Residuals to check for unbiased estimates homoscedasticity and normality in regression spss
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one of the big problems with non-normality in the residuals and heteroscedasticity is that the amount of error in your model is not consistent across the full range of your observed data. When you think about your predictor variables, this means that the amount of predictive ability they have (i.e., as calculated in their beta weights) is not the same across the full range of the dependent variable. Thus, your predictors technically mean different things at different levels of the dependent variable. Not so good for interpretation.
Transforming the dependent variable can help to correct for this - but at the same time makes the interpretation of the overall model a little bit more opaque. You have to make the trade-off on what you are comfortable with here.
If the square-root transformation did not fully normalize your data you can also try an inverse transformation. The strength of transformations tends to go from 1. Logarithmic, 2. Square Root, 3. Inverse (1/x). See if that helps.
I hope this will help you
if not then comment me
Transforming the dependent variable can help to correct for this - but at the same time makes the interpretation of the overall model a little bit more opaque. You have to make the trade-off on what you are comfortable with here.
If the square-root transformation did not fully normalize your data you can also try an inverse transformation. The strength of transformations tends to go from 1. Logarithmic, 2. Square Root, 3. Inverse (1/x). See if that helps.
I hope this will help you
if not then comment me
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