In a multiple regression with 16k cases 2 IV (non-normally distributed) and one dependent variable that is also not normally distributed. DV see below:
I've tried three ways of transforming the DV (sqrt/ln/log) to normal but the K-S statistic reports it clearly as non-normal .
The best I could come up with was the ln or log transformation, which clearly shows a trend in the detrended qq plot. Any ideas how to "fine-tune" the ln transformation to remove that trend?
Now I've performed the same ln-transformation which on both the IV which were also not normally distributed. The result of the transformation is shown below:
My approach was instead of trying harder to better transform the IV and DV, to bootstrap the regression in spss and and use the ln_RT_vol_in as the DV and ln_AT_vol_in and ln_FF_vol_in as the predictors. The residuals look fine at least the histogram.
The regression without bootstrapping:
REGRESSION /MISSING LISTWISE /STATISTICS COEFF OUTS CI(95) R ANOVA COLLIN TOL /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT LN_RT_vol_in /METHOD=ENTER LN_AT_vol_in LN_AT_bin_in_deg /PARTIALPLOT ALL /SCATTERPLOT=(*ZRESID ,*ZPRED) /RESIDUALS DURBIN HISTOGRAM(ZRESID) NORMPROB(ZRESID).
And with bootstrapping. I have the same B values but slightly bigger confidence intervals for the bootstrapped version and a slightly less see below.
BOOTSTRAP /SAMPLING METHOD=SIMPLE /VARIABLES TARGET=LN_RT_vol_in INPUT= LN_AT_vol_in LN_AT_bin_in_deg /CRITERIA CILEVEL=95 CITYPE=PERCENTILE NSAMPLES=1000 /MISSING USERMISSING=EXCLUDE.
What do you think of this approach? Do you have any comments on how to improve the transformation in order to maybe get rid of the trend in the de-trended versions of the qqplots?
Another problem is that the bootstrapping doesnt seem to fit the 1000 samples in my memory instead only 49 see below. What can I do about it apart from increasing my mem?