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    Scaling variables in regression

    If you need to scale variables in order to get the correct effect size and beta weight for each variable, does this mean you also scale for both categorical and continuous variables? For example: Model1=lm(DV ~ X + Y + Z) In the above equation, is it correct to scale for x/y/z or would...
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    Calculating effect size with interaction term

    If I am running multiple regression in r, what is the next step in order to get the effect size for all variables when there's also an interaction term in the equation. For example: model=lm(DV ~ Var1 + Var2 + Var2:Var3) Also, what's the best way to determine whether or not to do an...
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    Correct methodology when not sure of linearity for continuous response variable

    I already posted on this, but haven't gotten an answer to my core question - which is, what type of statistical analysis should I run. I have many dependent variables I want to measure (change in volume measurements over time) and want to look at how medication effects those changes. I want to...
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    Continuous response variable and categorical predictor - what type of analysis to use

    Hi, I am dealing with data in which the IV's are not easily partitioned - participants can belong to one, two+, or none of the categories for a given IV. Along with this, I am wanting to run the IV's on multiple DV's - differing volume measurements, so I am not quite sure how I would be able to...
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    Question about splitting data

    I am wondering what the most common way to handle data is wherein a participant belongs to multiple groups. For example, if you're wanting to run linear regression and look at how specific diet-type (IV) effects a specific health outcome (DV/response variable) and you have participants that are...