In order to make sure that I can use parametric test, I need to make sure that my residual distribution is normal.
There is really no way to demonstrate that you have exact normality, but that's okay because approximate normality will generally be sufficient for hypothesis tests in regression to work the way you want.
However, when I refer to the value of skewness and kurtosis of the residual, it is -0.017 and -0.438 respectively, where i think this is considered as normal.
You can obtain values like that with residuals from a simple regression on normal data, but the kurtosis is just significant at the 5% level.
(Technical aside: I used simulation to assess the significance of the kurtosis of residuals here; not knowing the number of predictors, I did it for both independent normals and for one predictor at the given sample size, both showed essentially the same p-value; results should be similar for regression with small numbers of predictors.)
This doesn't actually suggest a problem with the inference when doing a regression or correlation, however. Your data won't be exactly normal; the essential question is 'are the data so badly non-normal that the inference no longer has the properties you wish?'
Unfortunately, when i do kolmogorov-smirnov, the significant value is 0.021, which indicates the residual is not normal.
What were the specified population mean and variance of the residuals for your KS test and how did you get such population values?
Could anybody please explain to me what to do.
I suggest you don't do a hypothesis test to assess the suitability of the assumption of normality, but instead to look at diagnostic displays that show you how badly non-normal the data are.
Some pointers -
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