Regression for non-parametric data

I have a column of precipitation data.

A normality test indicates the data are non-parametric.

A histogram plot of the data does not resemble a normal curve.

So data violates normality assumption of regression

Question: Is it still possible to do a simple linear regression on the data?

If not, what are my options?

If I log transform the data, the precipitation data takes negative values, which is nonsense in the real world.

Any suggestions?

Thank you.

Answer 1

[the cyclist]

It is a common misconception that the data themselves have to be normally distributed, in order to satisify the assumptions of an ordinary least squares (OLS) regression. They do not.

See, for example, this page or this page for the actual assumptions.

Also, "the data are non-parametric" does not mean "the data are inconsistent with a normal distribution". Usually one would use the non-parametric about a technique that does not assume any underlying distributional properties of the data.

You only mention one variable -- precipitation. I assume you have more than one variable, otherwise a regression isn't even possible.

Please feel free to post your data (ideally in a MAT file, using the paper-clip icon) if you want people to take a look.