be the best test for significance.Β 

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    #165883

    AJ
    Participant

    You can also use a two sample Z test (notΒ t-test)Β using the two proportionsΒ 
    (p1-p2-0)/(SE)
    Where SE is the (pbar*qbar/n1 + pbarqbar/n2)^.5
    Β Β Β Β  where pbar is (x1+x2)/(n1+n2)
    You may need to pool the SE if you have equal variance.Β  To pool the variance you will need to:

    Β 
    Then look this up in the Z-table to see if theΒ P-value is below the desired setting which is normally p<.05 for significance.Β  But you need toΒ make sure if the alpha value of .05 is the right alpha risk for your application.
    Β 

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