What are the best possible ways to build a model form skew data which can be further used for estimation purpose?
what about other transformations? if not then may consider skewed model.
In statistics, any model has its assumptions, it seems you use linear regression, and there are 5 assumptions about linear regression,
1. All observation are independent
2. The dependent variable Y has a linear relationship with independent factors x1, x2, …
3. The variance of Y are homogeneous
4. All x1, x2, … are constant without errors
5. All x1, x2, … are independent
In other word, the residuals of the regression Y=a+b1x1+b2x2+… Yi-^YI are independent and identical normal distributed.
Hence you should check if is the distribution of residuals skew or not, rather than Yi
You should not use R^2=0.4 only to say if the model fit well or not. If your data size is large and a few independent variables are used, the R^2 could be low. If transformation is used, you need compare the R^2 between original data and transformed data to see how much are improved.
Low R^2 are due to missing significant factors, or other reasons.
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