(CRIES)My dependent variable is on a scale from 0 to 100 (percentage), but I took the log (seemed logical at the time)
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
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
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.
Understood that you used BP and then robust SEs. As you can see with the plot, the vertical spread of the residuals is not approximately constant which indicates the errors might not have constant variance (as you already saw using the BP). Are you using and categorical variables? If so, it would probably be helpful for you to plot the residuals using the categorical variable as a grouping variable (should give you the same plots, but it will use a different symbol or color on the plot to represent each group). This way you can get an idea of why the heteroscedasticity is occurring (i.e. is it that each group has the same pattern or that the groups just have different variances from one another).
I don't think your output is right. What ondansetron was looking for was just a scatterplot like you were doing before, but to have signs for each of your categorical variables. So if you had sex in the model, all of the females would be one color or shape and the males would be a different color or shape. It seems like you got close with your code, though it appears you have two values for each observation in the figure.