Why are you trying to normalize the variable?

Can you post a histogram and qq-plot of your data?

Large dataset will typically fail normality test when there is very small departures. What is your sample size. Also, feel free to upload the normality test results.

Can you post a histogram and qq-plot of your data?

Large dataset will typically fail normality test when there is very small departures. What is your sample size. Also, feel free to upload the normality test results.

I did a test of normality in STATA on my dependent variable. The rest was significant thus violations of normality. After taking the log, there is still violation of normality. What is the next step to take?

My main drivers are also on a scale from 0 to 100, but I had to transform them so I only (keep) have values of fifty and higher

Then I have some economic control variables (GDP) and nominal dummy control variable 1/0

I also checked for outliers. Ive attached the kdensity(histogram?)/qqplot and the other tests I used for normality

That seems to be OK. And with the large sample size the parameter estimates will be approx normal by the central limit anyway, so it is OK to go ahead with the inference based on normal theory. (In my view - but we can all be wrong.)

Would you be able to show us a histogram and normal probability plot of your untransformed residuals?

Doing the test for the untransformed and transformed variable both were significant

The difference between your transformed and untransformed plots doesn't look to be too big. I would see how much the conclusions vary between the two models. Did you happen to look into the constant variance assumption using a plot of residuals vs predicted y values in the transformed and untransformed models? I think the homoscedasticity assumption is more important than the normality issue.

Formal tests for normality aren't really a great idea for several reasons. In particular, they're often very sensitive to slight departures from normality, with this problem magnifying as you increase the sample size. I would personally not even run a formal test of normality and just rely on the histogram/stem-leaf/normal probability plot approach. If you check your standardized residuals and investigate the suspect outliers (absolute standardized value between 2 and 3) and the outliers (absolute standardized value great than 3) as well as other regression diagnostics (influential observations) I think you'll be better off than using anything than the formal normality tests. Especially since regression methods are able to perform pretty well in the presence of outliers and moderate non-normality.

The difference between your transformed and untransformed plots doesn't look to be too big. I would see how much the conclusions vary between the two models. Did you happen to look into the constant variance assumption using a plot of residuals vs predicted y values in the transformed and untransformed models? I think the homoscedasticity assumption is more important than the normality issue.

The difference between your transformed and untransformed plots doesn't look to be too big. I would see how much the conclusions vary between the two models. Did you happen to look into the constant variance assumption using a plot of residuals vs predicted y values in the transformed and untransformed models? I think the homoscedasticity assumption is more important than the normality issue.

I didnt check for homoscedasticity using plots, I used the BrueschPagan test in both cases there was heteroscedasticity, so I run the regression on robust error terms

But I've attached the plots

Ohh I see. Good to know!

I didnt check for homoscedasticity using plots, I used the BrueschPagan test in both cases there was heteroscedasticity, so I run the regression on robust error terms

But I've attached the plots

I didnt check for homoscedasticity using plots, I used the BrueschPagan test in both cases there was heteroscedasticity, so I run the regression on robust error terms

But I've attached the plots

I would search the documentation on your software to see how to apply a plotting or grouping variable to a scatter plot. It really depends on the program you're using.

I managed to change the color of only one of my categorical variable, cant seem to select more (if that makes sense?), but then I tried with another categorical variable and the colored dots were exactly the same