- Thread starter tfm09
- Start date
- Tags correlation non-parameteric parametric statistics help

As Ellis Ott always said: "Plot the Data!"

I'm going to model the non-parametric data sets to try and model the relationship between them: I was going to use a generalised mixed linear model, but thats for normal data isnt it- what would be the best model to use for non-normal data?

Regression analysis does not require normality in the variables themselves, but normality of the residuals. Run your analysis then test the normality of the residuals (as well as the remaining residuals diagnostics such as no unusual patterns vs. fitted values or time).

...the non-parametric data...

There are many parametric methods for skewed non-normal data. Examples are the the binomial distribution, the Poisson distribution and the exponential distribution.

The above mentioned distributions can be estimated in models like "generalized linear models" (that will also include the normal distribution as a special case). It does not seem clear at the moment if you need the "mixed" part in the model.

Normality of the residuals is actually not required for point estimates of regression. But it is neccessary for the CI and assessment of the model (the p values) and most won't be interested in running regression if they can not test the null hypothesis.

While Pearson might work with some non-normal data that is questionable if you have binary data (data with two levels). Polychoric correlations are commonly recommended for that.