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    hey guys,

    Ive got a data set with 3 seemingly unusual observations (given their standard residuals are greater than 2) and i decided to remove these 3 points to see if significance and goodness of fit would increase. However another unusual observation appeared out of nowhere when a regression analysis was performed.
    What does this mean and what should i do about it?

    Thanks in advance
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    If I'm not mistaken, it's the opposite of progression :holmes:.
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    Wait. What? haha

    Statistical regression!
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    Can you tell us what you are actucally analysing?
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    hi moesy,
    I'm trying to find the best predictor for Bodyfat% out of, forearm, abdomen, height, weight and hip. Ive deduced that abdomen is the best predictor and im trying to transform the data to increase goodness of fit and significance etc. does this help?

    thanks for the reply
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    (Original post by tsmith16)
    hey guys,

    Ive got a data set with 3 seemingly unusual observations (given their standard residuals are greater than 2) and i decided to remove these 3 points to see if significance and goodness of fit would increase. However another unusual observation appeared out of nowhere when a regression analysis was performed.
    What does this mean and what should i do about it?

    Thanks in advance
    By removing those points, you make your r^2 value closer to 0, which is what you want and you can see greater correlation between the points.

    The second bit I don't understand what you mean....
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    Surely r^2 moves closer to 1? and i removed my 3 points to increase significance and increase the r^2 value etc but i got another unusual observation after removing the points and performing another analysis
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    The reason you are getting another unusual point is because as you keep removing points, and since these may be outliers if I'm not wrong they have a large impact on the mean and hence once removed, there is a shift in the mean and you'll keep getting outliers.

    R^2 only tells you how much of the correlation is accounted for by your statistical model, hence you are right and it should move nearer to 1.

    Remember that R^2 is coefficient of determination and NOT your correlation coefficient.

    Now with regards to your research, a good predictor would be a combination of body fat.

    I'm not sure how statistical you want to be but, to break this down, you have
    body fat % = forearm+abdomen+height+weight+hi p. Thats a linear equation.
    I would suggest you do a step wise formulation by adding and removing variables to see when you get a significant effect. Remember when it comes to weight and body fat, there is usually a combination of predictors.

    Transforming data does help depending on how much variation you have between the measurements.
    Does this help or have I deviated from the main topic?!
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    (Original post by moesy)
    The reason you are getting another unusual point is because as you keep removing points, and since these may be outliers if I'm not wrong they have a large impact on the mean and hence once removed, there is a shift in the mean and you'll keep getting outliers.

    R^2 only tells you how much of the correlation is accounted for by your statistical model, hence you are right and it should move nearer to 1.

    Remember that R^2 is coefficient of determination and NOT your correlation coefficient.

    Now with regards to your research, a good predictor would be a combination of body fat.

    I'm not sure how statistical you want to be but, to break this down, you have
    body fat % = forearm+abdomen+height+weight+hi p. Thats a linear equation.
    I would suggest you do a step wise formulation by adding and removing variables to see when you get a significant effect. Remember when it comes to weight and body fat, there is usually a combination of predictors.

    Transforming data does help depending on how much variation you have between the measurements.
    Does this help or have I deviated from the main topic?!
    Hey, this has helped a lot thanks, however i'm only looking for the single best predictor, which i think ive found given all the results from a regression analysis, the first paragraph was what i was looking for, but the rest is also very helpful, thanks a bunch
 
 
 
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