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Math Help - Chi-squared analysis.

  1. #1
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    Chi-squared analysis.

    Ok so I am thoroughly confused.

    I am measuring the sex ratios of Drosophila melanogaster, and I am having issues interpreting my data for my report.
    My report is looking at whether increasing concentrations of estrogen will produce more male flies.
    Can someone please help clarify this for me.

    So I am confused about this :

    If my chi-squared number is larger than 3.481 for df =1. But my p-value>>0.05.

    How do I interpret this?

    Also what if both my chi-squared value are larger and p-value>0.05 also.

    And finally if chi-squared value lower than 3.481 but p-value still higher


    I will be so grateful to anyone who can explain this to me!
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  2. #2
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    When testing at 0.05 you will have a chi-squared 'critical value' that corresponds to this significance.

    If youre chi-squared calculated value is less than this critical value then p will always be > 0.05 and you do not reject the null hypothesis.

    If youre chi-squared calculated value is greater than this critical value then p will always be < 0.05 and you can conclude there is evidence to reject the null hypothesis.
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  3. #3
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    How can p always be something when i get these values from R ( program for stats.)
    The critical value for df=1 @ p = 0.05 = 3.84.

    If these are the values i have gotten , how can my p value be below 0.05? It's clearly not.
    And just to check , if i am checking to see wether the estrogen influences the sex to shift toward more females which would be my null hypothesis?

    null= Estrgoen has no effect on sex determination.
    Alt= Estrogen does have an effect on sex determination.

    Is that right , also below are my values.

    Control Vs 0.1 (0.0075M)
    X-squared = 0.125, df = 1, p-value = 0.7237

    Control vs 1% ( 0.075 M)
    X-squared = 1.2564, df = 1, p-value = 0.2623

    Control Vs 10% (0.75 M)
    X-squared = 1.3243, df = 1, p-value = 0.2498

    For 0.1 Vs 1%
    X-squared = 0.6052, df = 1, p-value = 0.4366
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  4. #4
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    ok so i just spend like an hour trying to get this and figured i'd just read it wrong , I get what you mean. Thank you for your help!!!
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  5. #5
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    Quote Originally Posted by Lucy1 View Post
    The critical value for df=1 @ p = 0.05 = 3.84.
    Don't do this it is confusing what you mean is:

    The critical value with one degree of freedom at p=0.05 is 3.84.

    CB
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  6. #6
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    ok so just to clarify.

    Re: the data i posted above.

    Null : Increasing concentrations of estrogen does not increase female to male ratio.
    Alt : Increasing concentrations of estrogen increases female to male ratio.

    Using data posted in previous post, all result in Chi- squared > 3.841 , and p>0.05 so there is no evidence to reject the null hypothesis.


    Is this correct?
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  7. #7
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    Quote Originally Posted by Lucy1 View Post
    Null : Increasing concentrations of estrogen does not increase female to male ratio.
    Alt : Increasing concentrations of estrogen increases female to male ratio.

    Sounds good.



    Quote Originally Posted by Lucy1 View Post


    Using data posted in previous post, all result in Chi- squared > 3.841 ,


    \displaystyle \chi ^2 = 3.841



    Quote Originally Posted by Lucy1 View Post
    and p>0.05 so there is no evidence to reject the null hypothesis.

    correct
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  8. #8
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    Not wanting to confuse the issue at all, but in my uni course we've always been told that H0 for chi-squared is that there is no association, and that H1 is that there is some association, but you don't know what the association is until you do the goodness of fit testing.
    Are they teaching us the 'easy' way, and there actually are different hypotheses to test for chi-squared?
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  9. #9
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    Not wanting to confuse the issue at all, but in my uni course we've always been told that H0 for chi-squared is that there is no association, and that H1 is that there is some association, but you don't know what the association is until you do the goodness of fit testing.
    Are they teaching us the 'easy' way, and there actually are different hypotheses to test for chi-squared?
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  10. #10
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    In general I would say

    H_0: Observed = Expected.

    H_a: Observed differs from Expected.

    This is not the only \chi^2 test as such. \chi^2 is a distribution that applies to many tests.
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